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The Future Of Healthcare: 12 Facts And Visions Of Change

Updated: June 19, 2024

Published: May 3, 2020

It’s a fact that everything changes. When it comes to the future of healthcare, experts believe a lot will be changing over the next few years. The industry of healthcare is impacted by so much, including politics, technology, people, and more.

With the rise of technology and big data, as well as a focus on health and wellness, the industry is shifting. To create a better system of healthcare, it’s expected that care will become more human-centric and personalized.

Here’s how healthcare is changing.

The Major Aspects Of Change

Healthcare is going to massively shift at the hands of technology. The cycle of innovation for technology is exponential. This means that healthcare will benefit from dramatic changes by 2040.

Right now, healthcare is institutionally-focused. With artificial intelligence, data, interoperable devices, wearable technology, and the like, healthcare will be easily personalized. Consumers are going to take healthcare into their own hands, quite literally, and provide data and information to reap the benefits of customizable wellness plans.

The Delivery of Healthcare

When you think of doctors and healthcare, the first places that come to mind are likely overcrowded doctor’s offices and hospitals. However, the delivery of healthcare is also changing because of technology.

From virtual communities to health hubs and special care operators, there will be innovative ways to connect healthcare experts with those who are in need of care.

Photo by ZMorph

The case for technology.

While technology is reshaping how the healthcare industry will work, the face of healthcare providers is also changing. There will be more people entering the field with business backgrounds, like those who have earned their MBA , for example.

Additionally, the majority of healthcare providers in specialities like pediatrics and gynecology are women. Women now make up one-third of practicing physicians . While the face of providers is changing, the population is also living longer.

This means that the patients whom healthcare professionals serve have different needs than before. Along with government policies that will need to provide aid for an aging population, disease trends will also propose new challenges.

All of the natural shifts like population changes will be better served with innovative solutions.

12 Visions of Change & What We Can Expect from the Healthcare Industry

On the forefront of technology, here are some major visions of change and expectations around the healthcare trends of the future.

1. Online Visits

Thanks to video calling, there has been an influx of online doctor’s visits. In crises where people cannot make it to doctor’s, online visits have become the new house call. The convenience of online visits also extends accessibility to doctors in rural areas that are lacking medical care.

2. Group Visits

As the population expands, there’s an increased demand for doctors. If the supply can’t keep up, what’s the next best thing? Group visits are becoming more popular. Group visits are when doctors see multiple patients with similar symptoms at the same time. Group visits are also likely to occur with patients who share chronic conditions like diabetes. On the upside, this could bring together people in similar situations who can meet and try to manage their conditions together.

3. Team Approach

To optimize healthcare, providers may leverage a team approach to treatment. This means that doctors in varying levels and specialties come together to help manage a patient and address all their needs from different perspectives. The teamwork approach places the patient in the middle and relies on each professional to contribute knowledge to optimize care.

4. Artificial Intelligence

Artificial intelligence will continue and expand its presence in healthcare. Medical uses of AI can be witnessed in the case of deep learning. This is when robots can use data to learn and respond to different situations without human interaction. In the medical field, real-time diagnosis and even prescriptions have been handled by AI. From a tangible sense, robots will also be used to aid healthcare professionals with physical tasks, like pulling supplies from stock rooms or the kitchen in a hospital, for example.

5. Virtual Reality

There are many uses for virtual reality in the healthcare field. Consider this, a child is in pain in a pediatric unit. They can put on a virtual reality headset and escape into a virtual oasis that can help to alleviate the pain. For medical school students, virtual reality is a way to simulate a surgical environment and practice before a student becomes a doctor and enters the OR.

6. Healthcare Trackers, Wearables, And Sensors

Data and technology are playing a massive role in personalizing healthcare. New healthcare trackers from the Apple Watch to diabetic sensors like Dexcom are helping people take control of their own health and wellness. From managing weight to stress levels to blood sugar, patients can also easily share health data with their doctors to help diagnose and prevent problems.

7. Personalized Healthcare

One of the biggest changes in healthcare is predictive medicine. Smart machines and devices can share healthcare data with professionals to predict problems before they even arise or become life threatening. This proactive model of healthcare is inherently personalized because patients’ data is being sent from their bodies, in essence, to their healthcare teams for plans and treatment.

8. Genome Sequencing

The ability for researchers and medical scientists to conduct whole genome sequencing is opening the door to understanding major diseases. Genome sequencing helps to discover how DNA and genes can cause diseases. For example, whole genome sequencing has given scientists new insight into the heritability of schizophrenia .

9. Drug Development

Medical professionals are finding new and faster ways to develop drugs since the current process is lengthy and costly. They are using the power of artificial intelligence and in silico trials to do so.

10. Nanotechnology

Nanotechnology can help with wound treatment and healing. Companies are creating nanotechnology in the form of patches, for example, to monitor wounds and even stimulate healing.

11. 3D Printing

3D printing allows the creation of something from nothing. The medical world can use 3D printers to make artificial limbs, blood vessels and even bio tissues. There have even been cases where pharmaceutical companies have 3D printed medicine. As time progresses, experts believe that the uses for 3D printing in healthcare will continue to expand.

12. Robotics

Robots have been used to help patients heal from surgery and can also make for great companions to those who are suffering from an illness. Startups are even creating ways in which robots can help kids with illnesses to monitor medication .

Photo by  Artur Łuczka  on  Unsplash

Technology will improve well-being.

It’s clear to see how technology is playing a major role in reshaping healthcare. But beyond sickness and surgery, wearable technology and smart devices bring a whole new focus to the patient. People can use wearables to monitor their own well-being and pay attention to data points they would have otherwise ignored, such as how many steps per day they take, their heart rate, and their stress levels.

More than just tackling diseases as they arise or treating illnesses, technology is putting a new focus on overall health and well-being, including mental health.

The Future of Healthcare and Those Impacted

The changes that will happen in the future of healthcare will affect everyone from insurers, healthcare professionals, new entrants in the marketplace, patients, consumers, and employers. Big businesses, like Apple and Google, are even becoming more involved in healthcare.

Here’s an overview of those who will be affected:

• Data Securers and Data Collectors
• Developers and Data Scientists
• Data Operators and Platform Builders
• Inventors and Manufacturers
• Virtual Healthcare Providers
• Wellness Coaches
• Specialty Care Operators
• Insurance Providers
• Healthcare Professionals

Studying Health Science

The importance of healthcare providers in the future of healthcare cannot be understated. From nurses to physical therapists and doctors to surgeons, those who provide healthcare solutions on a daily basis will continue to be needed.

For those interested in studying Health Science and entering the field of healthcare, consider the University of the People’s online tuition-free program . Students can choose between earning their Associate’s or Bachelor’s degree to enter the field in various professions .

The Future of Healthcare Starts Now

Technology, the population, patient needs, and more will continue to play a role in the future of healthcare. This will lead to more personalized care and predictive solutions when it comes to patient health. Both people, business, and science will help to create change within the industry to optimize care.

In this article

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124 Healthcare Essay Topic Ideas & Examples

Inside This Article

Healthcare is a diverse and complex field that encompasses a wide range of topics, issues, and challenges. Whether you are studying healthcare as a student, working in the healthcare industry, or simply interested in learning more about this important area, there are countless essay topics that you can explore. To help you get started, here are 124 healthcare essay topic ideas and examples that you can use for inspiration:

• The impact of healthcare disparities on patient outcomes
• Strategies for improving access to healthcare in underserved communities
• The role of technology in transforming healthcare delivery
• The ethics of healthcare rationing
• The importance of diversity and inclusion in healthcare organizations
• The rise of telemedicine and its implications for patient care
• The impact of the opioid epidemic on healthcare systems
• The role of nurses in promoting patient safety
• The challenges of providing mental health care in a primary care setting
• The future of healthcare: personalized medicine and precision healthcare
• The role of healthcare providers in addressing social determinants of health
• The impact of climate change on public health
• The role of public health campaigns in promoting healthy behaviors
• The challenges of healthcare delivery in rural areas
• The impact of healthcare reform on the uninsured population
• The role of healthcare informatics in improving patient outcomes
• The importance of cultural competency in healthcare delivery
• The ethical implications of genetic testing and personalized medicine
• The impact of healthcare costs on patient access to care
• The role of healthcare administrators in shaping the future of healthcare delivery
• The challenges of implementing electronic health records in healthcare settings
• The impact of healthcare privatization on patient care
• The role of healthcare providers in promoting patient autonomy
• The challenges of providing end-of-life care in a healthcare setting
• The impact of healthcare disparities on maternal and child health outcomes
• The role of healthcare providers in addressing the opioid crisis
• The challenges of providing healthcare to undocumented immigrants
• The impact of the COVID-19 pandemic on healthcare systems
• The role of healthcare providers in promoting vaccination uptake
• The challenges of healthcare delivery in conflict zones
• The impact of healthcare disparities on LGBTQ+ populations
• The role of healthcare providers in promoting healthy aging
• The challenges of providing healthcare to homeless populations
• The impact of healthcare disparities on rural communities
• The role of healthcare providers in addressing food insecurity
• The challenges of providing healthcare to refugees and asylum seekers
• The impact of healthcare disparities on people with disabilities
• The role of healthcare providers in promoting mental health awareness
• The challenges of providing healthcare to incarcerated populations
• The impact of healthcare disparities on immigrant populations
• The role of healthcare providers in promoting sexual health education
• The challenges of providing healthcare to indigenous populations
• The impact of healthcare disparities on veterans' health outcomes
• The role of healthcare providers in promoting healthy lifestyles
• The challenges of providing healthcare to low-income populations
• The impact of healthcare disparities on minority populations
• The role of healthcare providers in promoting preventive care
• The challenges of providing healthcare to elderly populations
• The impact of healthcare disparities on women's health outcomes
• The role of healthcare providers in promoting maternal health
• The challenges of providing healthcare to children and adolescents
• The impact of healthcare disparities on mental health outcomes
• The role of healthcare providers in promoting substance abuse treatment
• The challenges of providing healthcare to homeless youth
• The impact of healthcare disparities on LGBTQ+ youth
• The role of healthcare providers in promoting healthy relationships
• The challenges of providing healthcare to LGBTQ+ youth
• The impact of healthcare disparities on transgender populations
• The role of healthcare providers in promoting gender-affirming care
• The challenges of providing healthcare to LGBTQ+ elders
• The impact of healthcare disparities on people of color
• The role of healthcare providers in promoting racial equity
• The challenges of providing healthcare to immigrant populations
• The impact of healthcare disparities on refugee populations
• The role of healthcare providers in promoting cultural competency
• The challenges of providing healthcare to non-English speaking populations
• The role of healthcare providers in promoting disability rights
• The challenges of providing healthcare to people with mental illnesses
• The impact of healthcare disparities on people experiencing homelessness
• The role of healthcare providers in promoting housing stability
• The challenges of providing healthcare to people living in poverty
• The impact of healthcare disparities on incarcerated populations
• The role of healthcare providers in promoting criminal justice reform
• The challenges of providing healthcare to veterans
• The impact of healthcare

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How Supply Chains Function in Healthcare

June 18, 2019

View all blog posts under Articles | View all blog posts under Online Healthcare MBA

Collectively, these changes can dramatically shape the future of healthcare delivery. If facilities aren’t prepared to adapt, their ability to deliver high-quality care will be hindered. As healthcare professionals progress through their careers, pursuing advanced education, such as a Healthcare Master of Business Administration degree , can help them understand the impact these changes can have and prepare their facilities to integrate them into care strategies before industry innovations become industry standards.

Healthcare Business

Some of the most pronounced shifts in healthcare delivery are happening at the business level. One key change is the ongoing consolidation of healthcare organizations. Increased partnerships between organizations may create a narrower market that could drive up costs for providers and patients alike.

The consolidation of organizations dovetails with the trend of growing healthcare consumerization. As costs continue to increase through elements like co-pays and deductibles, patients are becoming more conscious of the quality of care they’re receiving. Additionally, there is a shift in care delivery environments, as technological innovations have turned many inpatient procedures into outpatient procedures.

It’s important for healthcare administrators to fully understand how these changes can impact their operations from a short- and long-term perspective and build strategies to respond.

Healthcare Technology

Technological innovations in healthcare are likely to have an enormous impact on the future of healthcare and healthcare delivery strategies.

Recent developments such as telehealth and EHRs allow patients to have easier access to care and increased information regarding the state of their health. Innovations such as wearable technology help patients to boost their health and well-being — and, ultimately, reduce the number of doctor visits they need and their costs. Advanced healthcare tech applications such as robotics and artificial intelligence (AI) have also played a part in developing more efficient and accurate care delivery solutions.

Health administrators who embrace innovative tech can transform their facilities into leaders in care delivery.

Healthcare Supply Chain Management

Healthcare supply chains are unique. Whereas a retail supply chain revolves around principles of supply and demand (e.g., more stock of a toy is needed to keep up with purchases), these concerns are less central to healthcare.

Certain essential supplies, like prescription drugs and syringes, will always need to be on hand. Plus, patients are not really consumers in the conventional sense, as they frequently require particular services and treatments, such as emergency room care, that are selected for them by healthcare professionals.

For many providers, supply chains are major cost centers, with significant overhead stemming from unoptimized processes. A 2019 report by Health Catalyst stated that roughly 30% of all American hospital spending is allocated to overspending on supply chains — around $25.4 billion per year.

Healthcare supply chain management (SCM) is an ongoing challenge, especially as providers look to align costs with outcomes in the context of the industrywide move toward value-based care. At the same time, efforts to reduce supply chain spending must keep in mind the importance of consistently maintaining sufficient supplies. The COVID-19 crisis drove this point home, as supply chain deficiencies made it difficult for some providers to deliver the best care possible.

To address a wide variety of issues with SCM, providers have resorted to a mix of technological, operational and patient-centric solutions. Here’s a quick look at four approaches that have found some success in transforming healthcare supply chains into more reliable and economical infrastructures.

1. Taking the Warehouse In-House

Warehouses are supply chain fixtures, so it’s no surprise that some providers have looked to assume greater control over these key facilities. Vanderbilt University Medical Center took this route in the early stages of the pandemic. It set up its own space and devised a self-distribution program for supplies, specifically targeting supplies that may be stretched thin due to COVID-19.

2. Betting on Blockchain Technology

Blockchain is the simple database tech underlying cryptocurrencies such as Bitcoin. It also has potential uses in SCM, due to the fact that it’s difficult for anyone to tamper with or corrupt its entries. In healthcare, this could help ensure that the right medicines are being transported and that everyone in the supply chain can trust one another. Organizations like the Food and Drug Administration (FDA) and IBM joined forces to develop tech-driven pilot programs that use blockchain technology to track and trace prescriptions and vaccine distribution.

3. Analyzing Real-Time Analytics

How are SCM costs tracking over a three-month period? Or over the course of a year? Real-time analytics is usually the quickest, most effective way to answer these types of questions. Healthcare providers can implement business intelligence software that fuels analytics dashboards, allowing them to get clear, actionable insights into trends across their operations. This helps pull information from multiple systems and track issues such as short- and long-term cost increases.

4. Customizing Customer Service for Each Patient

Telehealth initiatives offer a creative solution to some SCM issues. Instead of requiring a patient to visit an office and potentially undergo costly diagnostic procedures using particular supplies, providers may be able to conduct an accurate diagnostic via technology such as videoconferencing. A 2019 study found that telemedicine could save between $19 and $120 per visit for many patients. In addition to these savings, providers can better conserve some of their supplies and reroute resources.

Be Prepared to Make an Impact

Healthcare will continue to evolve in the future. One element that will remain constant is the need for leaders to successfully integrate advances into healthcare strategies.

George Washington University’s online Healthcare Master of Business Administration (HCMBA) program can prepare you to provide this important leadership. By enrolling in this fully online program, you will explore advanced topics in both the managerial and health-related aspects of the industry. Courses in regulatory affairs, accounting, and healthcare quality, among others, will prepare you to navigate common administrative issues in a senior role.

Learn more about how George Washington University can help you be a leader in the dynamic healthcare industry.

Recommended Readings

How Data and Decisions Work Together

How Hospital Management Affects Patient Care

The Top Costs Associated With Running a Hospital

BBC, “Pharmacists Warn of a ‘Surge’ in Shortage of Common Medicines”

Food and Drug Administration, Drug Shortages: Additional News and Information

Health Catalyst, “Improving Data Integrity Leads to Lower Supplies Costs”

Healthcare Financial Management Association, “Reimagining the Healthcare Supply Chain to Bolster Resilience and Efficiency”

HealthLeaders, “Cost Savings for Telemedicine Estimated at $19 to $120 per Patient Visit”

IBM, “How the FDA Is Piloting Blockchain for the Pharmaceutical Supply Chain”

IndustryWeek, “How COVID-19 Has Changed the Healthcare Supply Chain”

Managed Healthcare Executive , “Five Healthcare Industry Changes to Watch in 2020”

The Medical Futurist, 10 Ways Technology Is Changing Healthcare

Learn More About the Healthcare MBA Program at GW

Health Care Comes Home: The Human Factors (2011)

Chapter: 7 conclusions and recommendations.

7 Conclusions and Recommendations

Health care is moving into the home increasingly often and involving a mixture of people, a variety of tasks, and a broad diversity of devices and technologies; it is also occurring in a range of residential environments. The factors driving this migration include the rising costs of providing health care; the growing numbers of older adults; the increasing prevalence of chronic disease; improved survival rates of various diseases, injuries, and other conditions (including those of fragile newborns); large numbers of veterans returning from war with serious injuries; and a wide range of technological innovations. The health care that results varies considerably in its safety, effectiveness, and efficiency, as well as its quality and cost.

The committee was charged with examining this major trend in health care delivery and resulting challenges from only one of many perspectives: the study of human factors. From the outset it was clear that the dramatic and evolving change in health care practice and policies presents a broad array of opportunities and problems. Consequently the committee endeavored to maintain focus specifically on how using the human factors approach can provide solutions that support maximizing the safety and quality of health care delivered in the home while empowering both care recipients and caregivers in the effort.

The conclusions and recommendations presented below reflect the most critical steps that the committee thinks should be taken to improve the state of health care in the home, based on the literature reviewed in this report examined through a human factors lens. They are organized into four areas: (1) health care technologies, including medical devices and health information technologies involved in health care in the home; (2)

caregivers and care recipients; (3) residential environments for health care; and (4) knowledge gaps that require additional research and development. Although many issues related to home health care could not be addressed, applications of human factors principles, knowledge, and research methods in these areas could make home health care safer and more effective and also contribute to reducing costs. The committee chose not to prioritize the recommendations, as they focus on various aspects of health care in the home and are of comparable importance to the different constituencies affected.

HEALTH CARE TECHNOLOGIES

Health care technologies include medical devices that are used in the home as well as information technologies related to home-based health care. The four recommendations in this area concern (1) regulating technologies for health care consumers, (2) developing guidance on the structure and usability of health information technologies, (3) developing guidance and standards for medical device labeling, and (4) improving adverse event reporting systems for medical devices. The adoption of these recommendations would improve the usability and effectiveness of technology systems and devices, support users in understanding and learning to use them, and improve feedback to government and industry that could be used to further improve technology for home care.

Ensuring the safety of emerging technologies is a challenge, in part because it is not always clear which federal agency has regulatory authority and what regulations must be met. Currently, the U.S. Food and Drug Administration (FDA) has responsibility for devices, and the Office of the National Coordinator for Health Information Technology (ONC) has similar authority with respect to health information technology. However, the dividing line between medical devices and health information technology is blurring, and many new systems and applications are being developed that are a combination of the two, although regulatory oversight has remained divided. Because regulatory responsibility for them is unclear, these products may fall into the gap.

The committee did not find a preponderance of evidence that knowledge is lacking for the design of safe and effective devices and technologies for use in the home. Rather than discovering an inadequate evidence base, we were troubled by the insufficient attention directed at the development of devices that account, necessarily and properly, for users who are inadequately trained or not trained at all. Yet these new users often must

rely on equipment without ready knowledge about limitations, maintenance requirements, and problems with adaptation to their particular home settings.

The increased prominence of the use of technology in the health care arena poses predictable challenges for many lay users, especially people with low health literacy, cognitive impairment, or limited technology experience. For example, remote health care management may be more effective when it is supported by technology, and various electronic health care (“e-health”) applications have been developed for this purpose. With the spectrum of caregivers ranging from individuals caring for themselves or other family members to highly experienced professional caregivers, computer-based care management systems could offer varying levels of guidance, reminding, and alerting, depending on the sophistication of the operator and the criticality of the message. However, if these technologies or applications are difficult to understand or use, they may be ignored or misused, with potentially deleterious effects on care recipient health and safety. Applying existing accessibility and usability guidelines and employing user-centered design and validation methods in the development of health technology products designed for use in the home would help ensure that they are safe and effective for their targeted user populations. In this effort, it is important to recognize how the line between medical devices and health information technologies has become blurred while regulatory oversight has remained distinct, and it is not always clear into which domain a product falls.

