a GIS: geographic information system.
The variety of PHR platforms led to the generation of different data formats ( Table 1 ). Newly generated patient data were not limited to plain text and numbers in structured tables. Electronic messages, for example, were composed of text and metadata describing the time of transmission and the identity of sending and receiving parties. Templated documents and forms were used for standard reports such as legal documents, care plans, and insurance reports [ 46 ]. Images, also prevalent in PHRs today, were used by patients and providers to capture, store, and transmit health data, such as radiology results (2-dimensional x-rays, 3-dimensional computed tomography scans, positron emission tomography scans, magnetic resonance imaging scans, 4-dimensional beating heart) [ 84 ], signs and symptoms (wound images) [ 91 ], camera uploads [ 31 ], health trends (growth charts) [ 46 ], mood graphs [ 37 ], blood sugar graphs[ 99 ], laboratory flow sheets [ 31 ], and legal documentation (power of attorney for children and adolescents) [ 22 ]. Audio and video were used to capture phone call content [ 46 ] and record visits [ 46 ]. Newer data formats generated by patient tools and mobile apps included Google Maps for facility information and Google Calendar entries associated with appointment scheduling [ 31 ].
Next, we analyzed the data elements extracted by the year of first mention ( Figure 3 ). In the early 1990s, PHR data elements mentioned in the literature pertained to researchers’ and practitioners’ visions of potential future systems. These included general patient data, such as demographics, and medical encounter information, such as visit summary.
Patient health record (PHR) data elements by year of first mention.
After initial uses of PHR systems in the early 2000s, new data elements such as appointments, preferences, and system settings emerged. More recently, PHR data included reminders (eg, appointment reminders [ 51 , 99 , 101 ], medication reminders [ 93 , 110 , 114 ], screening and laboratory work reminders [ 42 , 46 , 110 ], immunization reminders [ 29 , 30 , 55 , 57 , 82 , 90 ], preventive care reminders [ 21 , 59 , 60 ], and health maintenance reminders [ 82 ]), in addition to alerts [ 22 , 76 , 77 , 99 ], identification of personal health goals [ 19 , 24 , 38 - 40 , 43 , 72 , 74 ], and disease prevention [ 76 , 77 , 99 , 110 , 115 ]. Tracking and monitoring data via e-journals [ 82 ] and diaries [ 50 ] also became available.
Today, PHR data are generated through different tools and devices. Tracking devices, now transmitting time-series PHR data, are used to monitor patients’ vital signs, such as blood pressure and glucose level (biomonitoring devices) [ 74 , 99 ], and to detect abnormal events, such as alerts from implantable cardioverter defibrillators [ 117 ].
PHR data were mainly used to provide added functionalities to patients. The provider search results [ 20 , 22 , 47 , 49 , 64 ], for example, helped patients locate health care providers and health-related services. Similar functionalities enabled patients to obtain health advice from support groups. Other functionalities assisted patients with preparing for medical encounters through visit preparation questionnaires [ 24 , 46 , 66 , 70 - 72 ]. Functionalities such as incentive programs [ 43 , 56 , 66 , 73 , 74 ] empowered patients through self-health monitoring. Finally, a unique PHR data category discovered in our review, environmental information [ 36 , 50 , 56 , 67 ], captured community health concerns and environmental domains, which can be linked to functionalities such as assessment of environment-related risk factors and recommendations for preventive care.
Description of the data extracted revealed which functionalities were available to the patient through the PHR and indicated an interesting evolution of PHR functionalities ( Figure 4 ).
Patient health record functionality evolution over time, showing the most common sources, data types, and functionalities found in the review. EHR: electronic health record.
The evolution of PHR data elements over time ( Figure 4 ) illustrates the general inclination in the early stages toward providing the patient with access to health information regarding their medical encounter.
Even though the giving patients access to their own health data was initiated in the 1970s, PHR systems were not widely used until the early 2000s. Because of the infancy of PHR systems, research in this domain has focused on system adoption and how it relates to patient satisfaction. Only limited research is available on how to leverage PHR data to improve health outcomes.