Recommendation 1. The U.S. Food and Drug Administration and the Office of the National Coordinator for Health Information Technology should collaborate to regulate, certify, and monitor health care applications and systems that integrate medical devices and health information technologies. As part of the certification process, the agencies should require evidence that manufacturers have followed existing accessibility and usability guidelines and have applied user-centered design and validation methods during development of the product.

Guidance and Standards

Developers of information technologies related to home-based health care, as yet, have inadequate or incomplete guidance regarding product content, structure, accessibility, and usability to inform innovation or evolution of personal health records or of care recipient access to information in electronic health records.

The ONC, in the initial announcement of its health information technology certification program, stated that requirements would be forthcom-

ing with respect both to personal health records and to care recipient access to information in electronic health records (e.g., patient portals). Despite the importance of these requirements, there is still no guidance on the content of information that should be provided to patients or minimum standards for accessibility, functionality, and usability of that information in electronic or nonelectronic formats.

Consequently, some portals have been constructed based on the continuity of care record. However, recent research has shown that records and portals based on this model are neither understandable nor interpretable by laypersons, even by those with a college education. The lack of guidance in this area makes it difficult for developers of personal health records and patient portals to design systems that fully address the needs of consumers.

Recommendation 2. The Office of the National Coordinator for Health Information Technology, in collaboration with the National Institute of Standards and Technology and the Agency for Healthcare Research and Quality, should establish design guidelines and standards, based on existing accessibility and usability guidelines, for content, accessibility, functionality, and usability of consumer health information technologies related to home-based health care.

The committee found a serious lack of adequate standards and guidance for the labeling of medical devices. Furthermore, we found that the approval processes of the FDA for changing these materials are burdensome and inflexible.

Just as many medical devices currently in use by laypersons in the home were originally designed and approved for use only by professionals in formal health care facilities, the instructions for use and training materials were not designed for lay users, either. The committee recognizes that lack of instructional materials for lay users adds to the level of risk involved when devices are used by populations for whom they were not intended.

Ironically, the FDA’s current premarket review and approval processes inadvertently discourage manufacturers from selectively revising or developing supplemental instructional and training materials, when they become aware that instructional and training materials need to be developed or revised for lay users of devices already approved and marketed. Changing the instructions for use (which were approved with the device) requires manufacturers to submit the device along with revised instructions to the FDA for another 510(k) premarket notification review. Since manufacturers can find these reviews complicated, time-consuming, and expensive, this requirement serves as a disincentive to appropriate revisions of instructional or training materials.

Furthermore, little guidance is currently available on design of user

training methods and materials for medical devices. Even the recently released human factors standard on medical device design (Association for the Advancement of Medical Instrumentation, 2009), while reasonably comprehensive, does not cover the topic of training or training materials. Both FDA guidance and existing standards that do specifically address the design of labeling and ensuing instructions for use fail to account for up-to-date findings from research on instructional systems design. In addition, despite recognition that requirements for user training, training materials, and instructions for use are different for lay and professional users of medical equipment, these differences are not reflected in current standards.

Recommendation 3. The U.S. Food and Drug Administration (FDA) should promote development (by standards development organizations, such as the International Electrotechnical Commission, the International Organization for Standardization, the American National Standards Institute, and the Association for the Advancement of Medical Instrumentation) of new standards based on the most recent human factors research for the labeling of and ensuing instructional materials for medical devices designed for home use by lay users. The FDA should also tailor and streamline its approval processes to facilitate and encourage regular improvements of these materials by manufacturers.

Adverse Event Reporting Systems

The committee notes that the FDA’s adverse event reporting systems, used to report problems with medical devices, are not user-friendly, especially for lay users, who generally are not aware of the systems, unaware that they can use them to report problems, and uneducated about how to do so. In order to promote safe use of medical devices in the home and rectify design problems that put care recipients at risk, it is necessary that the FDA conduct more effective postmarket surveillance of medical devices to complement its premarket approval process. The most important elements of their primarily passive surveillance system are the current adverse event reporting mechanisms, including Maude and MedSun. Entry of incident data by health care providers and consumers is not straightforward, and the system does not elicit data that could be useful to designers as they develop updated versions of products or new ones that are similar to existing devices. The reporting systems and their importance need to be widely promoted to a broad range of users, especially lay users.

Recommendation 4. The U.S. Food and Drug Administration should improve its adverse event reporting systems to be easier to use, to collect data that are more useful for identifying the root causes of events

related to interactions with the device operator, and to develop and promote a more convenient way for lay users as well as professionals to report problems with medical devices.

CAREGIVERS IN THE HOME

Health care is provided in the home by formal caregivers (health care professionals), informal caregivers (family and friends), and individuals who self-administer care; each type of caregiver faces unique issues. Properly preparing individuals to provide care at home depends on targeting efforts appropriately to the background, experience, and knowledge of the caregivers. To date, however, home health care services suffer from being organized primarily around regulations and payments designed for inpatient or outpatient acute care settings. Little attention has been given to how different the roles are for formal caregivers when delivering services in the home or to the specific types of training necessary for appropriate, high-quality practice in this environment.

Health care administration in the home commonly involves interaction among formal caregivers and informal caregivers who share daily responsibility for a person receiving care. But few formal caregivers are given adequate training on how to work with informal caregivers and involve them effectively in health decision making, use of medical or adaptive technologies, or best practices to be used for evaluating and supporting the needs of caregivers.

It is also important to recognize that the majority of long-term care provided to older adults and individuals with disabilities relies on family members, friends, or the individual alone. Many informal caregivers take on these responsibilities without necessary education or support. These individuals may be poorly prepared and emotionally overwhelmed and, as a result, experience stress and burden that can lead to their own morbidity. The committee is aware that informational and training materials and tested programs already exist to assist informal caregivers in understanding the many details of providing health care in the home and to ease their burden and enhance the quality of life of both caregiver and care recipient. However, tested materials and education, support, and skill enhancement programs have not been adequately disseminated or integrated into standard care practices.

Recommendation 5. Relevant professional practice and advocacy groups should develop appropriate certification, credentialing, and/or training standards that will prepare formal caregivers to provide care in the home, develop appropriate informational and training materials

for informal caregivers, and provide guidance for all caregivers to work effectively with other people involved.

RESIDENTIAL ENVIRONMENTS FOR HEALTH CARE

Health care is administered in a variety of nonclinical environments, but the most common one, particularly for individuals who need the greatest level and intensity of health care services, is the home. The two recommendations in this area encourage (1) modifications to existing housing and (2) accessible and universal design of new housing. The implementation of these recommendations would be a good start on an effort to improve the safety and ease of practicing health care in the home. It could improve the health and safety of many care recipients and their caregivers and could facilitate adherence to good health maintenance and treatment practices. Ideally, improvements to housing design would take place in the context of communities that provide transportation, social networking and exercise opportunities, and access to health care and other services.

Safety and Modification of Existing Housing

The committee found poor appreciation of the importance of modifying homes to remove health hazards and barriers to self-management and health care practice and, furthermore, that financial support from federal assistance agencies for home modifications is very limited. The general connection between housing characteristics and health is well established. For example, improving housing conditions to enhance basic sanitation has long been part of a public health response to acute illness. But the characteristics of the home can present significant barriers to autonomy or self-care management and present risk factors for poor health, injury, compromised well-being, and greater dependence on others. Conversely, physical characteristics of homes can enhance resident safety and ability to participate in daily self-care and to utilize effectively health care technologies that are designed to enhance health and well-being.

Home modifications based on professional home assessments can increase functioning, contribute to reducing accidents such as falls, assist caregivers, and enable chronically ill persons and people with disabilities to stay in the community. Such changes are also associated with facilitating hospital discharges, decreasing readmissions, reducing hazards in the home, and improving care coordination. Familiar modifications include installation of such items as grab bars, handrails, stair lifts, increased lighting, and health monitoring equipment as well as reduction of such hazards as broken fixtures and others caused by insufficient home maintenance.

Deciding on which home modifications have highest priority in a given

setting depends on an appropriate assessment of circumstances and the environment. A number of home assessment instruments and programs have been validated and proven to be effective to meet this need. But even if needed modifications are properly identified and prioritized, inadequate funding, gaps in services, and lack of coordination between the health and housing service sectors have resulted in a poorly integrated system that is difficult to access. Even when accessed, progress in making home modifications available has been hampered by this lack of coordination and inadequate reimbursement or financial mechanisms, especially for those who cannot afford them.

Recommendation 6. Federal agencies, including the U.S. Department of Health and Human Services and the Centers for Medicare & Medicaid Services, along with the U.S. Department of Housing and Urban Development and the U.S. Department of Energy, should collaborate to facilitate adequate and appropriate access to health- and safety-related home modifications, especially for those who cannot afford them. The goal should be to enable persons whose homes contain obstacles, hazards, or features that pose a home safety concern, limit self-care management, or hinder the delivery of needed services to obtain home assessments, home modifications, and training in their use.

Accessibility and Universal Design of New Housing

Almost all existing housing in the United States presents problems for conducting health-related activities because physical features limit independent functioning, impede caregiving, and contribute to such accidents as falls. In spite of the fact that a large and growing number of persons, including children, adults, veterans, and older adults, have disabilities and chronic conditions, new housing continues to be built that does not account for their needs (current or future). Although existing homes can be modified to some extent to address some of the limitations, a proactive, preventive, and effective approach would be to plan to address potential problems in the design phase of new and renovated housing, before construction.

Some housing is already required to be built with basic accessibility features that facilitate practice of health care in the home as a result of the Fair Housing Act Amendments of 1998. And 17 states and 30 cities have passed what are called “visitability” codes, which currently apply to 30,000 homes. Some localities offer tax credits, such as Pittsburgh through an ordinance, to encourage installing visitability features in new and renovated housing. The policy in Pittsburgh was impetus for the Pennsylvania Residential VisitAbility Design Tax Credit Act signed into law on October 28, 2006, which offers property owners a tax credit for new construction

and rehabilitation. The Act paves the way for municipalities to provide tax credits to citizens by requiring that such governing bodies administer the tax credit (Self-Determination Housing Project of Pennsylvania, Inc., n.d.).

Visitability, rather than full accessibility, is characterized by such limited features as an accessible entry into the home, appropriately wide doorways and one accessible bathroom. Both the International Code Council, which focuses on building codes, and the American National Standards Institute, which establishes technical standards, including ones associated with accessibility, have endorsed voluntary accessibility standards. These standards facilitate more jurisdictions to pass such visitability codes and encourage legislative consistency throughout the country. To date, however, the federal government has not taken leadership to promote compliance with such standards in housing construction, even for housing for which it provides financial support.

Universal design, a broader and more comprehensive approach than visitability, is intended to suit the needs of persons of all ages, sizes, and abilities, including individuals with a wide range of health conditions and activity limitations. Steps toward universal design in renovation could include such features as anti-scald faucet valve devices, nonslip flooring, lever handles on doors, and a bedroom on the main floor. Such features can help persons and their caregivers carry out everyday tasks and reduce the incidence of serious and costly accidents (e.g., falls, burns). In the long run, implementing universal design in more homes will result in housing that suits the long-term needs of more residents, provides more housing choices for persons with chronic conditions and disabilities, and causes less forced relocation of residents to more costly settings, such as nursing homes.

Issues related to housing accessibility have been acknowledged at the federal level. For example, visitability and universal design are in accord with the objectives of the Safety of Seniors Act (Public Law No. 110-202, passed in 2008). In addition, implementation of the Olmstead decision (in which the U.S. Supreme Court ruled that the Americans with Disabilities Act may require states to provide community-based services rather than institutional placements for individuals with disabilities) requires affordable and accessible housing in the community.

Visitability, accessibility, and universal design of housing all are important to support the practice of health care in the home, but they are not broadly implemented and incentives for doing so are few.

Recommendation 7. Federal agencies, such as the U.S. Department of Housing and Urban Development, the U.S. Department of Veterans Affairs, and the Federal Housing Administration, should take a lead role, along with states and local municipalities, to develop strategies that promote and facilitate increased housing visitability, accessibil-

ity, and universal design in all segments of the market. This might include tax and other financial incentives, local zoning ordinances, model building codes, new products and designs, and related policies that are developed as appropriate with standards-setting organizations (e.g., the International Code Council, the International Electrotechnical Commission, the International Organization for Standardization, and the American National Standards Institute).

RESEARCH AND DEVELOPMENT

In our review of the research literature, the committee learned that there is ample foundational knowledge to apply a human factors lens to home health care, particularly as improvements are considered to make health care safe and effective in the home. However, much of what is known is not being translated effectively into practice, neither in design of equipment and information technology or in the effective targeting and provision of services to all those in need. Consequently, the four recommendations that follow support research and development to address knowledge and communication gaps and facilitate provision of high-quality health care in the home. Specifically, the committee recommends (1) research to enhance coordination among all the people who play a role in health care practice in the home, (2) development of a database of medical devices in order to facilitate device prescription, (3) improved surveys of the people involved in health care in the home and their residential environments, and (4) development of tools for assessing the tasks associated with home-based health care.

Health Care Teamwork and Coordination

Frail elders, adults with disabilities, disabled veterans, and children with special health care needs all require coordination of the care services that they receive in the home. Home-based health care often involves a large number of elements, including multiple care providers, support services, agencies, and complex and dynamic benefit regulations, which are rarely coordinated. However, coordinating those elements has a positive effect on care recipient outcomes and costs of care. When successful, care coordination connects caregivers, improves communication among caregivers and care recipients and ensures that receivers of care obtain appropriate services and resources.

To ensure safe, effective, and efficient care, everyone involved must collaborate as a team with shared objectives. Well-trained primary health care teams that execute customized plans of care are a key element of coordinated care; teamwork and communication among all actors are also

essential to successful care coordination and the delivery of high-quality care. Key factors that influence the smooth functioning of a team include a shared understanding of goals, common information (such as a shared medication list), knowledge of available resources, and allocation and coordination of tasks conducted by each team member.

Barriers to coordination include insufficient resources available to (a) help people who need health care at home to identify and establish connections to appropriate sources of care, (b) facilitate communication and coordination among caregivers involved in home-based health care, and (c) facilitate communication among the people receiving and the people providing health care in the home.

The application of systems analysis techniques, such as task analysis, can help identify problems in care coordination systems and identify potential intervention strategies. Human factors research in the areas of communication, cognitive aiding and decision support, high-fidelity simulation training techniques, and the integration of telehealth technologies could also inform improvements in care coordination.

Recommendation 8 . The Agency for Healthcare Research and Quality should support human factors–based research on the identified barriers to coordination of health care services delivered in the home and support user-centered development and evaluation of programs that may overcome these barriers.

Medical Device Database

It is the responsibility of physicians to prescribe medical devices, but in many cases little information is readily available to guide them in determining the best match between the devices available and a particular care recipient. No resource exists for medical devices, in contrast to the analogous situation in the area of assistive and rehabilitation technologies, for which annotated databases (such as AbleData) are available to assist the provider in determining the most appropriate one of several candidate devices for a given care recipient. Although specialists are apt to receive information about devices specific to the area of their practice, this is much less likely in the case of family and general practitioners, who often are responsible for selecting, recommending, or prescribing the most appropriate device for use at home.

Recommendation 9. The U.S. Food and Drug Administration, in collaboration with device manufacturers, should establish a medical device database for physicians and other providers, including pharmacists, to use when selecting appropriate devices to prescribe or recommend

for people receiving or self-administering health care in the home. Using task analysis and other human factors approaches to populate the medical device database will ensure that it contains information on characteristics of the devices and implications for appropriate care recipient and device operator populations.

Characterizing Caregivers, Care Recipients, and Home Environments

As delivery of health care in the home becomes more common, more coherent strategies and effective policies are needed to support the workforce of individuals who provide this care. Developing these will require a comprehensive understanding of the number and attributes of individuals engaged in health care in the home as well as the context in which care is delivered. Data and data analysis are lacking to accomplish this objective.

National data regarding the numbers of individuals engaged in health care delivery in the home—that is, both formal and informal caregivers—are sparse, and the estimates that do exist vary widely. Although the Bureau of Labor Statistics publishes estimates of the number of workers employed in the home setting for some health care classifications, they do not include all relevant health care workers. For example, data on workers employed directly by care recipients and their families are notably absent. Likewise, national estimates of the number of informal caregivers are obtained from surveys that use different methodological approaches and return significantly different results.

Although numerous national surveys have been designed to answer a broad range of questions regarding health care delivery in the home, with rare exceptions such surveys reflect the relatively limited perspective of the sponsoring agency. For example,

• The Medicare Current Beneficiary Survey (administered by the Centers for Medicare & Medicaid Services) and the Health and Retirement Survey (administered by the National Institute on Aging) are primarily geared toward understanding the health, health services use, and/or economic well-being of older adults and provide no information regarding working-age adults or children or information about home or neighborhood environments.
• The Behavioral Risk Factors Surveillance Survey (administered by the Centers for Disease Control and Prevention, CDC), the National Health Interview Survey (administered by the CDC), and the National Children’s Study (administered by the U.S. Department of Health and Human Services and the U.S. Environmental Protection Agency) all collect information on health characteristics, with limited or no information about the housing context.
• The American Housing Survey (administered by the U.S. Department of Housing and Urban Development) collects detailed information regarding housing, but it does not include questions regarding the health status of residents and does not collect adequate information about home modifications and features on an ongoing basis.

Consequently, although multiple federal agencies collect data on the sociodemographic and health characteristics of populations and on the nation’s housing stock, none of these surveys collects data necessary to link the home, its residents, and the presence of any caregivers, thus limiting understanding of health care delivered in the home. Furthermore, information is altogether lacking about health and functioning of populations linked to the physical, social, and cultural environments in which they live. Finally, in regard to individuals providing care, information is lacking regarding their education, training, competencies, and credentialing, as well as appropriate knowledge about their working conditions in the home.

Better coordination across government agencies that sponsor such surveys and more attention to information about health care that occurs in the home could greatly improve the utility of survey findings for understanding the prevalence and nature of health care delivery in the home.

Recommendation 10. Federal health agencies should coordinate data collection efforts to capture comprehensive information on elements relevant to health care in the home, either in a single survey or through effective use of common elements across surveys. The surveys should collect data on the sociodemographic and health characteristics of individuals receiving care in the home, the sociodemographic attributes of formal and informal caregivers and the nature of the caregiving they provide, and the attributes of the residential settings in which the care recipients live.

Tools for Assessing Home Health Care Tasks and Operators

Persons caring for themselves or others at home as well as formal caregivers vary considerably in their skills, abilities, attitudes, experience, and other characteristics, such as age, culture/ethnicity, and health literacy. In turn, designers of health-related devices and technology systems used in the home are often naïve about the diversity of the user population. They need high-quality information and guidance to better understand user capabilities relative to the task demands of the health-related device or technology that they are developing.

In this environment, valid and reliable tools are needed to match users with tasks and technologies. At this time, health care providers lack the

tools needed to assess whether particular individuals would be able to perform specific health care tasks at home, and medical device and system designers lack information on the demands associated with health-related tasks performed at home and the human capabilities needed to perform them successfully.

Whether used to assess the characteristics of formal or informal caregivers or persons engaged in self-care, task analysis can be used to develop point-of-care tools for use by consumers and caregivers alike in locations where such tasks are encouraged or prescribed. The tools could facilitate identification of potential mismatches between the characteristics, abilities, experiences, and attitudes that an individual brings to a task and the demands associated with the task. Used in ambulatory care settings, at hospital discharge or other transitions of care, and in the home by caregivers or individuals and family members themselves, these tools could enable assessment of prospective task performer’s capabilities in relation to the demands of the task. The tools might range in complexity from brief screening checklists for clinicians to comprehensive assessment batteries that permit nuanced study and tracking of home-based health care tasks by administrators and researchers. The results are likely to help identify types of needed interventions and support aids that would enhance the abilities of individuals to perform health care tasks in home settings safely, effectively, and efficiently.

Recommendation 11. The Agency for Healthcare Research and Quality should collaborate, as necessary, with the National Institute for Disability and Rehabilitation Research, the National Institutes of Health, the U.S. Department of Veterans Affairs, the National Science Foundation, the U.S. Department of Defense, and the Centers for Medicare & Medicaid Services to support development of assessment tools customized for home-based health care, designed to analyze the demands of tasks associated with home-based health care, the operator capabilities required to carry them out, and the relevant capabilities of specific individuals.

Association for the Advancement of Medical Instrumentation. (2009). ANSI/AAMI HE75:2009: Human factors engineering: Design of medical devices. Available: http://www.aami.org/publications/standards/HE75_Ch16_Access_Board.pdf [April 2011].

Self-Determination Housing Project of Pennsylvania, Inc. (n.d.) Promoting visitability in Pennsylvania. Available: http://www.sdhp.org/promoting_visitability_in_pennsy.htm [March 30, 2011].

In the United States, health care devices, technologies, and practices are rapidly moving into the home. The factors driving this migration include the costs of health care, the growing numbers of older adults, the increasing prevalence of chronic conditions and diseases and improved survival rates for people with those conditions and diseases, and a wide range of technological innovations. The health care that results varies considerably in its safety, effectiveness, and efficiency, as well as in its quality and cost.

Health Care Comes Home reviews the state of current knowledge and practice about many aspects of health care in residential settings and explores the short- and long-term effects of emerging trends and technologies. By evaluating existing systems, the book identifies design problems and imbalances between technological system demands and the capabilities of users. Health Care Comes Home recommends critical steps to improve health care in the home. The book's recommendations cover the regulation of health care technologies, proper training and preparation for people who provide in-home care, and how existing housing can be modified and new accessible housing can be better designed for residential health care. The book also identifies knowledge gaps in the field and how these can be addressed through research and development initiatives.

Health Care Comes Home lays the foundation for the integration of human health factors with the design and implementation of home health care devices, technologies, and practices. The book describes ways in which the Agency for Healthcare Research and Quality (AHRQ), the U.S. Food and Drug Administration (FDA), and federal housing agencies can collaborate to improve the quality of health care at home. It is also a valuable resource for residential health care providers and caregivers.

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Future of Healthcare

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The Future of Health Care

Key Stakeholders

The affordable care act, political ideologies, a single-payer system, the rest of the world’s health care in the future, new threats, epidemics, and technology.

In discussions concerning the future of healthcare, certain subjects such as innovations and information technology become paramount. The concerns of the future of health care signify digitalization, enhanced connectivity, and improved relationship between medical professionals and patients, which will lead to better medical attention (Brende et al., 2017). Patents will be in a position of receiving more individualized care in addition to a real-time screening of their symptoms and conditions. The future of health care will depend on avant-garde innovations and technology, which may be further advanced to cater to the needs of each patient.

The major stakeholders who could shape the health care sector in the United States are doctors, government, insurance firms, and pharmaceutical companies. Insurance firms could influence the health care system by regulating the rate at which they sell coverage plans. Pharmaceutical companies generate and distribute medicines that are prescribed by physicians to patients as part of their treatment.

Characteristically, they obtain remuneration via governmental and insurance drug-advantage arrangements (Acosta et al., 2017). Doctors and other health professionals provide medical care while patients are the recipients. Moreover, the government is in a position of influencing the overall cost of care or subsidizing it for the disabled, poor, and the aged. All the stakeholders carry out fundamental responsibilities and duties through which they could shape the health care system in the US.

Presently, the increasing premiums and strict demands from insurance firms are limiting people from obtaining coverage. Insurance firms have for a long time remained profit propelled, although the nature of the service they provide necessitates them to avoid focusing on profitability to influence the health system positively. When insurance firms increase the cost of premiums, they make it difficult for patients to obtain sufficient healthcare attributable to fiscal hardships (Acosta et al., 2017).

Therefore, insurance firms should find a suitable balance involving their accountability between patients and shareholders to shape the health care system positively. Nevertheless, the reports regularly issued by stockholders persuade insurance firms to center strongly on profits when compared to affordability. This continues to make insurance companies maintain stringent regulations against preexisting situations, which results in healthy people being chosen for their plans.

The selected individuals do not have costly expenses as people with chronic conditions. If not addressed, this unethical practice will curtail personal development and influence the health care system in the US negatively by reducing it to a profit-centered sector and preventing patients from obtaining care (Saint Leo University, n.d.).

Contrary to other stakeholders in the health sector, doctors not only uphold integrity but also enjoy direct fiduciary tasks and accountabilities toward their patients. Although health professionals are given remuneration for the services they render, the physician-patient rapport acts as a sacred trust that goes beyond financial reward (Saint Leo University, n.d.). If doctors put efforts to improve their relationship with patients and the care they give, they could easily shape the health care sector in the United States.

Furthermore, the US government has the responsibility of ensuring that its citizens obtain care. How well the government pushes for the improvement of the quality of care could shape the health care industry significantly (Acosta et al., 2017). As responsible stewards, doctors have a crucial role in making sure that patients get sufficient medical attention and regulating the increasing costs of care. To shape the health care system in the US positively, physicians should find a balance between maintaining a gatekeeper task for the insurance firms and remaining an advocate for patients (Saint Leo University, n.d.).

From the time of the inception of the Affordable Care Act, insurer contribution and competition has gradually decreased. Instead of spurring competition, increasingly fewer insurers are providing coverage to the population. Studies establish that the number of people registered in subsidized exchange plans has reduced or remained stagnant at a rate below the target level, which was predicted to ensure proper functioning of the program. Approximately 15 percent of America’s population was not under any insurance coverage in early 2018, which marked a significant rise from 12 percent in 2016 (Rice, Unruh, van Ginneken, Rosenau, & Barnes, 2018).

This was witnessed regardless of there being a huge reduction in the unemployment level. Decreases in the rate of health insurance coverage in the face of employment increase prove that progressively fewer individuals are under Obamacare. Moreover, the people enrolled in the coverage have a high likelihood of being sicker, unemployable, or in poorer conditions when compared to the people in other health plans.

Republicans have managed to eradicate the fine for the lack of health insurance coverage, which has fundamentally resulted in healthier individuals opting out of any such schemes and waiting until they become sickly to enroll. The arising impact is that insurance firms are being left with the sickly people, who utilize more resources, which will continue to compel their increase in the cost of premiums. With the federal health care plans set for every year, organizations that collect premiums are anticipated to pass on the rising cost to their clients (Béland, Rocco, & Waddan, 2018). Therefore, the patients who enroll at the private, state, and federal level have a likelihood of experiencing progressive premium increases.

There are two alternatives as to what could happen to the Affordable Care Act. First, there is a probability that the Affordable Care Act could be repealed devoid of any replacement. Second, a single-payer approach could substitute the Affordable Care Act. If the Affordable Care Act is completely and immediately repealed without being replaced, the level of insured people in the United States would decrease by about 20 million. Furthermore, out-of-pocket expenses for an enrollee in the market would become approximately 7,000 dollars per annum, a rise of 4,000 dollars over the state of affairs (Rice et al., 2018).

Repeal will raise the federal deficit by over 30 billion dollars annually when compared to the status quo, mainly because it could eradicate the Affordable Care Act’s revenue-collection provisions. On the contrary, a single-payer plan will shift the funding of medical care from premiums given by individuals and employers to taxation. Tax-anchored funding is fundamental to true universal coverage because it signifies that every person is automatically covered. However, it could also be disrupting. The entire amount used on medical care could reduce under a single-payer approach though some individuals would ultimately pay less than others.

When people become unwell, they refer nearly unconsciously to questions regarding their diet, physical activity, or the people with whom they were in contact. Their search for treatment then shifts to medicines and physicians (Fox, Feng, & Yumkham, 2017).

Patients trust that doctors’ integrity will result in their dedication to excellence, improved patient outcome, and quality of care. Though such decisions influence people’s welfare, they reveal instances of individual health (Saint Leo University, n.d.). Most significantly, they are greatly established by political ideologies and economic aspects that affect the whole system. The strength of political ideologies on the future lies in the fact that they determine the number of physicians available and people’s ability to get treatment (Fox et al., 2017).

The assessment of the effects of systemic aspects demands evaluation of the basic form of a community’s political establishment, its political philosophy, and health of the population. Political ideologies of the ruling party have the ability to influence all segments of society through checking the existing establishments. Accordingly, this may affect the future of health systems through economic and social strategies that not only shape the interests of a state but also the structure of the labor force (Saint Leo University, n.d.).

Even in the occurrences of liberal political ideologies, the US represents the most considerable types of privatization. Attempts to improve the medical coverage in the United States, for instance, the issue of the Affordable Care Act, have experienced strong opposition from political ideologies of leaders dedicated to a system of free market capitalism. Therefore, progress towards equitable strategies may be restrained by political ideologies and end up affecting the idea of privatization hence negatively influencing the future of health care (Fox et al., 2017).

Although it may be argued that the future of health care offers patients increased management over their treatment, the amount of money that patients are required to pay in a private health institution limits access to treatment to only the wealthy. The inequity entrenched in such inclinations is laid bare in a growing divergence of health in the United States, where individuals are perceived to be either untreatable or very healthy. Liberal political ideologies have a likelihood of strongly affecting the future by decreasing expenditure on medical services, permitting exploitative labor situations, or helping privatization in health care thus compromising access to treatment.

Americans would be better with a single-payer system. This is because the system has the ability to bring down the cost of care in numerous approaches. One way is through reducing the amount of money paid for medical care (increased costs are the major rationale behind the US spending more when compared to other nations). Another means would be through a decrease in administrative expenses for insurance sales and billing.

Moreover, a reduction in profits would decrease the cost of care. Although countries approach their health sector differently, nations with universal coverage have a huge task for government in the regulation of costs and a minimal role when it comes to for-profit insurance firms (Levitt, 2018). Other components of a single-payer arrangement that could reduce medical costs encompass the coverage of more people and reduction or eradication of copayments and deductibles. The net impact on the cost of care depends considerably on the factors under which a single-payer system was planned and executed.

There are already some simplicity factors to single-payer arrangements that have been recommended thus far countrywide and in states such as California. This makes the single-payer plan particularly clear contrary to the present arrangements in the health system, which interlinks public schemes such as Medicaid and Medicare with employer-anchored and individually purchased private health insurance coverage in messy and obscure approaches. To support the single-payer system is the fact that everyone will be covered; any person will be free to seek the services of any hospital or doctor with no out-of-pocket expenses.

Nonetheless, such simplicity has the effect of obscuring trade-offs, which are unavoidable in any intensive health transformation (Levitt, 2018). Enhanced government involvement in the health sector may result in a reduction of prices although such costs offer income to health institutions, doctors, and pharmaceutical companies, which they will strongly resist decreasing. People might fail to pay premiums or any amount at the point of service though they will incur higher taxes. Nonexistence of copayments and deductibles will eliminate monetary barriers to obtaining the required medical attention but will lead to more needless care.

In the future, the world’s health care will ensure that patients are accorded respect and data on chronic conditions forwarded to the team of care, who will then know timely that specialists are required to offer the needed treatment (Saint Leo University, n.d.).

Though it will be easy for patients to find health professionals with specialized knowledge, they will not necessarily have face-to-face interaction as in the present case. A system of interconnected care signifies that numerous specialists will look at a complex issue simultaneously (Obermeyer & Emanuel, 2016). This will facilitate early diagnosis of patients’ problems through constant monitoring prior to their becoming severe. Although it will not be easy transforming a system that is hard-wired to a reactive one, gradual improvement will ultimately make it possible.

In the future, the pressure on health systems will change attributable to a variety of reasons, encompassing the requirements of patients. Each year, new medicines and treatment processes assist in the management of common diseases. This is facilitated by the advancements in technology, which will also result in increased patient empowerment. The world’s health care will improve to enable individualized treatment and personalized medicine.

With improved findings concerning the specific genetic structure of a tumor, the future health care systems will be in a position of offering efficacious medication and treatment that target it. In the recent past, the chances of surviving cancer have improved from 25% to about 50% (Obermeyer & Emanuel, 2016). The world’s health care systems in the future will improve the possibility of treating cancer to over 75%, which will be enhanced by intensive research.

In the future, the health sector will experience problems in trying to cope with the consequences of terror attacks. In most instances, such preparations will call for the divagation of other resources. As seen from past occurrences such as the anthrax attacks, the health system is not in a position to rapidly and successfully cope with a terrorist attack. In the future, there will be growing pressure to increase the capacity of local health systems to ensure that they remain prepared.

Additionally, in the course of 1980s and 1990s, there was a shift of the consideration of the health system to the challenges posed by chronic diseases, which presented the view that infectious illnesses were not a threat in the US anymore. However, the emergence of West Nile Virus and acute respiratory syndrome of late, the gradual rise in HIV/AIDS nationally and its fast growth internationally, and the problem of multidrug-resistant bacteria have gainsaid such a perception (Brende et al., 2017). It is now evident that infectious diseases are a serious threat that will probably result in higher interest in studying them as an area of specialization while seeking to restructure the public health sector.

The World Health Organization (WHO) has established that some diseases such as Ebola and Zika have a possibility of becoming epidemics in the future. The emergence of epidemics will compel scientists and medical specialists to come together in an effort of prioritizing the diseases that are anticipated to cause outbreaks, and for which either inadequate or no medical measures have been established.

The occurrence of epidemics in the future will offer a foundation for promoting research and development preparedness and tackling the outbreaks while preventing augment of such incidences. Different teams of scientists will also join hands to identify the likelihood of new strange illnesses and their potential to bring severe epidemics (Tambo, Kazienga, Talla, Chengho, & Fotsing, 2017). The significance of continued research and development endeavors lies in their ability to prepare the future health system for new potential diseases and epidemics.

Technological advancements will create hospitable communities that will facilitate a feeling of belonging, harmony, and mutuality anchored in shared trust and reverence to generate socially responsible settings that will challenge every stakeholder to listen, discover, transform, and serve. Respecting the dignity of patients will make doctors further their commitment to improving the quality of care (Saint Leo University, n.d.).

In the future, technology will also lead to the establishment of new forms of biomarkers (chemical elements which may be assessed in biological samples, for instance, urine and blood) to discover a heightened risk or early commencement of cardiovascular disease. Advanced imaging is also proceeding at a swift pace. In the future, improved technology will make it possible to use images to determine underlying biological progressions inside patients’ bodies (Tambo et al., 2017). For instance, imaging will not only help in the identification of the extent to which an artery to the heart is blocked but also the nature of such blockage and the required action.

With improved technology, regenerative medicine will keep on being a very essential field of study. For example, in the future, it will be possible to reprogram some of the cells in the body to undertake tasks that vary from their initial function (Tambo et al., 2017). Cells that form a scar in the occurrence of heart disease may be altered to make them elicit proper pumping of the patient’s heart, which will greatly improve the quality of care and ensure long life span. With the emergence of wearable technology, conventional patient records, as well as remote monitoring and control, health systems will place emphasis on the collection and analysis of huge quantities of data. This will result in the personalization of care as treatment will be individually adapted to the needs of the patient.

The main stakeholders who could influence the health care sector in the United States are health professionals, government, pharmaceutical companies, and insurance firms. In the future, the Affordable Care Act could either be repealed without any replacement or a single-payer approach could substitute it. The impact of political ideologies on the future health systems is in the determination the number of caregivers available and patients’ access to treatment.

A single-payer system would benefit Americans because it has the ability to reduce the cost of care in various approaches. The implication of persistent research and development attempts lies in their capability to prepare the future health system for new probable conditions and epidemics. Prospective health care will depend on ultramodern innovations and technology, which might be further enhanced to cater for the welfare of each patient.

Acosta, J. D., Whitley, M. D., May, L. W., Dubowitz, T., Williams, M. V., & Chandra, A. (2017). Stakeholder perspectives on a culture of health: Key findings. Rand Health Quarterly , 6 (3), 1-10.

Béland, D., Rocco, P., & Waddan, A. (2018). Obamacare in the Trump era: Where are we now and where are we going? The Political Quarterly , 89 (4), 687-694.

Brende, B., Farrar, J., Gashumba, D., Moedas, C., Mundel, T., Shiozaki, Y.,… Røttingen, J. A. (2017). CEPI- A new global R&D organization for epidemic preparedness and response. The Lancet , 389 (10066), 233-235.

Fox, A. M., Feng, W., & Yumkham, R. (2017). State political ideology, policies, and health behaviors: The case of tobacco. Social Science & Medicine , 181 , 139-147.

Levitt, L. (2018). Single-payer health care: Opportunities and vulnerabilities. Jama , 319 (16), 1646-1647.

Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future- Big data, machine learning, and clinical medicine. The New England Journal of Medicine , 375 (13), 1216-1222.

Rice, T., Unruh, L. Y., van Ginneken, E., Rosenau, P., & Barnes, A. J. (2018). Universal coverage reforms in the USA: From Obamacare through Trump. Health Policy , 122 (7), 698-702.

Saint Leo University. (n.d.). History, values, & catholic roots . Web.

Tambo, E., Kazienga, A., Talla, M., Chengho, C. F., & Fotsing, C. (2017). Digital technology and mobile applications impact on Zika and Ebola epidemics data sharing and emergency response. Journal of Health & Medical Informatics , 8 , 254-260.

More From Forbes

AI In Healthcare: Revolutionizing Medicine Or Overhyped Promise?

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AI is going to change healthcare forever. In the near future, we can look forward to revolutionary new cures and treatments, personalized medicine, and a new generation of hospitals and facilities where super-smart robots take care of everything from cleaning to brain surgery .

At least – so we’re told . Now, I’m a believer in the power of AI to change the world for the better. But I can also plainly see that there’s a lot of hype around it. Technology companies stand to make trillions from selling it, and they all want us to believe their models and algorithms are the ones that will change the world.

In a world that’s quickly becoming flooded with AI washing , it’s critical that we learn how to cut through the hype and marketing bluster. So here I’ll take a look at how well one of the most frequent claims – that it will transform the fields of healthcare, medicine and wellbeing – stands up to scrutiny.

Where Are We Today?

So far, attempts to improve research and delivery of healthcare using AI have provided some encouraging success stories, as well as some clear examples of over-exaggerated expectations.

In drug and vaccine discovery, for example, it accelerated the discovery of mRNA vaccines for COVID-19. The same technology is now being used to create new protections against many other diseases . And generative AI built on similar technology to ChatGPT has also been used to create new immunotherapy drugs .

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It has been used to analyze and interpret medical scans, images, medical records and tissue samples, and has been shown to be able to spot signs of diseases such as cancer and Alzheimer’s disease .

According to the American College Of Surgeons, “Most research shows that scan interpretation from AI is more robust and accurate than those from radiologists, often picking up small, rare spots in the images.”

It can shorten the length of time we need to stay in hospital, with one study of patients diagnosed with pulmonary embolism finding that those triaged with AI tools were able to return home an average of two days earlier.

The use of chatbots to answer patient questions or assist with the training of clinical staff, as well as analytics tools to help hospitals plan their use of resources more efficiently, all promise to streamline delivery and ultimately improve outcomes.

However, that isn’t to say that there haven’t also been missteps and over-inflated claims, particularly in the early days. In 2017, a partnership between IBM and MD Anderson Cancer Center was discontinued after it was found that the Watson Health system’s decision-making wasn’t accurate enough, creating a “credibility gap” with physicians.

In 2002, a study of online symptom-checker and self-diagnosis tools, some of which involve using AI to provide self-triage, found that “overall, the diagnostic and triage accuracy of symptom checkers are variable and of low accuracy … this study demonstrates that reliance upon symptom checkers could pose significant patient safety hazards.”

It should also be noted that despite the fact that many hospitals and healthcare providers are exploring the use of AI systems for creating efficiencies in administerial, record-keeping and staffing processes, it’s hard to find evidence that this has yet led to significant cost savings or improvements in patient care.

What Are The Experts Saying?

If we look beyond the words of marketers and the CEOs of big tech companies, opinions vary on the value that AI has – or will – bring to the field of healthcare.

Geneticist and cardiologist Dr Eric Topol, author of Deep Medicine , says that while AI will probably never replace a thorough physical examination by an experienced doctor, many applications are already proving highly valuable. These include self-administered kits that test for urinary tract infections and analyze lung problems from the sound of a cough.

In the future, he believes , mobile phones are likely to provide inexpensive ultrasound scans, and recognize early warning signs of diabetes from a picture of the retina.

Perhaps most valuable of all, he suggests, will be the ability of AI to restore the “ human element ” to medicine. By handling routine tasks, AI will free up doctors to spend more face-to-face time with patients, enabling them to develop a better understanding of their condition.

This is a position echoed by Dr Fei-Fei Li, director of Stanford University’s Institute for Human-Centered AI, who says “We’re seeing the medical profession using AI technology … [doctors] tell me that medical summaries are very painful … they take away from patients. Now you can get a language model to help.”

However an article in MIT Technology review presents a more cautionary outlook. Tools designed to diagnose disease or predict outbreaks could be trained on limited or biased data – for example, research has shown that some tools are less effective with female patients simply because less women take part in medical studies.

And Dr. Robert Glatter, assistant professor of emergency medicine at the Zucker School of Medicine, together with Dr. Peter Papadakos, professor of anesthesiology and surgery at the University of Rochester Medical Center, in their article No AI Can Ever Learn The Art Of Medicine , argue “Even with the advent and ongoing evolution of AI … empathy is best learned and communicated in the form of bedside teaching by humans – not AI or chatbots.”

The Verdict – Hype Or Reality?

While AI has undoubtedly already made significant impacts and seems likely to become more valuable as we move forward, it’s unlikely to be the literal panacea that will cure all of our ills.

However, we’re in the very early days of the AI revolution, and successes in fields such as drug discovery and detection of early warning signals clearly demonstrate that the potential to improve care and outcomes is real.