Starting in 2005, data elements reported in the literature indicate a shift toward a more interactive view of the PHR system and the introduction of several new attributes and functionalities. Patient PHR settings, including security and privacy preferences, became more prevalent. The most significant development of this time period of PHR evolution was the interaction and engagement of the patient with the system. Functionalities such as patient-provider secure messaging and appointment scheduling were becoming more common.
More recently, the PHR system has seen a greater inclusion of patient tracking and monitoring functionalities as daily reported data from patients and caregivers become more prevalent. Albeit rare, PHR systems also increasingly allow for cost measurement and management.
Overall, the results indicate an increasing focus in the literature on newer types and sources of data, as well as on providing patients with access to their health data. Yet some of these may be progressing so rapidly that important related issues are somewhat neglected. Few studies, for instance, have examined the impact of user interface design on patients’ understanding of data and system use. Issues associated with the use of PHRs are mainly related to patients’ understanding of the underlying information presented. Problems related to understanding of health data may lead to stress and anxiety [ 63 ], which could outweigh the potential benefits of data access. Hence, research is needed in the area of data visualization and representation models specifically targeted for patient use. Examples of such models available in the literature are the what-if analysis, [ 99 ] brief intervention [ 109 ], and traffic-light feedback system [ 74 ]. These methods indicate the risks associated with specific health activities, along with related outcomes and recommended interventions. The traffic-light feedback system, for example, provides patients with an effective visualization tool to track their progress toward attainment of blood pressure goals.
In addition, more research is needed to investigate and improve the quality of patient-entered data. Today, more than 35,000 mobile health apps are available for the iOS and Android operating systems, generating large amounts of data [ 118 ]. Data are also increasingly entered through patient forums and portals. While new platforms allow the generation and availability of large data volumes, the wide variety of levels of expertise could lead to reliability and validity issues. Patient-entered data have been shown to be reliable for simple measures such as demographics and symptoms, but less reliable when they pertain to reporting more complicated measures such as laboratory values [ 5 ]. One method for improving accuracy could be to provide patients with standardized measures and guidelines for entering their own data, but even that needs to be part of a broader strategy to verify accuracy of data through triangulation from multiple sources.
As the variety of PHR data sources increases, special care is needed for data curation [ 119 ] and harmonization [ 120 ]. Processes need to be established to produce usable patient-reported data that can be used for research [ 121 ]. Standards need to be developed to improve interoperability between different components of the new PHR systems [ 122 ]. Data integration methods, such as entity stream mining [ 123 ], might be required to cross-reference patient data generated by different tools and devices.
In the coming years, PHR systems will create many data-related challenges, such as quality, heterogeneity, openness, security, scalability, and transparency. Abundant patient data might also trigger information overload. While potentially beneficial for improving health outcomes, streaming patient data can amount to very large volumes, creating new data quality, storage, and analysis issues. All of these challenges open doors for valuable research in health information systems.
The large amounts of data generated by sensors and devices might also require storage and analysis on the cloud [ 118 ], potentially increasing storage and analysis costs. Sharing patient data between networks may also create a risk of personal health information disclosure [ 124 ], generating additional costs for preserving patient privacy and security. This could also necessitate stronger methods for patient data protection beyond today’s practice, which opens up yet another important avenue for health informatics research.
Overall, PHR data evolution indicates a general trend toward greater patient engagement and health tracking. Moving forward, a continuation of these trends will lead to accumulation of vast amounts of rich data. If patients provide permission, research on PHR data can pave the way for patient-centered care.
The design of patient-centered decision support systems that use a combination of comprehensive individual patient information and aggregate data (collections of patient records) to provide personalized patient recommendations will be a significant area of research.
While past literature has listed patient-provider messaging as an important communication tool for patients and providers, secure message content may potentially provide a valuable patient data source for analysis. Based on their reported intended use, patient secure messages may contain information regarding health-related concerns such as new symptoms and adverse events. Among other possibilities, information retrieved from secure messages could, therefore, be used in research to identify treatment side effects and build patient risk models. However, it is important to keep in mind that terminology used by patients is likely to differ from terminology used by providers. Hence, natural language processing models traditionally used to extract patient information from provider notes may need to be adapted to fit the patient context.
Recently developed and highly effective deep learning algorithms could also be used to extract, search, sort, and analyze information from the tremendous amounts of image, voice, and video data [ 125 ] available in the PHR. Other new techniques might be needed to analyze relational data, such as from Google Maps and Google Calendars.