Challenges – particularly around data privacy, implementation costs and training of healthcare professionals in its use – will need to be overcome before the most optimistic predictions become viable.

As in other fields, professionals agree that its most valuable applications will involve augmenting rather than replacing human skills, experience and expertise.

For the technology companies building tools, keeping this firmly in mind while developing the next generation of AI-enhanced medical applications will be key to driving real change that will benefit us all.

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The Future of Healthcare Ethics Essay

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Introduction

Why the catholic church holds its position.

• Effect on the Dignity of the Human Person

Consequences of Ignoring the Church

My opinion and counterarguments, annotated bibliography.

This study presents health care ethics as an instrumental element in the practice of medicine. In this regard, the contribution by religious bodies, particularly the Catholic Church, cannot be underscored. However, the conservative and inflexible ethical opinions on various subjects by the Catholic Church have resulted in conflicts between the cathedral and policymakers. One such controversial subject is the issue of abortion.

Consequently, this study tries to investigate the role of the Catholic Church with respect to its contribution to biomedical ethics, including a highlight of how its opinions clash with those of policymakers and scientists. Among the various elements being scrutinized include the reasons why the church holds its firm position on abortion, the effect of abortion on human dignity, which the church is trying to protect, and the consequences that the world may face after ignoring its (church) recommendations. The article supports the church’s stand on illegalizing abortion since the act infringes on human dignity and life. However, the paper also prompts the need to legalize abortion on medical grounds, such as when the life of the mother is at risk.

Medical ethics refers to a set of principles and rules that govern the conduct of health professionals. The principles are formulated by the government in consultation with medical experts and other relevant bodies. In the past, the church played no role in the formulation of medical ethics. In other words, governments enacted the ethical code without involving religious players. However, as time went by, they realized the need to include the church in formulating bioethics to make health delivery effective. The Catholic Church is the commonly consulted body apparently due to its conservative nature regarding ethics (Hanson 67).

Since the establishment of bioethics, medical principles have evolved based on the contributions made by the stakeholders involved in their formulation. The entry of the church into the business of ethics has specifically given a different shape to medical values. One of the controversies that the church has created regarding medical ethics concerns the issue of abortion. Generally, abortion is illegal in most countries across the world since it infringes on the right to life for the unborn child. However, in certain situations, abortion may be ethically conducted to free the mother from life-threatening danger. This decision goes against the teachings of the Catholic Church, which does not approve of any form of abortion, irrespective of the motive. This paper explores the medical ethics, which regulates the issue of abortion with reference to the Catholic Church.

The church condemns all forms of abortion, regardless of the motive of the action. In some cases, medical principles conflict with the church regarding the issue of abortion. On the one hand, doctors argue that it would be justifiable to effect abortion on a mother whose pregnancy is a threat to her life. Additionally, there are controversies regarding the justification of an abortion on the grounds that the pregnancy resulted from rape or incest (Dobbelaere and Pérez-Agote 54). Guided by general ethical principles such as benevolence and patient autonomy, a physician would be tempted to conduct an abortion based on the aforementioned situations. However, the church condemns such abortion on the grounds that the move violates the right to life.

One of the reasons why the church is against abortion is that it goes against the right to life, which is guaranteed by God. Generally, the Catholic Church emphasizes the need to respect the life of all individuals, irrespective of their age, gender, or marital status. According to the church, no one is allowed to take the life of another human being for whatever reason (Schlesinger 51). God is the giver of life. He is the only one who has the power to rightfully take a person’s life. In this regard, an unborn child is viewed as a living person, just like any other human being (Kuhse and Singer 103). Ending a person’s life for any reason amounts to the violation of the principle of respect for life, which is immoral and unjustifiable.

The other reason why the church is against the legalization of abortion for mothers whose lives are threatened by pregnancy or whose expectancy resulted from rape is that such substantiation would attract many cases of unjustifiable abortion. In an unregulated abortion environment, prospective mothers would collude with physicians to perform unwarrantable abortions on the grounds that the pregnancy is a threat to the mothers’ lives (Hanson 34). This situation would, in turn, result in an increase in the number of deaths for unborn babies and hence heightened cases of violation of the right to live.

E ffect on the Dignity of the Human Person

The decision by the Catholic Church to condemn any form of abortion is motivated by the need to protect human dignity. Every person around the globe has a right to live. No one should take the life of any individual on whatever grounds. Scientifically, life commences once a woman conceives and continues until when the person dies. Therefore, the unborn child is a living being who has equal rights, just like other human beings (Dobbelaere and Pérez-Agote 54). Taking his or her life at any stage of the pregnancy is a violation of human dignity.

However, as much as the church protects the dignity of the unborn, it fails to consider the poise of the mother. As noted previously in this paper, the church overlooks the life of the mother who may be seeking an abortion on the grounds that the unborn child is a threat to her life. Medically, some forms of pregnancy result in complications to the mother. They may even precipitate death. Based on the utilitarianism theory of ethics, a person is advised to adopt the course of action, which maximizes positive outcomes (De Lange 9). Based on the theory, a physician would consider conducting an abortion to save the life of the mother. Such a decision would be justified since it maximizes the welfare of the mother who may be having other dependents. However, the church may not allow such an abortion since it may result in poor health and even the death of the concerned mother.

Who is the Church Trying to Protect?

Based on the arguments presented by the church, it is evident that the motive behind the church’s position on abortion is to protect the interests of the unborn child. The church condemns any form of abortion, regardless of the justifications presented either by the mother or the medical personnel. In some cases, the mother may be in a situation in which she cannot successfully deliver a child without losing her life. However, physicians may consider conducting an abortion to save the life of the mother. The church does not approve of such abortions, hence illustrating that it totally protects the lives of unborn children. It does not consider the life of the mothers. Instead, it only emphasizes the need to protect the lives of unborn children.

The issue of abortion is a weighty one since it requires consultation with all the relevant stakeholders when formulating the ethical code to address it. The government and policymakers at large need to consider the church to establish a code that reflects the moral values of the society. On the issue of abortion, serious consequences may arise from the failure by the policymakers to consult the church. From the policymakers’ perspective, it would be imperative to allow abortion in certain situations, such as when the mother’s life is at stake. On the other hand, the church would be opposed to such proposals on the grounds that abortion amounts to the disrespect of human life (Tomašević 148).

If policymakers ignore the church when making medical ethics, perhaps abortion would be legalized in some cases. Such legalization would open up the way for irresponsible physicians to conduct unjustifiable abortions for selfish interests. The result would be that the number of infants’ death would increase, hence threatening the next generations.

A decrease in the youthful population in the future would negatively affect governments economically, socially, and politically. Besides, the church spells several punishments that may be effected by God on mankind if they fail to respect His teachings. Such punishments include natural calamities, which would directly harm humankind (Tomašević 149). If the church’s position on such punishments is anything to go by, it would be imperative to consider its stipulations when formulating medical ethics.

Based on the analysis of the issue, I would adopt a more conservative approach to the issue of abortion. On one side, I would support the church in its attempt to have abortion illegalized to protect the dignity of unborn children. The life of unborn children is as important as the lives of the rest of the population, a situation that underscores the need to protect them from any harm. The future of the world largely depends on unborn children. Hence, legalizing abortion would threaten the future of the next generations. However, other scholars such as Schlesinger argue that as much as the unborn infants need to be protected against harm, abortion needs to be legalized if it is conducted to mitigate an imminent danger to the mother (43).

The mother’s life may be more important relative to that of the unborn child since, in some cases, the mother may have several dependents (Schlesinger 43). Additionally, there is no guarantee that the unborn child will survive after birth, given the high child mortality rates. Therefore, doctors need to prioritize the mother’s life. If the pregnancy may lead to her demise, they need to approve an abortion. However, abortion should only be considered if the mother is at risk of death. Other reasons such as rape and incest should not be regarded as justifications for abortion. However, the decision to terminate the pregnancy should only be effected after approval by the concerned mother.

The Catholic Church is central to the formulation of medical ethics. Its contribution to modern bioethics cannot be underestimated. However, controversies present themselves since the church contradicts the current legislation regarding clinical practice. A case in point is the church’s position on the issue of abortion. As it stands now, most governments across the world allow abortion to be conducted in special situations such as when the life of the mother is at risk. On the other hand, the church is entirely against any form of abortion, irrespective of the justification.

The aim of fighting abortion in its entirety is to protect the dignity of the unborn kid. However, critics of the church’s position argue that it ignores the welfare of the mother while overemphasizing the dignity of the unborn child. My opinion on the issue is that mothers whose pregnancies are a threat to their lives should be allowed to do an abortion. Any other reason apart from the possible demise of the mother should not be used to justify an abortion.

De Lange, Magdalena. “Dealing with Bioethical Dilemmas: A Survey and Analysis of Responses from Ministers in the Reformed Churches in South Africa.” HTS Theological Studies , vol. 68, no. 1, 2012, pp. 1-10.

De Lange’s article investigates the impact of the rapid and increasing scientific advancements on the churches’ contribution to biomedical ethics. The author believes that speedy technological and scientific advancements in the medical field have overtaken the pace at which Christian ethicists are able to contemplate and develop a logical approach to bioethical principles, including abortion. To achieve the study’s objective, the researcher developed and issued bioethical questionnaires to ministers from the Reformed Churches in South Africa (RCSA). The questionnaires were meant to clarify specific bioethical issues confronted by the ministers in their ministry, the specific value judgment inclinations they came across during counseling sessions, and the theoretical frameworks they apply when confronted by such ethical situations. The study results highlight the need to take appropriate courses of action in bioethics through suggested approaches such as the initial bioethical training for theological students and the need to keep the ethical debate alive through workshops, short courses, and seminars for practicing ministers.

Dobbelaere, Karel, and Alfonso Pérez-Agote. The Intimate. Polity and the Catholic Church: Laws about Life, Death and the Family in So-called Catholic Countries . Leuven University Press, 2015.

The objective of this book is to provide an insight into the impact of secularism regarding the dwindled participation of the Catholic Church in ethical debates such as abortion. The authors are convinced that the influence of the Catholic Church on society’s ethical issues has declined. They illustrate this trend by comparing the past and present influences of the church on ethical debates such as the contested issue concerning when life begins. For example, according to the church, life begins at conception. As a result, abortion should be regarded as an unethical practice. According to the writers, such views would have influenced previous societies’ ethical principles. However, today, such perspectives have been met with remarkable defense by ethicists, parliamentarians, politicians, media, and professionals. The writers emphasize the need for anti-Catholics to change and consider the church’s ethical contribution, warning of dire consequences if ignored.

Hanson, Eric. The Catholic Church in World Politics . Princeton University Press, 2014.

The book aims at examining the influence of the church in the current national and international political systems, including the corresponding impact of the modern systems on internal matters of the cathedral. One of the issues the author touches on is bioethics. Hanson believes that the Catholic Church has deeply shaped the international system, citing abortion as a fundamental example. The book achieves this objective by providing a historical outlook of the Catholic Church’s participation in politics, ethics, its political organization, its political ideology, and its role in contemporary national and regional politics and ethics. The book examines systems in Western Europe, America, Eastern Europe, and the international framework that governs the rest of the structures. Regarding bioethics, the writer concludes that the Catholic Church has played an influential role in shaping ethical policies such as abortion in both national and international systems through its constant consultation with policymakers.

Kuhse, Helga, and Peter Singer. A Companion to Bioethics . John Wiley & Sons, 2013.

The book provides a comprehensive outlook on bioethics for students, nurses, teachers, ethics consultants, and doctors. The authors present an elaborate introduction on the subject of bioethics. They also illustrate its (bioethics) relationship with other cadres such as law, gender, religion, and culture—one of the philosophical and bioethical issues that the writers highlight is the right to life. In the central chapters, the writers examine issues relating to embryos, fetuses, human reproduction, and life and death. In the last sections of the publication, they provide a detailed review of how the ethical issues affect the practice of healthcare. In conclusion, Kuhse and Peter incite a discussion by engaging the audience in a critical review concerning the role of ethics committees, as well as the methodology of teaching bioethics in related fields.

Schlesinger, Eugene. “From Rights to Rites: A Eucharistic Reframing of the Abortion Debate.” Anglican Theological Review , vol. 94, no. 1, 2012, pp. 37-57.

The objective of this article is to provide an insight into how most of the divisive expression on the abortion debate is toxic. In the context of the cultural climate, the writer examines the various arguments regarding rights, the beginning of life, and moral virtues. In addition, the writer uses the Eucharistic liturgy to analyze, inform, and promote the transformation of the way contemporary Christians undertake the debate. To achieve this goal, the author develops a Eucharistic bodily account, which he describes as ungraspable and indefinable. He uses it to illustrate how the various arguments on abortion are contradictory from a biblical perspective. The article supports the Eucharistic approach by denoting that it is voluntary and that it does not violate a person’s conscience. According to the author, a more Eucharistic approach should be applied to the abortion debate to promote a better future composed of virtues such as sharing and unity.

Tomašević, Luka. “Bioethics in Catholic Theology and Scientific Bioethics.” International Journal of BioMedicine , vol. 3, no. 2, 2013, pp. 145-149.

The paper is cognizant of the many ethical issues faced in the humanistic and professional worlds. One of the critical issues that the article seeks to address is the threat to human life. According to the author, life is a gift from God. As such, it needs to be respected. The article presents man as a threat not only to his life but also that of other living beings. Moreover, the writer illustrates his worry on how biocentrism has slowly replaced anthropocentrism, a Christian perspective. This situation has caused a paradoxical challenge to a man in which he has an obligation to protect nature, including life, although he does not require it (nature) to protect himself. Therefore, the script tries to answer the question of how a man can preserve life, suggesting ways in which he can find a balance between the development of modern trends and ideas and the preservation of life. One of the propositions the article makes is for man to limit his actions such as abortion through guidance by Christianity.

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The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

Shiva maleki varnosfaderani.

1 Department of Electrical Engineering, Wayne State University, Detroit, MI 48202, USA

Mohamad Forouzanfar

2 Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada

3 Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada

Associated Data

Not Applicable.

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI’s potential to mitigate these issues and aims to critically assess AI’s integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI’s transformative potential, this review equips researchers with a deeper understanding of AI’s current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

1. Introduction

In recent years, artificial intelligence (AI) has emerged as a transformative force in various sectors, with healthcare being one of the most significant [ 1 ]. The integration of AI into hospitals and clinics represents a paradigm shift in how medical care is delivered and managed. This paper aims to explore the multifaceted role of AI in healthcare settings, focusing on its impact on clinical decision-making, hospital operations, medical diagnostics, patient care, and the ethical considerations it raises.

The concept of AI in healthcare is not new; it dates back to the early days of computer science when researchers first envisioned machines capable of mimicking human intelligence [ 2 ]. However, it was not until the advancement of machine learning algorithms [ 3 ] and the exponential increase in computational power and data availability [ 4 ] that AI applications in healthcare truly began to flourish. This evolution has been marked by significant milestones, from early expert systems [ 5 ] to advanced neural networks capable of outperforming human experts in specific tasks [ 6 ].

Today, AI in healthcare encompasses a broad range of applications [ 7 ]. In clinical settings, it assists in diagnosing diseases, predicting patient outcomes, and personalizing treatment plans [ 8 ]. In hospital management, AI optimizes operational efficiency, streamlines administrative tasks, and improves patient flow and scheduling [ 9 ]. In the field of medical diagnostics, AI enhances the accuracy and speed of image analysis in radiology and pathology [ 10 ]. Moreover, AI plays a crucial role in patient care through remote monitoring, telemedicine, and virtual assistance, fundamentally altering the patient–doctor interaction paradigm [ 11 ].

This paper explores artificial intelligence’s evolving role in healthcare, focusing on its application in hospitals and clinics. In consideration of the extensive scope of this study, we employed a meticulous approach in selecting references, focusing primarily on works published within reputable journals over the past five years. Our search was conducted using both Google Scholar and PubMed, ensuring a comprehensive exploration of the pertinent literature. Figure 1 provides a comprehensive overview of the key topics addressed in this paper. We start with AI in clinical decision-making, highlighting its use in diagnosis, prognosis, and personalized medicine through specific disease case studies. The discussion then moves to AI’s role in improving hospital operations and management, including logistics, administrative tasks, and scheduling. Further, we examine AI in medical imaging and diagnostics, where it enhances accuracy and efficiency in radiology and pathology. This paper also covers AI’s impact on patient care and monitoring, with a look at AI-powered wearables and virtual nursing assistants, and the expansion of telemedicine. We also discuss methodologies to assess the performance of AI healthcare solutions. Ethical considerations and challenges of AI integration, such as privacy, bias, and data security, are addressed, followed by a look at the future of AI in healthcare, considering its potential to improve patient outcomes and respond to global health crises.

Comprehensive overview of AI applications in hospitals and clinics: detailed exploration of key topics addressed in this paper.

2. AI in Clinical Decision-Making

This section explores how AI, with its advanced learning and processing capabilities, is reshaping the domain of medical diagnostics and treatment. By harnessing the power of AI, healthcare professionals are now equipped with tools that provide deeper insights into patient data. This leads to more accurate diagnoses and effective treatment plans. We will explore three critical aspects: AI algorithms for diagnosis and prognosis, case studies of AI in detecting diseases like cancer and diabetes, and AI’s role in the growing field of personalized medicine.

2.1. AI Algorithms for Diagnosis and Prognosis

AI algorithms are becoming important contributors in diagnosing and predicting diseases and offer new insights to healthcare. These algorithms analyze vast amounts of medical data to identify patterns and correlations that might elude human analysis. For instance, in oncology, AI algorithms can sift through radiographic images, genetic information, and patient histories to detect cancer at early stages [ 12 ]. Similarly, in the field of cardiology, AI models are employed to predict heart attacks and strokes by analyzing ECG patterns and other vital signs [ 13 ].

One of the key strengths of AI in diagnosis is its ability to continually learn and improve. As these algorithms are exposed to more data, their diagnostic precision and predictive accuracy are enhanced. This is crucial in managing complex and chronic diseases where early detection and timely intervention can be life-saving [ 14 ].

Moreover, AI’s role in prognosis is equally transformative [ 15 , 16 ]. By analyzing patterns in disease progression, AI can forecast potential complications, enabling healthcare professionals to devise preemptive strategies. This is particularly important in chronic diseases like diabetes, where AI can predict potential risks, such as kidney failure or vision loss, by analyzing blood sugar levels, lifestyle factors, and treatment responses over time [ 17 ].

AI algorithms can be broadly categorized into machine learning, deep learning, and natural language processing, each with unique strengths and applications:

• Machine learning (ML): ML algorithms learn from data to make predictions or decisions without being explicitly programmed for the task [ 18 ]. In healthcare, supervised learning algorithms have been instrumental in developing predictive models for patient outcomes based on historical data [ 19 ]. Unsupervised learning, on the other hand, is used to identify patterns or clusters within data, useful in discovering novel disease subtypes [ 20 ]. Reinforcement learning, where algorithms learn to make sequences of decisions by trial and error, has potential in personalized treatment optimization [ 21 ].
• Deep learning (DL): A subset of ML, deep learning uses neural networks with multiple layers (hence “deep”) to analyze complex data structures. Convolutional Neural Networks (CNNs) are particularly effective in processing imaging data, making them invaluable for diagnosing diseases from medical images like X-rays or MRIs [ 22 ]. Some other advanced CNN architectures include Residual Network (ResNet), Inception, Visual Geometry Group (VGG), and Graph Convolutional Networks (GCNs), each with its own strengths and applications in image analysis, classification, and graph data processing [ 23 ]. Recurrent Neural Networks (RNNs), known for their ability to handle sequential data, are used for analyzing time-series data, such as physiological signals collected during patient monitoring, to predict health deteriorations or outcomes over time [ 24 ]. For instance, Long Short-Term Memory (LSTM) networks, a sophisticated variant of RNNs, have been extensively utilized in the detection of sleep apnea using polysomnography data [ 25 ]. Additionally, Transformer models, such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer), offer revolutionary approaches to processing natural language in clinical notes, enabling more accurate extraction of patient information and insights. Generative Adversarial Networks (GANs) [ 26 ] and conditional diffusion models [ 27 ] have emerged as a powerful tool for generating synthetic medical images for training without privacy concerns, while Graph Neural Networks (GNNs) are unlocking new possibilities in modeling complex biological and health-related networks, from predicting protein interactions to understanding disease pathways.

Table 1 provides a summary of the various deep learning models discussed, including their applications, strengths, and areas of healthcare they are transforming.

Overview of advanced deep learning models in healthcare diagnosis and prognosis.

• 3. Natural language processing (NLP): NLP algorithms allow computers to understand and interpret human language. In healthcare, NLP is used to extract meaningful information from unstructured data sources like clinical notes or the research literature, aiding in both diagnostic processes and the aggregation of knowledge for prognosis estimation [ 33 ]. An example of such a language model is the GatorTron [ 34 ]. It is a large-scale Transformer-based NLP model tailored for the healthcare domain. It utilizes the Transformer architecture, known for its efficiency in handling sequence-to-sequence tasks and its ability to process large datasets, to interpret and analyze electronic health records. With its 8.9 billion parameters, GatorTron is trained on over 90 billion words of clinical text, making it a highly advanced model for extracting and understanding complex medical information from unstructured data sources.

AI algorithms are not just tools for efficient diagnosis and prognosis; they represent a paradigm shift in understanding and managing health and disease. The next sections will provide deeper insights into specific case studies and the role of AI in personalizing medical care, further highlighting AI’s profound impact on clinical decision-making.

2.2. Case Studies of AI in Detecting Diseases

The potential of AI in the early detection and accurate diagnosis of diseases such as cancer, diabetes, and other critical conditions has been demonstrated in various case studies. This subsection explores some notable examples, illustrating how AI technology is making strides in the field of disease detection:

• Cancer detection: One of the most groundbreaking applications of AI is in the early detection of cancer. A notable case study involves the use of deep learning algorithms in the analysis of mammograms for breast cancer detection. Research has shown that AI can identify patterns in mammographic images that are indicative of cancerous growths, often with greater accuracy than traditional methods. A notable study published in the journal Nature reported the development of an AI model by Google Health [ 35 ]. This model was trained on a large dataset of mammograms and demonstrated the ability to detect breast cancer more accurately than human radiologists. The AI system showed a reduction in both false positives and false negatives, key factors in cancer diagnostics. This progress in AI technology is significant because early detection of breast cancer can dramatically improve prognosis and treatment outcomes.

In one study, several supervised classification algorithms were applied to predict and classify eight diabetes complications, including metabolic syndrome, dyslipidemia, neuropathy, nephropathy, diabetic foot, hypertension, obesity, and retinopathy [ 36 ]. The dataset utilized in this study comprises 79 input attributes, including results of medical tests and demographic information collected from 884 patients. The performance of the models was evaluated using the accuracy and F1 score, reaching a maximum of 97.8% and 97.7%, respectively. Among different classifiers, random forest (RF), Adaboost, and XGBoost achieved the best performance. This high level of accuracy demonstrates the potential of machine learning in effectively predicting diabetes complications.

Another study focused on evaluating the efficacy of machine learning algorithms in predicting complications and poor glycemic control in nonadherent type 2 diabetes patients [ 37 ]. This real-world study used data from 800 type 2 diabetes patients, of which 165 met the inclusion criteria. Different machine learning algorithms were used to develop prediction models, with the predictive performance assessed using the area under the curve. The highest performance scores for predicting various complications such as diabetic nephropathy, neuropathy, angiopathy, and eye disease were 90.2%, 85.9%, 88.9%, and 83.2%, showcasing the effectiveness of these models.

One innovative study in this area focused on a machine learning-based prediction model that performs both binary and multiple classifications of heart disease [ 38 ]. The model, known as Fuzzy-GBDT, integrates fuzzy logic with a gradient boosting decision tree to streamline data complexity and improve prediction accuracy. Additionally, to avoid overfitting, the model incorporates a bagging technique, enhancing its capability to classify the severity of heart disease. The evaluation results of this model show excellent accuracy and stability in predicting heart disease, demonstrating its potential as a valuable tool in healthcare.

Another interesting study introduces a cutting-edge healthcare system that employs ensemble deep learning coupled with feature fusion approaches [ 39 ]. This system is designed to overcome the limitations of traditional machine learning models that struggle with high-dimensional datasets. It achieves this by integrating sensor data with electronic medical records, creating a more holistic dataset for heart disease prediction. The system uses the information gain technique to streamline this dataset, focusing on the most relevant features and thereby reducing computational complexity. A key aspect of this model is the application of conditional probability for precise feature weighting, enhancing the overall performance of the system. Impressively, this ensemble deep learning model achieved an accuracy of 98.5%, outperforming existing models and illustrating its efficacy in heart disease prediction.

One area of notable advancement is the use of deep learning in neuroimaging studies. DL’s ability to process and learn from raw data through complex, nonlinear transformations makes it well suited for identifying the subtle and diffuse alterations characteristic of many neurological and psychiatric disorders. Research in this domain has shown that DL can be a powerful tool in the ongoing search for biomarkers of such conditions, offering potential breakthroughs in understanding and diagnosing brain-based disorders [ 40 ].

Furthering this progress, a comprehensive review of deep learning techniques in the prognosis of a range of neuropsychiatric and neurological disorders, such as stroke, Alzheimer’s, Parkinson’s, epilepsy, autism, migraine, cerebral palsy, and multiple sclerosis, has underscored deep learning’s versatility in addressing real-life challenges across various domains, including disease diagnosis [ 41 ]. In the specific case of Alzheimer’s Disease (AD), the most common cause of dementia, deep learning has shown promise in enhancing diagnosis accuracy. Utilizing Convolutional Neural Networks (CNNs), researchers have developed frameworks for detecting AD characteristics from Magnetic Resonance Imaging (MRI) data [ 42 ]. By considering different stages of dementia and creating high-resolution disease probability maps, these models provide intuitive visualizations of individual AD risk. This approach, especially when addressing class imbalance in datasets, has achieved high accuracy, surpassing existing methods. The adaptation of such models to extensive datasets like the Alzheimer’s Disease Neuroimaging Initiative (ADNI) further validates their effectiveness in predicting AD classes.

• Key insights: These case studies highlight AI’s significant role in advancing disease detection across multiple medical disciplines, offering accurate and timely diagnoses, often through non-invasive methods. However, as AI technology continues to evolve, there is a critical need for addressing challenges such as data privacy, algorithmic transparency, and ensuring equitable access to these technologies. Future developments should focus on creating more robust AI systems that can handle diverse datasets, thereby reducing potential biases in diagnosis. Additionally, integrating AI with traditional diagnostic methods and enhancing interdisciplinary collaboration among technologists, clinicians, and patients will be key to harnessing AI’s full potential in disease detection and management.

2.3. The Role of AI in Personalized Medicine

The advent of AI in healthcare has boosted the growth of personalized medicine, a paradigm that tailors medical treatment to the individual characteristics of each patient. This subsection explores how AI is instrumental in driving this personalized approach, offering new insights into patient care that were previously unattainable:

A prime example of this application is a study focusing on nonmuscle invasive urothelial carcinoma, a type of bladder cancer known for its high recurrence risk [ 43 ]. In this study, researchers employed a machine learning algorithm to analyze genomic data from patients at their initial presentation. They aimed to identify genes most predictive of recurrence within five years following transurethral resection of the bladder tumor. The study involved whole-genome profiling of 112 frozen nonmuscle invasive urothelial carcinoma specimens using Human WG-6 BeadChips. A genetic programming algorithm was then applied to evolve classifier mathematical models for outcome prediction. The process involved cross-validation-based resampling and assessing gene use frequencies to pinpoint the most prognostic genes. These genes were subsequently combined into rules within a voting algorithm to predict the likelihood of cancer recurrence. Of the genes analyzed, 21 were identified as predictive of recurrence. Further validation through the quantitative polymerase chain reaction was conducted on a subset of 100 patients. The results were promising: a five-gene combined rule using the voting algorithm showed 77% sensitivity and 85% specificity in predicting recurrence in the training set. Additionally, a three-gene rule was developed, offering 80% sensitivity and 90% specificity in the training set for recurrence prediction.

In recent years, AI has made remarkable strides in drug development. Exscientia introduced the first AI-designed drug molecule for clinical trials in early 2020 [ 46 ]. DeepMind’s AlphaFold then achieved a breakthrough in July 2021 by predicting structures for over 330,000 proteins, including the entire human genome. In 2022, Insilico Medicine started Phase I trials for an AI-discovered molecule, a process significantly faster and more cost-effective than traditional methods. By 2023, AbSci had innovated in creating antibodies using generative AI, and Insilico Medicine saw an AI-designed drug receive FDA Orphan Drug Designation, with Phase II trials planned shortly thereafter. These milestones mark a transformative era in AI-driven drug discovery.

AI’s application extends to the identification of novel proteins or genes as potential disease targets, with systems capable of predicting the 3D structures of these targets using deep learning [ 47 ]. AI is also revolutionizing molecular simulations and the prediction of drug properties such as toxicity and bioactivity, enabling high-fidelity simulations that can be run entirely in silico [ 44 ]. Moreover, AI is shifting the paradigm of traditional drug discovery from screening large libraries of molecules to generating novel drug molecules from scratch [ 48 ]. This approach can enhance the efficiency of the drug discovery process and can lead to the development of novel therapies.

The growing industry interest in AI-enabled drug discovery is evident from the substantial investments flowing into the sector. The promise of lower costs, shorter development timelines, and the potential to treat currently incurable conditions positions AI as an important tool in the future of drug development.

The advances of AI in drug development underscore the necessity for legal and policy frameworks to adapt to these rapid technological changes, ensuring the continued assurance of drug safety and efficacy while harnessing the full potential of AI in healthcare.

• 3. Customizing treatment plans: AI systems are adept at integrating and analyzing various types of health data—from clinical records and lab results to lifestyle information and environmental factors. This capability allows healthcare providers to create more refined and comprehensive treatment plans [ 49 ]. For instance, in managing chronic diseases like diabetes, AI can analyze data from wearable devices, diet logs, and blood sugar readings to recommend personalized lifestyle and medication adjustments for better disease management [ 50 ].
• 4. AI in mental health: In the field of mental health, AI is used to personalize treatment approaches. By monitoring patterns in speech [ 51 ], behavior [ 52 ], and social media activity [ 53 , 54 ], AI tools can help in identifying the onset of mental health issues and suggest interventions tailored to the individual’s unique situation. This personalized approach is crucial in mental health, where treatment efficacy can vary significantly from person to person.

In future research and development within mental health treatment, a promising direction is the integration of AI systems with emotional intelligence [ 55 ]. Such systems could be crucial in early detection and intervention of mental health disorders by analyzing speech and behavior patterns for signs of conditions like depression or anxiety. Further exploration into personalizing therapy using AI could lead to more individualized and effective care.

Addressing accessibility is also crucial; AI-powered chatbots or virtual assistants can provide immediate support, overcoming barriers to traditional mental health services. Moreover, incorporating AI to assist therapists in real time during sessions could significantly enhance the effectiveness of therapy. Focusing on these aspects can transform mental health care into a more empathetic, accessible, and personalized practice, ultimately improving patient outcomes and support.

• 5. Key insights: While the integration of AI into personalized medicine offers transformative potential, it also presents a spectrum of challenges that must be addressed. Beyond data privacy and algorithmic bias, significant concerns include interoperability and data integration across diverse healthcare systems [ 56 ], ensuring AI systems are compliant with regulatory and ethical standards, and establishing their clinical validity and reliability [ 57 ].

Moreover, health equity remains a critical challenge, as AI must be accessible and beneficial to all population segments, avoiding disparities in healthcare [ 58 ]. The scalability and generalization of AI systems to various patient demographics and healthcare environments is also essential. Equally important is the training and acceptance of these tools among healthcare professionals. While AI may excel in certain diagnostic tasks, it serves as a valuable tool that enhances the capabilities of healthcare professionals rather than replacing human judgment entirely. Therefore, the integration of AI into healthcare workflows should be viewed as a symbiotic relationship, ultimately leading to improved patient outcomes. Additionally, cost considerations and effective resource allocation pose challenges in implementing AI solutions in healthcare settings [ 59 ].

3. AI in Hospital Operations and Management

In the complex and dynamic environment of hospitals and clinics, efficient operations and management are crucial for delivering quality healthcare. The integration of AI into these aspects creates a new era in healthcare management. This section explores how AI is being leveraged to revolutionize hospital operations, enhancing efficiency, reducing costs, and improving patient care. We will explore three primary areas: AI’s role in optimizing logistics and resource management, its application in automating administrative tasks, and its contribution to improving patient flow and scheduling.

Table 2 summarizes the transformative applications of AI in hospital operations and management.

Transformative applications of AI in hospital management.

3.1. AI for Hospital Logistics and Resource Management

Effective logistics and resource management are vital for the smooth functioning of any healthcare facility. AI technologies are playing an increasingly significant role in optimizing these aspects, leading to more efficient and cost-effective operations:

• Inventory management: AI systems are being used to predictively manage inventory in hospitals [ 60 , 61 ]. By analyzing usage patterns, patient inflow, and other relevant data, AI can forecast the need for medical supplies, medications, and equipment. This predictive capability ensures that hospitals maintain optimal stock levels, reducing wastage and ensuring the availability of critical supplies when needed.
• Facility management: AI also contributes to the efficient management of hospital facilities. For example, AI-powered systems can control heating, ventilation, and air conditioning (HVAC) systems more efficiently, reducing energy costs while maintaining a comfortable environment for patients and staff [ 62 ]. Additionally, AI can help in the predictive maintenance of hospital equipment, identifying potential issues before they lead to breakdowns, thus minimizing downtime and repair costs [ 63 ].
• Resource allocation: One of the most substantial applications of AI in hospital management is in the optimization of resource allocation [ 64 ]. AI algorithms can analyze complex datasets, including patient admissions, staff availability, and operational capacities, to optimize the allocation of human and material resources. This includes scheduling surgeries and medical procedures in a manner that maximizes the utilization of operating rooms and medical staff, while minimizing patient wait times [ 65 ].
• Supply chain optimization: AI enhances supply chain operations in hospitals by analyzing trends and automating ordering processes [ 66 , 67 ]. It can anticipate supply chain disruptions and suggest alternative solutions, ensuring that the hospital’s operations are not affected by external supply chain challenges. In emergency situations or during health crises, AI systems play a crucial role in managing logistics and resources [ 68 ]. They can quickly analyze the situation, predict the resources required, and assist in the efficient distribution of these resources where they are needed most.

In conclusion, AI’s role in hospital logistics and resource management is multifaceted and profoundly impactful. By automating and optimizing these critical aspects, AI can bring about operational efficiencies and enhance the overall quality of patient care. As AI technology continues to advance, its potential to further revolutionize hospital operations and management is vast, opening new avenues for innovation in healthcare delivery.

3.2. Automating Administrative Tasks with AI

This subsection examines how AI is being utilized to streamline administrative processes, thereby reducing the workload on healthcare staff and improving overall service delivery:

• Patient data management: AI plays an important role in managing vast amounts of patient data [ 69 ]. AI systems can organize, categorize, and process patient records, appointments, and treatment histories with high efficiency and accuracy. These systems can also extract relevant information from unstructured data, such as doctor’s notes, making it easier for healthcare providers to access and analyze patient information. For example, a study utilized AI and natural language processing (NLP) to analyze electronic medical records (EMRs), focusing on uncoded consultation notes for disease prediction [ 70 ]. Techniques like bag of words and topic modeling were applied, along with a method to match notes with a medical ontology. This approach was particularly tested for colorectal cancer. The study found that the ontology-based method significantly enhanced predictive performance, with an AUC of 0.870, surpassing traditional benchmarks. This highlights AI’s potential in extracting useful information from EMR’s unstructured data, improving disease prediction accuracy.
• Billing and claims processing: AI algorithms can also be used to automate billing and insurance claims processing. They can quickly analyze and process claims data, identify errors or inconsistencies, and ensure that billing is accurate and compliant with relevant regulations [ 71 ]. This not only speeds up the reimbursement process but also reduces the likelihood of billing errors, leading to improved financial operations and patient satisfaction. For example, a study in the insurance sector utilized machine learning to improve loss reserve estimation accuracy, crucial for financial statements [ 72 ]. Moving away from traditional macro-level models, this approach used individual claims data, integrating details about policies, policyholders, and claims. The method addressed the challenge of right-censored variables by creating tailored datasets for training and evaluating the algorithms. Compared to the conventional chain ladder method, this AI-driven approach showed notable improvements in accuracy, evidenced by a real case study with a Dutch loan insurance portfolio.
• Scheduling appointments: AI-driven scheduling systems are revolutionizing the way appointments are managed in healthcare settings [ 73 ]. These systems can analyze patterns in appointment bookings and cancellations to optimize the scheduling of patients. By predicting peak times and adjusting appointments accordingly, AI helps in reducing wait times and improving patient flow. For example, a project aimed at reducing outpatient MRI no-shows effectively utilized AI predictive analytics [ 74 ]. In this quality improvement initiative, over 32,000 anonymized outpatient MRI appointment records were analyzed using machine learning techniques, specifically an XGBoost model, a decision tree-based ensemble algorithm. This approach achieved notable results; the model’s predictive accuracy was demonstrated by an ROC AUC of 0.746 and an optimized F1 score of 0.708. When implemented alongside a practical intervention of telephone call reminders for patients identified as high-risk for no-shows, the no-show rate decreased from 19.3% to 15.9% over six months. In another study, a data-driven approach was used to optimize appointment scheduling and sequencing, especially in environments with uncertain service durations and customer punctuality [ 75 ]. Leveraging a novel method based on infinite-server queues, the study developed scalable solutions suitable for complex systems with numerous jobs and servers. Tested using a comprehensive dataset from a cancer center’s infusion unit, this approach significantly improved operational efficiency. The results showed a consistent reduction in costs—combining waiting times and overtime—by 15% to 40%, demonstrating the effectiveness of AI-based strategies in optimizing appointment scheduling.
• Document management and processing: AI technologies are adept at automating the processing of various documents, including consent forms, admission forms, and medical reports [ 76 ]. By using natural language processing (NLP) and machine learning, AI can quickly parse through documents, extract relevant information, and categorize them appropriately. This automation reduces the administrative burden on staff and speeds up document processing.
• Automated communication and reminders: A notable application of AI in healthcare is the optimization of information extraction from electronic health records (EHRs), particularly from scanned documents. A study demonstrated this by successfully extracting sleep apnea indicators from scanned sleep study reports using a combination of image preprocessing techniques and natural language processing (NLP) [ 77 ]. By employing methods like gray-scaling and OCR with Tesseract, followed by analysis through advanced models like ClinicalBERT, the study achieved high accuracy rates (over 90%) in identifying key health metrics.

An example of this application is seen in the ChronologyMD project [ 78 ], which utilized AI to improve eHealth communication programs. The project addressed major deficiencies in existing eHealth communication strategies, which often failed to fully engage audiences and sometimes even negatively impacted the delivery of crucial health information. By strategically employing AI, the ChronologyMD project succeeded in making health communication more engaging, relevant, and actionable. Additionally, it led to increased exposure to relevant messages, reduced the workload of healthcare staff, and improved the overall efficiency of the program while minimizing costs.

Building on this, recent research has explored the role of AI in ensuring compliance with the General Data Protection Regulation (GDPR), crucial for data controllers [ 80 ]. This study aimed to bridge gaps in compliance checking through a two-pronged approach: firstly, by conceptualizing a framework for document-centric compliance checking in the data supply chain, and secondly, by developing methods to automate the compliance checking of privacy policies. The study tested a two-module system, where the first module uses natural language processing (NLP) to extract data practices from privacy policies, and the second module encodes GDPR rules to ensure the inclusion of all mandatory information. The results demonstrated that this text-to-text approach was more effective than local classifiers, capable of extracting both broad and specific information with a single model. The system’s effectiveness was validated on a dataset of 30 privacy policies, annotated by legal experts.

In summary, automating administrative tasks with AI significantly enhances the efficiency and accuracy of hospital operations. It allows healthcare professionals to focus more on patient care rather than administrative duties, leading to improved healthcare delivery. As AI technology continues to evolve, it could progress from automating tasks to personalizing patient interactions through emotional intelligence and cultural awareness, ultimately aiming to provide a more holistic and supportive care experience.

3.3. AI in Patient Flow and Scheduling Optimization

The effective management of patient flow and scheduling is a critical component of hospital operations, impacting both patient satisfaction and healthcare delivery efficiency. The integration of AI in this domain has shown significant promise in optimizing these processes:

• Optimizing patient flow: AI algorithms are particularly adept at analyzing patterns in patient admissions, discharges, and transfers, enabling more efficient patient flow throughout the hospital [ 65 , 81 ]. By predicting high-demand periods, AI can assist in preemptively allocating resources such as beds, staff, and equipment to meet patient needs. For instance, AI systems can forecast daily or seasonal fluctuations in patient admissions, allowing hospitals to adjust staffing levels and bed availability accordingly [ 82 ]. This proactive approach reduces bottlenecks, minimizes wait times, and enhances the overall patient experience.

In a study aimed at improving outpatient department efficiency and patient satisfaction, researchers developed an innovative appointment scheduling system based on a Markov decision process model, incorporating patient preferences to maximize satisfaction [ 84 ]. Adaptive dynamic programming algorithms were utilized to overcome the complexity of scheduling, dynamically adjusting to patient preferences and continuously improving appointment decisions. The system’s performance was evaluated through various experiments, which demonstrated optimal convergence behavior and accuracy.

Utilizing machine learning algorithms, a recent study predicted patient waiting times before consultation and throughput time in an outpatient clinic, aiming to enhance patient satisfaction by providing more accurate wait time information [ 87 ]. The study employed random forest and XGBoost algorithms, analyzing input variables such as gender, day and time of visit, and consultation session. The study achieved high accuracy (86–93%) in predicting wait and throughput times in an outpatient clinic using machine learning models with novel input variables.

In a recent study, a machine learning model was developed to predict patient responses to the “Doctor Communications” domain of the Hospital Consumer Assessment of Healthcare Providers and Systems survey, using data from a tertiary care hospital (2016–2020) [ 89 ]. The random forest algorithm effectively predicted patient responses about doctors’ courtesy, explanation clarity, and attentiveness. The model achieved an AUC of 88% for these doctor communication survey questions.

• Integrating with telehealth: In the era of digital health, AI in scheduling extends beyond in-person appointments to include telehealth services. AI systems can effectively schedule and manage virtual consultations, ensuring that patients receive timely care without the need to physically visit the healthcare facility, which is particularly beneficial for routine follow-ups or during health crises like pandemics [ 90 ].

In conclusion, AI’s role in optimizing patient flow and scheduling in hospitals and clinics is profoundly transformative, offering significant enhancements in operational efficiency, reduced waiting times, and improved patient experiences. As an important element in modernizing healthcare delivery, AI-driven optimization strategies are increasingly crucial. Looking to the future, AI technology is poised for further evolution, with potential advances including real-time adaptive scheduling algorithms, deeper integration with electronic health records for more personalized patient care, and the use of predictive analytics for anticipating patient demand and resource allocation.

4. AI in Medical Imaging and Diagnostics

The integration of AI into medical imaging and diagnostics marks a transformative development in healthcare. This section examines how AI is reshaping the fields of radiology and pathology, bringing unprecedented levels of accuracy and efficiency. We will explore AI’s expanding role in enhancing diagnostic processes and review specific examples of AI systems in imaging technologies such as MRI and CT scans.

4.1. AI’s Role in Radiology and Pathology

AI’s impact on radiology and pathology has been profound, revolutionizing the way medical images are analyzed and interpreted.

In radiology, AI algorithms, particularly those based on deep learning, are increasingly being used to analyze radiographic images. These AI models are trained on vast datasets of X-rays [ 91 ], MRIs [ 92 ], CT scans [ 93 ], and other imaging modalities [ 94 ], enabling them to detect abnormalities such as tumors, fractures, and signs of diseases like pneumonia or brain bleeds with high precision. In many cases, AI can highlight subtle findings that may be overlooked by the human eye, thus serving as an invaluable tool for radiologists. For example, a recent study introduced an anatomy-aware graph convolutional network (AGN) tailored for mammogram mass detection, enabling multi-view reasoning akin to radiologists’ natural ability [ 95 ]. This AGN, significantly outperforming current methods on benchmarks, involves modeling relations in ipsilateral and bilateral mammogram views, and its visualization results offer interpretable cues crucial for clinical diagnosis.

AI in radiology is not only about detecting abnormalities; it also helps in quantifying disease progression [ 96 ], assessing response to treatment [ 97 ], and predicting patient outcomes [ 98 ]. For example, in cancer treatment, AI can measure the size and growth of tumors over time, providing crucial information for treatment planning [ 99 ].

The field of pathology has also seen significant advancements with the integration of AI [ 100 ]. Digital pathology, where slides are scanned and analyzed by AI algorithms, has enabled more accurate and faster diagnosis of diseases. AI excels in pattern recognition, which is essential in identifying markers of diseases in tissue samples. This is particularly impactful in the diagnosis of cancers, where AI can assist pathologists in spotting cancerous cells, often with greater accuracy and speed than traditional methods. As an example, deep learning neural networks have significantly advanced molecular diagnostics in clinical oncology, leading to a new era in digital pathology and precision medicine [ 101 ]. This advancement holds significant promise particularly for resource-limited settings. For example, in India, an AI-powered software has been used to analyze key molecular markers in endoscopic images, enabling more precise diagnoses of gastric cancer, potentially paving the way for personalized treatment approaches [ 102 ].

AI’s contribution to pathology extends beyond disease detection. It also includes predicting disease aggressiveness and patient prognosis, helping pathologists make more informed decisions about patient care. For example, an AI model utilizing MRI scans accurately predicts the aggressiveness of soft tissue sarcomas with an average accuracy of 84.3% and sensitivity of 73.3%, providing valuable insights as a second expert opinion for clinicians prior to biopsy, presenting a novel approach for rare pathology diagnosis [ 103 ].

In summary, AI’s role in radiology and pathology is transformative, offering advanced diagnostic capabilities. However, this progress invites critical considerations, such as the need for ongoing training for medical professionals to effectively integrate AI tools, and continuous evaluation of AI systems to ensure they complement rather than replace human expertise. Future advancements should aim to harmonize AI technology with clinical practice, ensuring it remains a supportive tool that enhances, rather than overshadows, the critical role of medical professionals.

4.2. Enhancing Accuracy and Efficiency in Diagnostic Processes

The incorporation of AI into diagnostic processes is a game-changer in healthcare, notably enhancing both accuracy and efficiency. This subsection considers the various ways in which AI is achieving these improvements and the impact it has on the overall diagnostic workflow:

• Improving diagnostic accuracy: AI algorithms, particularly those based on deep learning, have demonstrated remarkable accuracy in diagnosing diseases from medical images and test results. These systems are trained on vast datasets, allowing them to recognize patterns and anomalies that might be imperceptible to the human eye. For example, in dermatology, AI systems trained on images of skin lesions have shown the ability to detect skin cancers, such as melanoma, with a level of precision comparable to that of experienced dermatologists [ 104 ].
• Reducing diagnostic errors: One of the key benefits of AI in diagnostics is its potential to reduce errors [ 105 ]. Misdiagnosis and missed diagnoses are significant concerns in medicine, often leading to delayed or inappropriate treatment. AI systems provide a level of consistency and attention to detail that is challenging for humans to maintain over long periods, thus reducing the likelihood of such errors.
• Speeding up diagnostic processes: AI significantly speeds up the diagnostic process. Analyzing medical images or test results, tasks that would take a healthcare professional considerable time, can be performed by AI in a fraction of the time. This rapid analysis is particularly beneficial in urgent care situations, where quick decision-making is critical. For instance, AI algorithms can quickly analyze CT scans of stroke patients to identify blockages or bleeding in the brain, enabling faster initiation of life-saving treatments [ 106 ].
• Automated reporting and documentation: AI not only automates reporting and documentation in diagnostic processes [ 107 ] but also enhances the quality of these processes. While AI systems generate preliminary reports from image analysis for radiologist review, streamlining workflow and reducing administrative burden, a recent study has furthered this efficiency by consolidating existing ML reporting guidelines [ 108 ]. This study, after an extensive review of 192 articles and expert feedback, created a comprehensive checklist encompassing 37 reporting items for prognostic and diagnostic ML studies. This effort in standardizing ML reporting is pivotal in improving the quality and reproducibility of ML modeling studies, complementing AI’s role in simplifying diagnostic reporting.

As an example, a scoping review focused on AI techniques for fusing multimodal medical data, particularly EHR with medical imaging, to develop AI methods for various clinical applications [ 110 ]. The review analyzed 34 studies, observing a workflow of combining raw data using ML or DL algorithms for clinical outcome predictions. It found that multimodality fusion models generally outperform single-modality models, with early fusion being the most commonly used technique. Neurological disorders were the dominant category studied, and conventional ML models were more frequently used than DL models. This review provides insights into the current state of multimodal medical data fusion in healthcare research.

In conclusion, AI’s significant role in improving diagnostic accuracy and efficiency is transforming healthcare, delivering faster and more precise diagnoses. However, a critical concern is that these AI systems are often primarily designed for specific groups, which can lead to disparities in healthcare. Future advancements should emphasize the development of more inclusive AI models that cater to a broader patient demographic, ensuring equitable healthcare improvements across all populations.

4.3. The Role of Hardware Acceleration in AI-Powered Diagnostics

The previous sections explored how AI is revolutionizing medical imaging and diagnostics by enhancing accuracy and efficiency. However, this transformation hinges on the immense processing power required to analyze large medical datasets of X-rays, MRIs, and CT scans, along with the complex AI algorithms used for tasks like image recognition and disease detection. This is where hardware acceleration steps in, acting as a powerful engine that fuels AI-powered diagnostics [ 111 ].

Hardware accelerators are specialized components within a computer system designed to offload and expedite specific computing tasks typically handled by the main processor (CPU). While CPUs are versatile, they may not always be the most efficient for computationally intensive AI workloads. Hardware accelerators, on the other hand, are optimized for these tasks, offering significant performance boosts.

Several types of hardware accelerators are well suited for AI-powered diagnostics [ 112 ]:

• Graphics Processing Units (GPUs): Originally designed for computer graphics rendering, GPUs excel at parallel processing, making them ideal for handling the massive datasets and complex calculations involved in AI algorithms. In the medical image analysis domain, GPUs can be used to accelerate basic image processing operations such as filtering and interpolation. Additionally, GPUs can enhance the operation of different AI algorithms used in medical imaging tasks like image registration, image segmentation, image denoising, and image classification [ 113 ].
• Tensor Processing Units (TPUs): Custom-designed chips like TPUs, pioneered by companies like Google, are specifically optimized for high-performance deep learning inference, a key technique used in medical image analysis. TPUs offer significant speed advantages over CPUs for tasks like image recognition and classification. For example, researchers implemented a system for glaucoma diagnosis using both edge TPUs and embedded GPUs [ 114 ]. While both achieved fast image segmentation and classification for real-time diagnosis support, the study found that TPUs consumed significantly less energy compared to GPUs. This makes TPUs a more attractive option for battery-powered medical devices used in edge computing scenarios.
• Field-Programmable Gate Arrays (FPGAs): These versatile chips offer flexibility for hardware customization. Unlike pre-designed GPUs and TPUs, FPGAs can be programmed to perform specific AI algorithms, potentially leading to highly optimized solutions for certain diagnostic tasks. However, programming FPGAs requires specialized expertise. For instance, researchers have proposed a MobileNet accelerator designed specifically for FPGAs that focuses on minimizing on-chip memory usage and data transfer, making it ideal for low-power devices [ 115 ]. They achieve this by using two configurable modules for different convolution operations and a new cache usage method. Their implementation demonstrates real-time processing with low memory usage, making FPGAs a viable option for running efficient CNNs in auxiliary medical tasks on portable devices.
• Application-Specific Integrated Circuits (ASICs): When dealing with a well-defined AI algorithm in a specific diagnostic application, ASICs can be designed to offer the ultimate performance [ 116 ]. Engineered for a single task, ASICs provide unparalleled efficiency and processing speed for that specific function. However, the lack of flexibility limits their application to well-established and unchanging algorithms.

By leveraging hardware acceleration, AI-powered diagnostics can achieve several benefits: faster processing for near-real-time analysis of medical images, leading to quicker and potentially life-saving interventions; improved accuracy through the ability to perform intricate image analysis, potentially leading to a higher degree of disease detection; and enhanced efficiency by streamlining the diagnostic process, allowing radiologists and clinicians to analyze more images in a shorter timeframe.

It is important to note that these benefits extend beyond medical imaging, with hardware acceleration playing a crucial role in other AI health tasks such as analyzing genetic data for personalized medicine or processing real-time sensor data from wearable devices for remote patient monitoring [ 117 ].

4.4. Examples of AI Systems Used in Imaging

AI has made significant contributions in the field of medical imaging, with various AI systems being developed and used for analyzing images from MRI, CT scans, and other modalities. This subsection highlights some notable examples of these AI systems, showcasing their capabilities and the impact they have on diagnostic imaging. An overview of AI applications in medical imaging is also presented in Table 3 .

Overview of AI applications in medical imaging.

An example of AI application in MRI is an AI system developed for detecting brain abnormalities [ 118 ]. This system uses a deep CNN to analyze MRI images and can identify conditions such as tumors, strokes, and neurodegenerative diseases. The AI not only detects these abnormalities but also helps in quantifying the volume of affected areas, which is vital for treatment planning and monitoring disease progression. Another example is the application of AI in the interpretation of breast cancer. CNNs are employed to extract features from MRI breast scans, and alongside classifiers, they effectively detect the presence of cancer, showcasing the potential of AI in enhancing diagnostic accuracy in breast cancer detection [ 124 ].

AI systems are increasingly used for the automated segmentation of images in radiology [ 125 ]. These systems can differentiate and label various anatomical structures in the images, such as organs and tissues, aiding radiologists in diagnosis and in planning surgeries or treatments. For example, a study introduced a 4D deep learning model, combining 3D convolution and LSTM, for the precise segmentation of hepatocellular carcinoma (HCC) lesions in dynamic contrast-enhanced MRI images [ 126 ]. Utilizing both spatial and temporal domain information from multi-phase images, the model significantly improved liver tumor segmentation performance, achieving superior metrics compared to existing models and offering a comparable performance to the state-of-the-art nnU-Net model with reduced prediction time.

AI is also being adapted for pediatric imaging, addressing the unique challenges presented by the varying sizes and developmental stages of pediatric patients [ 127 ]. AI systems in this domain are tailored to recognize and interpret patterns specific to children, aiding in the diagnosis of congenital and developmental conditions. For instance, in pediatric imaging for focal epilepsy, a deep CNN model was introduced, excelling in tract classification and identifying critical white matter pathways with 98% accuracy [ 128 ]. This model effectively predicted surgical outcomes and postoperative language changes, showcasing its potential to enhance preoperative evaluations and improve surgical precision in children.

• AI for CT scan interpretation: AI applications in CT scan interpretation span detecting lung nodules, identifying fractures and hemorrhages, assessing stroke severity, and characterizing tumor progression. One innovative AI application in CT imaging is in the rapid identification of pulmonary embolisms [ 119 ]. The AI system processes CT pulmonary angiograms to detect blood clots in the lungs with high accuracy, often faster than traditional methods. This speed is critical in emergency situations, where timely intervention can be life-saving. As another example, Google’s AI, in collaboration with researchers from Northwestern University, NYU-Langone Medical Center, and Stanford Medicine, has developed a CT scan model that diagnoses lung cancer with accuracy equal to or surpassing six radiologists [ 129 ]. This model analyzes 3D volumetric scans to predict malignancy and detect subtle lung nodules, viewing the lungs as a single 3D object and comparing scans over time to track lesion growth. Tested on over 45,800 de-identified chest CT screenings, it detected 5% more cancer cases and reduced false positives by over 11% compared to traditional radiologist evaluations, demonstrating significant potential for enhancing lung cancer diagnosis.
• AI in X-ray analysis: AI is revolutionizing X-ray analysis across various medical fields. Take mammography, for instance, AI is transforming breast cancer screening by enhancing image analysis for tumor detection, improving accuracy in identifying benign and malignant lesions, and reducing false positives and negatives, thereby streamlining the diagnostic process for early and effective treatment [ 130 ]. These systems analyze mammograms to identify signs of cancerous lesions, with some AI models demonstrating the ability to detect cancers that were initially missed by radiologists. By serving as a second reviewer, these AI systems enhance the accuracy of breast cancer screening. A recent study demonstrated that cmAssist™, an AI-based CAD algorithm based on multiple custom deep learning-based networks, significantly enhanced radiologists’ sensitivity in breast cancer detection [ 120 ]. Analyzing 122 mammograms with a blend of false negatives and BIRADS 1 and 2 ratings, radiologists showed a notable improvement in cancer detection rates (CDRs) by an average of 27% when using cmAssist, with a minimal increase in false positives. This marked improvement underscores the potential of AI-CAD software in improving accuracy and sensitivity in breast cancer screening.
• AI in ultrasound: AI is significantly impacting various applications of ultrasound. In cardiac imaging, for example, AI systems are used to analyze images from echocardiography scans to assess cardiovascular function [ 131 ]. They can measure parameters such as the ejection fraction, which indicates how well the heart is pumping blood, and detect structural abnormalities of the heart. This information is crucial in diagnosing and managing heart diseases. For example, a study evaluating a novel AI for automated left ventricular ejection time calculation in echocardiography showed high accuracy, closely correlating with cardiac MRI results [ 121 ]. The AI, which demonstrated lower bias and greater reliability especially in challenging cases, outperformed conventional methods. This algorithm is based on a patented CNN, though specific details of its architecture and training process remain proprietary. This underscores the algorithm’s potential in reducing user-dependent variability and enhancing the clinical utility of echocardiography.

In conclusion, these examples illustrate the diverse and impactful applications of AI in medical imaging. By enhancing the accuracy, speed, and efficiency of image analysis, AI systems are proving to be invaluable assets in diagnostic radiology, ultimately leading to better patient care and outcomes. As AI technology continues to advance, its applications in medical imaging are expected to broaden, further transforming the field of radiology.

5. AI in Patient Care and Monitoring

The rise of AI in healthcare marks a paradigm shift, promising a future of more efficient and effective patient care and monitoring. This section explores how AI is enhancing patient care through innovative technologies and personalized approaches. The focus is on three key areas: AI-powered wearable devices for continuous monitoring, the impact of virtual nursing assistants, and AI’s role in telemedicine and remote patient engagement. These applications of AI are transforming the way patient care is administered and are empowering patients with more control over their health and wellness. Table 4 presents a summary of AI powered technologies for patient care and monitoring covered in this section. These topics are further discussed in the following:

AI-powered technologies for patient care and monitoring.

5.1. AI-Powered Wearable Devices for Continuous Monitoring

AI-powered wearables mark a breakthrough in patient monitoring, blending convenience with real-time analysis of vital signs like heart rate, blood pressure, blood glucose, and oxygen saturation. They can also capture additional physiological data like electroencephalography (EEG), electrical activity of the heart (electrocardiography, ECG), and peripheral physiological signals like photoplethysmography (PPG), providing a more comprehensive picture of a patient’s health. Especially valuable for managing chronic conditions, these devices provide timely alerts for crucial interventions, such as notifying diabetic patients of blood sugar levels to prevent critical episodes [ 132 ].

One of the most impactful aspects of these wearables is their ability to analyze collected data and predict potential health issues before they become serious. Utilizing AI algorithms, these devices can detect patterns or anomalies in health data indicative of emerging problems. For instance, wearables can analyze heart rate variability [ 133 ], other cardiac markers [ 134 ], and sleep patterns [ 135 ] to predict the risk of heart conditions and sleep disorders, facilitating early preventive measures. For example, a novel deep learning framework based on a hybrid CNN-LSTM model forecasts sleep apnea occurrence from single-lead ECG with an accuracy of up to 94.95% when validated on 70 sleep recordings [ 135 ]. This approach utilizes ECG R-peak amplitudes and R-R intervals, making it suitable for wearable sleep monitors to manage sleep apnea effectively.

AI-powered wearables significantly enhance patient engagement by offering insights into health metrics and progress, encouraging active health management [ 136 ]. These devices, often paired with companion apps, provide personalized recommendations for lifestyle changes, medication adherence, and exercise based on the patient’s health data [ 137 ]. Additionally, they are increasingly being used for sleep monitoring, offering valuable data on sleep patterns and quality [ 138 ]. This feature aids in identifying sleep-related issues, allowing for targeted interventions that can improve overall well-being and health management.

While AI-powered wearables hold promise for revolutionizing patient care, they face specific challenges from data collection to model deployment [ 139 ]. Collecting sufficient, reliable data for training, especially in healthcare, is difficult due to high costs and the complexity of ensuring data reliability. Selecting the most effective features and frameworks and evaluating and deploying the best ML models add layers of complexity, compounded by the necessity for models to generalize well across diverse personal features. Wearable device developers must also navigate the selection of deployment options, balancing the advantages of on-device computing against the limitations of power consumption, storage, and computational power. Addressing these challenges involves a careful trade-off between model accuracy and the practical constraints of wearable technology, requiring innovations in model design, data processing, and system integration to optimize the clinical impact and user acceptance of wearable ML applications.

5.2. Virtual Nursing Assistants

Virtual nursing assistants, powered by AI, are transforming healthcare by offering continuous patient support and enhancing the efficiency of healthcare services [ 140 ]. These systems provide round-the-clock assistance, including health-related queries, medication reminders, and appointment scheduling, thereby supporting both patients and healthcare professionals. For example, AI-driven voice technology, through chatbots on mobile phones and smart speakers, enhances patient management and healthcare workflow, offering solutions for acute care triaging, chronic disease management, and telehealth services, particularly noted during the COVID-19 pandemic [ 141 ].

AI systems enhance patient engagement and education through personalized interactions, improving compliance with treatment plans and encouraging healthier lifestyle choices. A recent study in the Greater Toronto area on patient engagement in AI healthcare development educated diverse participants on AI before gathering their perspectives. The results indicated a strong desire for early and diverse patient involvement in AI development stages, emphasizing the critical role of patient education for meaningful engagement [ 142 ].

Additionally, they monitor health status and symptoms for those with chronic conditions, alerting healthcare providers when necessary to prevent complications and reduce hospital readmissions [ 143 ]. Virtual nursing assistants also collect and analyze patient data, offering insights into patient behavior and healthcare trends [ 144 ].

Despite their benefits, challenges such as data privacy, information accuracy, and ensuring they complement human care remain. With ongoing advancements in AI, virtual nursing assistants are expected to become more enhanced, promising a future of accessible, personalized, and efficient healthcare.

5.3. AI in Telemedicine and Remote Patient Engagement

The integration of AI into telemedicine and remote patient engagement is revolutionizing healthcare accessibility and effectiveness [ 145 ]. AI is enhancing telehealth platforms with advanced diagnostic and consultation services, enabling healthcare providers to diagnose patients remotely and personalize virtual consultations based on patient data [ 146 ]. AI-powered chatbots and virtual assistants facilitate patient interaction, offering support and streamlining the appointment process [ 147 ], while AI’s role in remote patient monitoring and predictive analytics supports proactive care for chronic conditions and anticipates potential health issues. For example, a study developed and evaluated PROSCA, an AI-based medical chatbot for prostate cancer education, involving ten men with suspicion of prostate cancer [ 148 ]. The chatbot effectively increased prostate cancer knowledge among 89% of its users, with all participants expressing a willingness to reuse and support chatbots in clinical settings, highlighting its potential in enhancing patient education and doctor–patient communication.

While AI integration into telemedicine offers enhanced capabilities for remote healthcare delivery, challenges including data privacy, system accuracy, and seamless healthcare system integration persist [ 149 ]. Despite these obstacles, AI’s incorporation into telemedicine remains crucial and offers a more accessible, personalized, and proactive healthcare future, where technology effectively narrows the distance between patients and providers, supported by physician-guided implementation and adherence to clinical practices.

6. Methodologies for Assessing AI Healthcare Solutions

Evaluating AI-based healthcare solutions requires a comprehensive approach that considers various aspects of performance, effectiveness, safety, and ethical considerations. In this section, we explore the methodologies employed to assess the viability and impact of AI technologies within healthcare settings.

6.1. Validation

Validation encompasses multiple stages, each crucial for ensuring the reliability and effectiveness of AI algorithms in healthcare, as elaborated below:

• Algorithm validation: The successful integration of AI algorithms into healthcare hinges on their accuracy, reliability, and performance. This necessitates comprehensive testing using diverse datasets [ 150 ]. A critical challenge in this process is overfitting, where the algorithm performs well on the training data but fails to generalize to unseen data. To address this, techniques like cross-validation are employed [ 151 ]. Cross-validation involves splitting the training data into multiple folds and iteratively training the algorithm on a subset of folds while using the remaining folds for validation. This process helps assess how well the algorithm generalizes to new data and prevents overfitting. Beyond generalizability, AI in healthcare should be adaptable for personalized use. This means the algorithms should continuously learn from individual patient data to enable tailored treatment approaches. Rigorous assessment helps identify strengths, weaknesses, and areas for improvement, ultimately enhancing the reliability of AI-based healthcare solutions. Furthermore, validation on different patient groups is essential to address potential biases in the training data. Biases can lead to unfair and ineffective outcomes for certain demographics. By ensuring the algorithms perform consistently across diverse populations, we can ensure fairness and effectiveness for all.
• Clinical validation: Clinical validation plays a crucial role in assessing the efficacy and safety of AI interventions [ 152 ]. Rigorous clinical trials and studies should be conducted to compare AI-based interventions with standard treatments or existing practices. These evaluations can encompass a range of study designs, including randomized controlled trials (RCTs), observational studies, or real-world evidence analyses. Through these studies, researchers can determine the effectiveness of AI technologies in improving patient outcomes and clinical decision-making. Furthermore, defining appropriate outcome measures is essential for assessing the impact of AI interventions on patient outcomes. Outcome measures such as mortality rates, disease progression, quality of life, and healthcare costs can be used to evaluate the effectiveness of AI technologies in improving healthcare delivery.

6.2. Interpretability and Usability

To earn trust and acceptance within the healthcare system, AI technologies must be interpretable, usable, and ethically sound. Interpretability ensures that AI models provide clear explanations for their decisions, fostering trust with clinicians who can understand the reasoning behind recommendations [ 153 ]. Usability focuses on the seamless integration of AI tools into existing workflows for all stakeholders. User-centered design principles, with active involvement from clinicians and patients throughout development, are crucial not only for usability but also for user engagement. This collaborative approach fosters a sense of ownership and trust in the AI solution, ultimately driving successful adoption and improved patient outcomes.

Furthermore, interpretability extends beyond simply understanding the “why” behind an AI decision. Explainability techniques like feature importance analysis, LIME (Local Interpretable Model-agnostic Explanations) [ 154 ], and SHAP (SHapley Additive exPlanations) [ 155 ] values can provide deeper insights into the model’s reasoning.

While interpretability and usability are crucial for the initial acceptance of AI solutions, user engagement plays a vital role in driving long-term trust and successful adoption [ 156 ]. User engagement refers to the ongoing interaction and positive user experience with the AI tool. User-centered design principles can promote engagement as follows:

• Active stakeholder involvement: Throughout the development process, actively involving clinicians, patients, and other stakeholders provides valuable insights into their needs and expectations. This collaborative approach fosters a sense of ownership in the solution, leading to higher engagement.
• Iterative development and feedback loops: Developing AI solutions is an iterative process. By incorporating user feedback throughout development cycles, researchers can refine the AI tool to better address user needs. This ongoing feedback loop not only improves usability but also strengthens user confidence and engagement.
• User-friendly interfaces and clear visualizations: Designing clear and user-friendly interfaces is essential for user engagement. This includes presenting AI outputs in a way that is easy to understand and interpret, even for users with limited technical expertise. Additionally, providing clear visualizations of the AI’s reasoning can further enhance user trust and engagement.

6.3. Scalability and Continuous Improvement

Scalability refers to the ability of AI models to adapt and perform effectively across diverse healthcare settings, patient populations, and clinical scenarios [ 157 ]. An AI model trained in a large academic hospital, to be truly impactful, needs to adapt and deliver accurate results in smaller clinics with different patient populations and clinical scenarios. Scalability ensures AI solutions can be implemented and benefit a wider range of healthcare providers and patients.

Continuous improvement involves implementing mechanisms for ongoing monitoring, feedback collection, and iterative enhancement of AI solutions over time. This may include the following:

• Post-market surveillance: Closely monitoring the performance of AI solutions after deployment in real-world settings to identify any unforeseen issues or areas for improvement [ 158 ].
• Performance monitoring: Continuously tracking the effectiveness of the AI tool in achieving its intended outcomes [ 159 ]. These data can be used to identify areas where the AI can be further optimized.
• Updating algorithms based on new data and insights: AI algorithms are not static. As new data become available, or as researchers gain a deeper understanding of the underlying problem, the algorithms can be updated to improve their performance and accuracy.

By prioritizing scalability and continuous improvement, researchers and developers should ensure the long-term success and sustainability of AI-based healthcare solutions in addressing evolving healthcare challenges.

7. Ethical Considerations and Challenges

As AI continues to enhance the healthcare sector, it brings significant ethical considerations and challenges. This section explores the complex ethical landscape surrounding the use of AI in healthcare. We will explore the implications of AI on privacy, consent, and bias, scrutinize the practical challenges in its integration, such as data security and interoperability, and discuss the evolving regulatory and compliance landscape. The integration of AI into healthcare raises fundamental questions about patient rights, data stewardship, and the equitable delivery of care, demanding a thoughtful and refined approach to its deployment. Figure 2 navigates the ethical considerations and challenges in healthcare AI. These topics are further discussed in the following sections:

Navigating ethical considerations and challenges in healthcare AI.

7.1. Ethical Implications of AI in Healthcare

The ethical implications of AI in healthcare include various possibilities, including the following:

• Privacy concerns: One of the foremost ethical concerns in AI healthcare is the privacy of patient data. AI systems require access to large datasets of patient information, which raises questions about the security and confidentiality of sensitive health data [ 160 ]. Ensuring that patient data used for AI applications are anonymized and securely stored is paramount. There is also a need for transparent policies regarding who has access to these data and for what purposes.
• Informed consent: The issue of informed consent in AI healthcare is complex, necessitating clear communication with patients about the use of their data, especially with AI algorithms that may be challenging for non-experts to grasp. This includes detailing data sharing implications, potential benefits and risks associated with AI-driven healthcare, and the level of human oversight in AI decisions. More details on the use of informed consent forms for AI in medicine with a comprehensive guideline for emergency physicians can be found in [ 161 ].
• Bias and fairness: AI systems are only as unbiased as the data they are trained on. There is a risk that AI algorithms may perpetuate existing biases present in healthcare data, leading to unfair treatment outcomes for certain groups [ 162 ]. For example, if an AI system is trained predominantly on data from a specific demographic, its accuracy might be lower for patients outside of that demographic. Ensuring that AI systems are developed and trained on diverse datasets is crucial to mitigate these biases. Moreover, the continuous monitoring and auditing of AI systems for biased outcomes are necessary to uphold fairness in healthcare delivery.
• Transparency and accountability: Transparency in AI decision-making processes is a key ethical concern [ 163 ]. It is important for healthcare providers and patients to understand how AI systems make their recommendations. This transparency is essential for building trust in AI systems and for accountability [ 164 ]. In cases where AI-driven decisions impact patient care, it is crucial to have mechanisms in place to review and understand these decisions, particularly in the event of adverse outcomes. A recent study highlights the need for transparent and accountable AI systems in natural NLP to address the “black box” issue of deep learning models [ 165 ]. It introduces the Explaining and Visualizing CNNs for Text Information (EVCT) framework, which offers human-interpretable solutions for text classification with minimal information loss, aligning with recent demands for fairness and transparency in AI-driven decision support systems.

In conclusion, while AI presents significant opportunities for enhancing healthcare, it also introduces complex ethical challenges that must be addressed. Privacy, consent, bias, transparency, and accountability are critical considerations that need to be carefully managed to ensure the responsible and equitable use of AI in healthcare.

7.2. Challenges in Integrating AI

The integration of AI in healthcare systems is not without its challenges. Among the most prominent are issues related to data security and interoperability. These challenges can impede the effective and safe use of AI in healthcare settings, and addressing them is crucial for the successful adoption of AI technologies. Some possible challenges in integrating AI include the following:

• Data security concerns: As healthcare AI systems require access to large volumes of sensitive patient data, ensuring the security of these data is paramount [ 166 ]. The risk of data breaches and cyberattacks poses a significant concern. These security breaches can lead to the exposure of confidential patient information, resulting in privacy violations and potentially harming the trust between patients and healthcare providers. Implementing robust cybersecurity measures, including encryption, secure data storage solutions, and regular security audits, is crucial to protect patient data [ 167 ]. Additionally, educating healthcare staff about data security best practices is essential in safeguarding against breaches.
• Interoperability between systems: Another major challenge in integrating AI into healthcare is the issue of interoperability—the ability of different healthcare IT systems and software applications to communicate, exchange data, and use the information that has been exchanged [ 168 ]. Many healthcare systems use a variety of electronic health record (EHR) systems and other digital tools that may not be compatible with one another or with new AI technologies. This lack of interoperability can hinder the seamless exchange of patient data, reducing the effectiveness of AI tools. Developing standardized data formats and communication protocols, as well as encouraging the adoption of interoperable systems, is vital to overcome this challenge [ 169 ].
• Integration with existing clinical workflows: Integrating AI into existing clinical workflows can be challenging. Healthcare professionals may need to adjust their workflows to accommodate AI tools, which can be a time-consuming and complex process. Ensuring that AI systems are user-friendly and align with current clinical practices is essential to facilitate their adoption. Training and support for healthcare professionals in using these AI systems are also crucial for successful integration. For example, in a recent study, a three-tiered integration approach of AI-based image analysis into radiology workflows is outlined, focusing on enhancing automation and incorporating radiologist feedback for continuous AI improvement [ 170 ]. This approach entails initially visualizing AI outcomes without generating new patient records. It allows for the storage of AI-generated results in institutional systems and equips radiologists with tools to refine AI inferences for periodic retraining. This methodology was exemplified in a case study on brain metastases detection, where radiologist input substantially decreased false positives via iterative retraining with an expanded dataset.
• Data quality and quantity: The effectiveness of AI systems depends heavily on the quality and quantity of the data they are trained on. Inconsistent, incomplete, or inaccurate data can lead to poor AI performance. Ensuring the collection of high-quality, comprehensive patient data is therefore a significant challenge in AI integration [ 171 ]. Standardizing data collection methods and ensuring thorough data curation processes are essential steps in addressing this issue.

7.3. Regulatory and Compliance Issues

The integration of AI into healthcare raises significant regulatory and compliance issues. Navigating this complex landscape is crucial for ensuring that AI applications in healthcare are safe, effective, and ethically sound. This subsection discusses the key regulatory and compliance challenges associated with AI in healthcare.

The regulatory framework for AI in healthcare is still evolving. Different countries and regions have varying standards and guidelines for the use of AI in medical settings [ 172 , 173 ]. For instance, in the United States, the Food and Drug Administration (FDA) is actively working on establishing clear guidelines for AI and machine learning-based medical devices [ 174 ]. Ensuring compliance with these regulations, which are often in a state of flux, is a challenge for AI developers and healthcare providers. Staying up to date with these developments and comprehending their relevance to AI applications is essential.

AI-based systems used in healthcare often require approval from regulatory bodies [ 175 ]. This process can be lengthy and complex, as it involves rigorous testing and validation of the AI models. Proving the safety and efficacy of AI systems to regulatory standards is a significant challenge, especially given the dynamic and evolving nature of AI algorithms. Regulatory bodies are increasingly focusing on the ethical implications of AI, including concerns about privacy, bias, and transparency. Ensuring that AI systems uphold these ethical standards and do not compromise patient safety is a key compliance issue.

Compliance with data protection and privacy laws is another major challenge. Laws such as the General Data Protection Regulation (GDPR) in the European Union and the Health Insurance Portability and Accountability Act (HIPAA) in the United States impose strict requirements on the handling of patient data [ 176 ]. AI systems that process patient data must comply with these laws, which involves implementing robust data protection measures and ensuring that patient data are used in a lawful and transparent manner.

Lastly, and critically, regulatory compliance for AI in healthcare extends beyond a mere initial approval. It demands continuous monitoring and reporting to ensure ongoing adherence to standards. This involves regular audits, necessary updates to AI algorithms to guarantee their correct functioning, and the immediate reporting of any adverse events or discrepancies to regulatory bodies.

8. The Future of AI in Healthcare

The rapid evolution of AI promises a transformative future for healthcare. This final section of this paper looks forward to the emerging trends and potential applications of AI in healthcare, examining how they might shape patient outcomes and the overall delivery of healthcare services. We will also explore the role of AI in responding to global health crises, such as pandemics, and its impact on public health strategies.

Table 5 thoroughly outlines the emerging trends and potential impacts of AI in healthcare. The subsequent sections further investigate and enhance understanding of these trends.

Emerging trends and potential impacts of AI in healthcare.

8.1. Personalized Healthcare Applications

Future research should continue to prioritize personalized healthcare applications. Possible future directions in this domain encompass the following:

• Personalized medicine: One of the most promising trends in AI healthcare is the move towards more personalized medicine [ 177 ]. AI’s ability to analyze vast amounts of genetic, health data, and lifestyle information will enable the development of more precise and effective treatments tailored to individual patient profiles. This personalized approach can improve treatment outcomes and reduce side effects.
• AI-powered tools for health and sleep monitoring: Future research should explore the development and validation of AI-driven tools and algorithms for the diagnosis, monitoring, and management of health issues and sleep disorders [ 178 ]. This includes leveraging machine learning to analyze data from wearable devices such as sleep patterns, heart rate variability, and activity levels. These analyses can, for example, help detect abnormalities such as sleep apnea and personalize treatment recommendations based on individual sleep profiles.
• Longevity and aging: By harnessing the power of predictive analytics, AI can explore vast datasets to uncover biomarkers of aging and offer personalized strategies to slow or even reverse the aging process [ 179 ]. This includes leveraging AI for genomic interventions, where it could guide the editing of genes associated with aging mechanisms, enhancing cellular repair, resilience, and longevity. The potential of AI extends to the field of drug discovery and repurposing, where it can expedite the identification of compounds with anti-aging effects [ 180 ]. Moreover, AI’s integration into healthcare promises a paradigm shift towards preventive medicine, emphasizing early detection and intervention in age-related declines.

8.2. Enhanced Treatment Technologies

Future research should focus on AI-powered technologies for enhancing treatment methodologies. Some potential future directions include the following:

• AI in drug discovery and development: AI is poised to play a significant role in accelerating drug discovery and development [ 181 ]. By rapidly analyzing molecular and clinical data, AI has the potential to identify potential drug candidates much faster than traditional methods. This acceleration could significantly reduce the time and cost associated with bringing new drugs to market.
• Advanced robotics in surgery and rehabilitation: The use of AI-driven robotics in surgery and rehabilitation is expected to advance further [ 182 ]. Robotic systems, guided by AI algorithms, could potentially perform complex surgeries with high precision, reducing risks and improving patient outcomes. In rehabilitation, AI-powered exoskeletons and prosthetics are anticipated to offer greater mobility and independence to patients.
• AI hardware accelerators: As AI applications in healthcare grow, the demand for efficient processing capabilities rises. AI hardware accelerators like GPUs, TPUs, and FPGAs optimize AI model performance, enabling real-time medical data processing with minimal latency. Integrating these accelerators into medical devices promises faster diagnosis, treatment planning, and analysis, thereby enhancing patient care outcomes. Developing dedicated AI hardware accelerators tailored to healthcare needs is a promising future direction for improving the efficiency and accessibility of AI-driven healthcare solutions.
• AI-enhanced medical imaging: Future developments in AI are likely to produce even more advanced medical imaging techniques [ 183 ]. These advancements could provide clearer, more detailed images and enable the earlier detection of diseases, potentially even identifying health risks before symptoms appear.
• Integrating AI with IoT and wearables: The integration of AI with the Internet of Things (IoT) and wearable technology is an emerging trend [ 184 ]. This combination could lead to real-time health monitoring systems that not only track health data but also provide proactive recommendations and alerts. AI can also be integrated into existing wearable technologies to provide further information regarding health and performance [ 185 ].

8.3. Healthcare System Optimization

In guiding future research, emphasis should be placed on healthcare system optimization, which can include the following:

• Enhancing patient outcomes and system efficiency: The transformative potential of AI in healthcare can revolutionize patient care and system efficiency. Future AI applications aim to detect diseases earlier, customize treatments, and significantly personalize patient care, leading to improved recovery times and reduced mortality rates. AI’s role extends to optimizing healthcare resources, reducing costs, and improving care accessibility, especially for underserved communities [ 186 ]. Moreover, AI will support healthcare professionals by augmenting decision-making, promising equitable health improvements and a more efficient healthcare delivery system.
• Global health monitoring systems: The significance of AI in addressing pandemics and global health emergencies is increasingly recognized as crucial [ 187 ]. By integrating and analyzing diverse data streams, AI is adept at quickly detecting the emergence of disease outbreaks, projecting their spread, and guiding effective public health interventions. During the COVID-19 pandemic, AI-powered models were used to predict the disease’s trajectory, showcasing the potential of AI in navigating the complexities of pandemic management [ 188 ]. Moreover, AI’s capabilities extend to enhancing public health strategies, enabling the expedited development and dissemination of vaccines and therapeutic solutions in times of crisis.

8.4. Data Management

Recognizing the critical role of data management, future research should prioritize its advancement. Data management involves the following:

Few-shot learning: Few-shot learning requires only a small number of labeled examples for a new concept. This could be beneficial for situations where obtaining even a small amount of labeled data for a rare disease is possible. By learning from these few examples, the model could potentially generalize to similar cases [ 190 , 191 ].

Zero-shot learning (ZSL): In theory, ZSL could allow AI models to learn about new diseases or medical conditions even with no labeled data for those specific cases. ZSL leverages existing knowledge and relationships between concepts to make predictions for unseen categories. While ZSL is still under development, it holds promise for healthcare applications where data are extremely limited [ 192 ].

Meta-learning: This approach focuses on training models to “learn how to learn” efficiently. A meta-learning model could be trained on various healthcare-related tasks with limited datasets for each task. This acquired knowledge about learning itself could then be applied to new, unseen medical problems with minimal data, potentially improving performance [ 193 ].

• Ensuring model versatility: Achieving versatility in AI models is essential for their effective application across the diverse landscape of healthcare settings and patient demographics. Techniques such as domain adaptation and transfer learning stand out as effective solutions, enabling AI models trained on one dataset to adjust and perform accurately on another with little need for retraining [ 194 ]. This capability is particularly valuable in healthcare, where patient characteristics, disease profiles, and treatment responses can vary widely [ 195 ]. By fostering such adaptability, these techniques ensure that AI can be deployed more universally, enhancing its effectiveness and utility for a broad spectrum of patients.

8.5. Ethical Considerations and Trust Building

Acknowledging the importance of ethical considerations and trust-building, future research should concentrate on these aspects. Ethical considerations and trust-building involve the following:

• Ensuring data privacy: Addressing data privacy concerns in healthcare has become increasingly crucial with the rise in AI applications. An exemplary solution to this challenge is federated learning, a novel AI model training approach that enables algorithms to learn from data stored on local servers across different healthcare institutions without the need for direct data sharing [ 196 ]. This method significantly enhances privacy and security and offers a strategic advantage in the healthcare industry where the sensitivity and confidentiality of patient data are of utmost importance.
• Stakeholder acceptance: Ensuring trust and acceptance among stakeholders is critical for the successful integration of AI into healthcare practices [ 197 ]. This encompasses not only patients and clinicians but also policymakers, regulatory bodies, healthcare administrators, and other relevant parties. Patients may express concerns regarding the reliability and accountability of AI-driven decision-making processes. Therefore, transparent communication about the role of AI in treatment plans and the potential benefits it offers is essential to foster patient acceptance. Similarly, clinicians may have reservations about entrusting AI algorithms with decision-making responsibilities, fearing loss of autonomy or professional judgment, as well as doubting the accuracy of AI decisions. Establishing comprehensive training programs and collaborative frameworks that empower clinicians to understand and validate AI tools effectively can mitigate these concerns. Furthermore, building trust extends to engaging stakeholders such as policymakers, regulatory bodies, and healthcare administrators. Transparency in AI development and deployment, coupled with clear communication about ethical, legal, and regulatory considerations, is crucial for gaining stakeholder trust. Establishing robust governance frameworks that address these concerns can enhance confidence in AI systems and ensure accountability.
• Building trust with Explainable AI: Explainable AI (XAI) aims to make AI decision-making processes transparent and understandable to humans, a crucial aspect for clinical applications [ 198 ]. By providing insights into how AI models arrive at their conclusions, XAI fosters trust among healthcare professionals and patients, ensuring that AI-supported decisions are well informed and ethically sound. This transparency is vital for integrating AI into sensitive healthcare decisions, where understanding the rationale behind AI recommendations can significantly impact patient care and outcomes.

To sum up, the future of AI in healthcare is bright and filled with possibilities. While challenges remain, particularly in terms of ethics, regulation, and integration, the potential benefits are immense. As AI technology continues to evolve, it promises to revolutionize healthcare, making it more personalized, efficient, and responsive to global health needs.

9. Conclusions

This paper has provided an in-depth examination of the significant role played by AI in revolutionizing healthcare. Across various domains, including clinical decision-making, hospital operations, medical imaging, diagnostics, and patient care through wearable technologies and virtual assistants, AI has showcased its transformative impact. By enabling enhanced diagnostic accuracy, facilitating personalized treatments, and optimizing operational efficiency, AI holds promise for reshaping the healthcare landscape.

However, alongside these advancements, AI implementation in healthcare also raises important ethical considerations. Concerns surrounding data privacy, consent, and bias necessitate careful integration and adherence to regulatory standards. Balancing the potential benefits of AI with ethical considerations is imperative for ensuring its responsible and effective utilization in healthcare settings. In addition, equitable access and affordability are key building blocks for the future.

Looking towards the future, AI holds immense potential for personalized medicine, advanced drug discovery, and addressing global health crises. By leveraging AI technologies, healthcare delivery can become more efficient, data-driven, and patient-centric. Yet, realizing this potential requires a concerted effort from various stakeholders including technology developers, healthcare providers, policymakers, and patients.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, M.F.; methodology, M.F.; investigation, S.M.V. and M.F.; resources, M.F.; writing—original draft preparation, S.M.V.; writing—review and editing, M.F.; visualization, S.M.V.; supervision, M.F.; project administration, M.F.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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As a scientist, could I work remotely?

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In recent years, more industries have adopted a hybrid working model. However, while the aim isn't to create a virtual laboratory, scientists have found it challenging to work outside of the lab environment. In fact, many scientists' roles remain fully on-site.

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For starters, all of their research and tests are conducted at their institutes and stored on an on-premises server.

This implies that scientists cannot frequently access their lab notebooks from home, limiting their productive work hours to days when they are in the office. As an alternative, they must rely on their colleagues to email them images of instrument readings because such machines cannot be accessed outside of the laboratory.

While most people can now operate in hybrid mode, the amount of digital data generated in scientific studies has increased dramatically.

From genomic sequences to digital pathology, scientists handle massive datasets requiring advanced computing power and data analysis tools. While these capabilities drive significant advancements in life sciences, they also present a challenge for remote working.

Additionally, effective collaboration is a third obstacle for scientists trying to embrace remote work practices. Scientific discoveries rely heavily on teamwork, and having disparate spreadsheets, notebooks, and email conversations hinders true collaboration.

Currently, the main barrier to remote working for scientists is the absence of suitable lab informatics, LIMS (Laboratory Information Management Systems), and ELN (Electronic Lab Notebook) software. So, what key features should you look for in LIMS or ELN software to facilitate remote work?

Lab software for remotely working scientists

Sapio is significantly accelerating scientific discoveries by demonstrating to the scientific community the potential of a science-aware approach to Laboratory Information Management Systems (LIMS) and Electronic Lab Notebooks (ELN) software.

A key differentiator is that Sapio is the only completely cloud-based lab informatics platform. This global accessibility means that as long as there is an internet connection, all data in the cloud-based LIMS and ELN can be accessed from anywhere. This feature supports scientists in becoming remote workers.

Additionally, Sapio's platform seamlessly integrates with laboratory instruments, feeding data directly into the system. This integration allows for real-time data uploads from different instruments across multiple global locations. Consequently, scientists can review their research remotely, whether attending conferences or working in other laboratories.

Sapio ELN enables scientists to design experiments and evaluate data from any location, including their home office, a serviced office, or a neighborhood coffee shop. This means that scientists can collaborate efficiently and successfully with colleagues all over the world without having to spend all of their time in the lab office.

Sapio's Electronic Lab Notebook also supports full cloud-based collaboration, enabling real-time collaboration with peers all over the world. Sapio ELN can serve as the foundation for remote scientific collaboration by providing internal notifications and messaging.

Sapio ELN allows scientists from all around the world to collaborate on groundbreaking research initiatives, share discoveries, and brainstorm new ideas. It can even be used to identify which team members have accessed each document or message.

In the evolving landscape of remote work, the importance of system integration capabilities in cloud software cannot be overstated. As businesses manage the complexities of distributed teams, seamlessly integrating various tools and systems becomes crucial for operational efficiency.

Integration capabilities ensure that diverse platforms—such as project management, CRM, communication, and HR systems—work harmoniously, creating a unified workflow that bridges gaps between different departments and functions. This synergy enhances collaboration, streamlines processes, and supports the smooth functioning of remote and hybrid work environments.

In the world of remote work, where digital innovation is critical, ELain  stands out as a disruptive artificial intelligence solution designed to change the way businesses operate.

ELain, which represents the pinnacle of AI technology, provides a set of features designed to boost productivity, automate mundane operations, and promote more accurate decision-making.

Its incorporation into remote work environments empowers teams by automating administrative activities, analyzing massive datasets to unearth important insights, and even forecasting future trends that may be used in strategic planning.

By leveraging the capabilities of ELain, remote teams can easily navigate the challenges of distributed work, ensuring that workflow efficiency and innovation stay uninterrupted despite physical distances.

What is the future of remote working for scientists?

While most other knowledge workers have been able to embrace remote and hybrid working, it has proven to be much harder for scientists. Although scientific research requires in-person, hands-on lab time, there are always opportunities to work from home, whether that involves data analysis , writing reports, or planning experiments.

Scientists should also be able to enjoy the better work-life balance that comes with hybrid work—whether it is reduced commuting time, improved well-being and mental health, or simply better access to coffee and snacks.

AS previously mentioned, the primary barrier to remote working for scientists is the lack of appropriate lab informatics, LIMS, and ELN software. However, if a laboratory adopts the right platform—one that is cloud-based and designed for scientists—it can unlock the potential for remote work previously unavailable to scientists.

For the scientific community, integrating technology, virtual collaboration tools, and remote access with real-time data is essential. This integration transforms the home office from a mere place for paperwork into a hub of innovation where scientific breakthroughs can occur.

About Sapio Sciences

Sapio Sciences ' mission is to improve lives by accelerating discovery, and because science is complex, Sapio makes technology simple. Sapio is a global business offering an all-in-one science-aware (TM) lab informatics platform combining cloud-based LIMS, ELN, and Jarvis data solutions.

Sapio serves some of the largest global and specialist brands, including biopharma, CRO/CDMOs and clinical diagnostic labs across NGS genomic sequencing, bioanalysis, bioprocessing, stability, clinical, histopathology, drug research, and in vivo studies. Customers love Sapio's platform because it is robust, scalable, and with no-code configuration, can quickly adapt to meet unique needs.

Sponsored Content Policy: News-Medical.net publishes articles and related content that may be derived from sources where we have existing commercial relationships, provided such content adds value to the core editorial ethos of News-Medical.Net which is to educate and inform site visitors interested in medical research, science, medical devices and treatments.

Last updated: Aug 12, 2024 at 8:07 AM

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COVID-19: Long-term effects

Some people continue to experience health problems long after having COVID-19. Understand the possible symptoms and risk factors for post-COVID-19 syndrome.

Most people who get coronavirus disease 2019 (COVID-19) recover within a few weeks. But some people — even those who had mild versions of the disease — might have symptoms that last a long time afterward. These ongoing health problems are sometimes called post- COVID-19 syndrome, post- COVID conditions, long COVID-19 , long-haul COVID-19 , and post acute sequelae of SARS COV-2 infection (PASC).

What is post-COVID-19 syndrome and how common is it?

Post- COVID-19 syndrome involves a variety of new, returning or ongoing symptoms that people experience more than four weeks after getting COVID-19 . In some people, post- COVID-19 syndrome lasts months or years or causes disability.

Research suggests that between one month and one year after having COVID-19 , 1 in 5 people ages 18 to 64 has at least one medical condition that might be due to COVID-19 . Among people age 65 and older, 1 in 4 has at least one medical condition that might be due to COVID-19 .

What are the symptoms of post-COVID-19 syndrome?

The most commonly reported symptoms of post- COVID-19 syndrome include:

• Symptoms that get worse after physical or mental effort
• Lung (respiratory) symptoms, including difficulty breathing or shortness of breath and cough

Other possible symptoms include:

• Neurological symptoms or mental health conditions, including difficulty thinking or concentrating, headache, sleep problems, dizziness when you stand, pins-and-needles feeling, loss of smell or taste, and depression or anxiety
• Joint or muscle pain
• Heart symptoms or conditions, including chest pain and fast or pounding heartbeat
• Digestive symptoms, including diarrhea and stomach pain
• Blood clots and blood vessel (vascular) issues, including a blood clot that travels to the lungs from deep veins in the legs and blocks blood flow to the lungs (pulmonary embolism)
• Other symptoms, such as a rash and changes in the menstrual cycle

Keep in mind that it can be hard to tell if you are having symptoms due to COVID-19 or another cause, such as a preexisting medical condition.

It's also not clear if post- COVID-19 syndrome is new and unique to COVID-19 . Some symptoms are similar to those caused by chronic fatigue syndrome and other chronic illnesses that develop after infections. Chronic fatigue syndrome involves extreme fatigue that worsens with physical or mental activity, but doesn't improve with rest.

Why does COVID-19 cause ongoing health problems?

Organ damage could play a role. People who had severe illness with COVID-19 might experience organ damage affecting the heart, kidneys, skin and brain. Inflammation and problems with the immune system can also happen. It isn't clear how long these effects might last. The effects also could lead to the development of new conditions, such as diabetes or a heart or nervous system condition.

The experience of having severe COVID-19 might be another factor. People with severe symptoms of COVID-19 often need to be treated in a hospital intensive care unit. This can result in extreme weakness and post-traumatic stress disorder, a mental health condition triggered by a terrifying event.

What are the risk factors for post-COVID-19 syndrome?

You might be more likely to have post- COVID-19 syndrome if:

• You had severe illness with COVID-19 , especially if you were hospitalized or needed intensive care.
• You had certain medical conditions before getting the COVID-19 virus.
• You had a condition affecting your organs and tissues (multisystem inflammatory syndrome) while sick with COVID-19 or afterward.

Post- COVID-19 syndrome also appears to be more common in adults than in children and teens. However, anyone who gets COVID-19 can have long-term effects, including people with no symptoms or mild illness with COVID-19 .

What should you do if you have post-COVID-19 syndrome symptoms?

If you're having symptoms of post- COVID-19 syndrome, talk to your health care provider. To prepare for your appointment, write down:

• When your symptoms started
• What makes your symptoms worse
• How often you experience symptoms
• How your symptoms affect your activities

Your health care provider might do lab tests, such as a complete blood count or liver function test. You might have other tests or procedures, such as chest X-rays, based on your symptoms. The information you provide and any test results will help your health care provider come up with a treatment plan.

In addition, you might benefit from connecting with others in a support group and sharing resources.

• Long COVID or post-COVID conditions. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects.html. Accessed May 6, 2022.
• Post-COVID conditions: Overview for healthcare providers. Centers for Disease Control and Prevention. https://www.cdc.gov/coronavirus/2019-ncov/hcp/clinical-care/post-covid-conditions.html. Accessed May 6, 2022.
• Mikkelsen ME, et al. COVID-19: Evaluation and management of adults following acute viral illness. https://www.uptodate.com/contents/search. Accessed May 6, 2022.
• Saeed S, et al. Coronavirus disease 2019 and cardiovascular complications: Focused clinical review. Journal of Hypertension. 2021; doi:10.1097/HJH.0000000000002819.
• AskMayoExpert. Post-COVID-19 syndrome. Mayo Clinic; 2022.
• Multisystem inflammatory syndrome (MIS). Centers for Disease Control and Prevention. https://www.cdc.gov/mis/index.html. Accessed May 24, 2022.
• Patient tips: Healthcare provider appointments for post-COVID conditions. https://www.cdc.gov/coronavirus/2019-ncov/long-term-effects/post-covid-appointment/index.html. Accessed May 24, 2022.
• Bull-Otterson L, et al. Post-COVID conditions among adult COVID-19 survivors aged 18-64 and ≥ 65 years — United States, March 2020 — November 2021. MMWR Morbidity and Mortality Weekly Report. 2022; doi:10.15585/mmwr.mm7121e1.

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Future-Ready: 10 Best Practices to Improve Prepay Claims Editing

by Mark Turner, Vice President, Clinical Content, Lyric 08/12/2024 Leave a Comment

In the fast-evolving and intricate landscape of payment integrity, the value of prepay editing is paramount. As powerful AI and advanced analytics are being applied earlier in the claim adjudication process, the importance of next-generation claim editing capabilities will separate health plan leaders from laggards.

If you’re equating claims editing with medical savings alone—you’re missing the boat and the opportunity. Because forward-thinking payment integrity and claims operations leaders recognize that prepay editing, and its underlying best practices, improve administrative costs, plan member satisfaction, and provider relationships.  

Here are ten best practices of prepay editing, which health plans should be using today:

• Staying Agile with Regulatory Updates

In a sector governed by stringent regulations, it’s crucial for health plans to stay updated on policies and regulations from authorities that include CMS and the AMA. This proactive approach helps ensure compliance and efficiency, positioning your plan as a leader in payment integrity. 

• Fostering Strategic Alignment in Claims Editing

Finding the right balance between maximizing savings and maintaining positive provider relations is critical. Building “better bridges” between plans and providers involves both applying traditional sources of rules, as well as plan policies rooted in evidence-based data and resources. 

This approach improves trust and transparency, leading to more sustainable, mutually beneficial relationships with providers.   

3.  Harnessing AI for Enhanced Analytics

AI offers transformative potential in analyzing healthcare claims, enabling plans to identify trends and anomalies effectively. When combined with human oversight, AI becomes a powerful tool for enhancing decision-making processes. 

This technology should be utilized to amplify the capabilities of your team, including experienced medical directors and certified coders, ensuring a deeper and more accurate analysis of claims data.

• Periodically Reviewing Carveouts

Regular re-evaluation of exclusions and the scenarios that lead to them is essential. This continuous process not only ensures that your claim editing strategies are current but also provides the framework needed to adjust and refine your editing strategies continually.

• Discovering Opportunities in Diagnosis Editing

In-depth analysis of current editing strategies can reveal significant untapped savings in diagnosis edits. This expanded approach relies on a comprehensive content library supported by health information specialists who bring a wealth of knowledge and expertise.

• Recognizing Efficiency Equals Economy

Streamlining claims processing directly impacts economic outcomes by reducing administrative expenses. Efficient processes, supported by robust editing tools and advanced policy management, not only save medical costs, but also conserve time and resources. Unlocking value goes beyond savings to drive greater positives across health plan payment operations. 

• Preventing Post-Payment Issues

By ensuring payment accuracy upfront, health plans can avoid the costs associated with post-payment corrections. This proactive strategy in pre-payment claim editing involves foresight and prevention fueled by proper technology and clinical content library resources, supported by experienced medical directors and certified coders.   

• Improving Transparency in Claim Adjudication Explainability

Enhancing transparency in claims adjudication can significantly reduce provider abrasion. By making payment policies clear and accessible, and explaining decisions in understandable terms, health plans can improve payer-provider relationships. 

This transparency is underpinned by comprehensive policy management and the integration of intelligible content from respected sources.

• Aligning Operations 

Health plans focused on enhancing and helping to optimize their payment integrity, must integrate underlying capabilities—across the value chain—into a unified workflow. This approach avoids conflicting edits and promotes greater payment accuracy.

The next era of operational alignment in payment integrity will be driven by AI-based platforms incorporating best-of-breed tools, advanced analytics, and robust, evidence-based clinical content assets.

•   Improve Plan Member Experience

Clear communication regarding payment policies enhances member satisfaction by making healthcare costs more predictable. Ensuring that policies are both accurate and easy to understand requires a sophisticated infrastructure that supports policy automation and effective communication, key for fostering trust and clarity among plan members.

Charting the Course: Next Steps in Prepay Editing

Optimizing prepay editing is a strategic imperative that goes beyond mere compliance and financial outcomes; it’s about positioning your health plan as a leader in a complex healthcare ecosystem. By adopting these best practices, health plan leaders can ensure their operations are efficient, compliant, and closely aligned with both the needs of providers and the expectations of members. 

As health plans continue to integrate advanced technologies and robust policy management into their payment integrity operations, the synergy between innovative solutions and traditional management practices will be key to their success. This holistic approach not only enhances operational efficiency but also strengthens the overall healthcare system, supporting better outcomes for all stakeholders involved.

About Mark Turner

Mark Turner, Vice President of Clinical Content & Strategy at Lyric , brings over twenty-five years of experience in delivering superior healthcare IT solutions across both provider and payer settings. He is responsible for delivering the high-quality content that drives the value for our customers and as well as setting the go forward clinical content strategy for Lyric. 

Under his leadership, Lyric’s market-leading clinical content supports health plan customers by enhancing payment accuracy, boosting medical savings, improving fraud detection, and reducing provider abrasion.

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Guest Essay

Erin Brockovich: What’s at Stake in November

By Erin Brockovich

Ms. Brockovich is an environmental activist.

Every day, I get emails from people asking for help. They think I’m a lawyer. I’m not. They want to know what caused their cancer or why their farm has tested high for chemicals they’ve never heard of. They want someone to fight for them.

The recent Supreme Court decision overturning the 40-year-old Chevron precedent, which allowed federal agencies to interpret the laws they oversee, should wake us up to how truly alone we are when it comes to environmental health protections. If Donald Trump wins in November, things could go from bad to worse. Progress to protect Americans from dangerous chemicals could reach a standstill.

I could list dozens, if not thousands, of contaminants we come in contact with, some regulated by federal and state agencies, and others not. I’ll focus on per- and polyfluoroalkyl substances, or PFAS, a class of thousands of synthetic chemicals that are finally being recognized for the damage they cause.

PFAS are known as “forever chemicals” because they persist in the environment and in human bodies for decades. These chemicals have been used to make common items from textiles to adhesives to food packaging to firefighting foams to nonstick cookware.

The health problems associated with exposure to PFAS include fertility issues, developmental delays in children and increased risk of certain cancers and of obesity, according to the Environmental Protection Agency . Scientists have detected PFAS chemicals in the blood of almost all Americans .

What’s frustrating is that we’ve known for decades which industries use these chemicals, and we’ve known they are accumulating in the environment. But companies and our regulators delayed action.

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