Also, current methods used to store, extract, and analyze EHR data are not adequate for analysis of large volumes of time-series data. Nonrelational databases might be needed to store tracking information. Stream learning algorithms [ 126 ] would also need to be applied to extract meaningful information from the terabytes of streaming data analyzed.
As patient-centered decision support systems are being implemented, it is important to ensure the validity of the generated output. Misclassification errors can be dangerous in this domain. Patient systems, which are embedded in mobile devices, need to be evaluated and approved by medical experts. Data transmitted from different sources can potentially be leveraged by providers to improve patient and population health outcomes. However, accurate measures are still needed to assess and improve the performance of such systems. In addition, these metrics need to account for biases present in patient-generated data. Prior research indicated that PHR systems are mostly used by patients who are typically more sick. Those are patients with comorbidities, such as cancer survivors [ 127 ]. Therefore, findings and models generated from analyzing these data might not be generalizable to other patient populations.
The new health care vision in the United States is characterized by automation and collaboration, creating the need for adaptation by all actors in the industry. Empowered patients today have the opportunity to leverage PHR systems data and functionalities. This, however, requires some level of technical expertise for system access and interaction, and medical knowledge in order to understand and interpret the medical information presented. Similarly, medical providers now have to learn and adopt new technologies in order to report medical data and communicate with patients. As a major actor in the health care industry, insurance companies also need to adapt to the new industry environment. Insurance firms today need to assess the value of virtual medical encounters and automated care, and process new types of patient data such as secure messages. Adaptation methods by all health industry players are yet to be assessed and optimized.
A limitation of this study is its focus on PHR data reported in the literature. The evolution of PHRs as described in this study might not necessarily reflect the state of the practice. More research is therefore needed to extract and evaluate PHR scope and the functionalities of the various PHR systems available in practice. Also, as mentioned above, this study focused on US studies, thereby limiting the scope of our analysis. Research comparing PHR systems in the United States with those used in other countries would help improve future data uses.
Digital health platforms have changed drastically in recent years. The introduction of distributed PHR systems enabled a shift toward more personalized and increasingly automated health care. The multiuser nature of PHR systems also facilitated patient-to-provider and patient-to-patient information sharing. Yet these changes generated opportunities and challenges at the user, system, and industry levels. Our assessment here of the state of the patient digital infrastructure serves as a valuable foundation for future research. Research implications identified also offer ways to significantly advance health information systems research. Identifying available PHR data also facilitates the development of intelligent health systems. Although primarily aimed at health information systems researchers, implications listed in this study can be further extended to health practitioners, insurance providers, and policy makers.
EHR | electronic health record |
HITECH | Health Information Technology for Economic and Clinical Health |
MeSH | Medical Subject Headings |
PHR | patient health record |
Conflicts of Interest: None declared.
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A systematic literature review of health information systems for healthcare.
2. material and method, 3. discussion, 3.1. the evolution of health information systems, 3.2. his structural deployment, 3.3. health information systems benefits, 3.4. information system and knowledge management in the healthcare arena, 3.4.1. information system, 3.4.2. knowledge management, 4. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
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Source: Authors | Core Enabling HIS Components | Benefits |
---|---|---|
Malaquias and Filho [ ] | Health ER eHealth mHealth | Ease of access to patient and medical information from records; Cost reduction; Enhance efficiency in patients’ data recovery and management; Enable stakeholders’ health information centralization and remote access. |
Ammenwerth, Duftschmid [ ] | eHealth | Upsurge in care efficacy and quality and condensed costs for clinical services; Lessen the health care system’s administrative costs; Facilitates novel models of health care delivery. |
Tummers, Tobi [ ] | HIS | Patient information management; Enable communication within the healthcare arena; Afford high-quality and efficient care. |
Steil, Finas [ ] | HIS | Enable inter- and multidisciplinary collaboration between humans and machines; Afford autonomous and intelligent decision capabilities for health care applications. |
Nyangena, Rajgopal [ ] | HIS | Enable seamless information exchange within the healthcare arena. |
Sik, Aydinoglu [ ] | HIS | Support precision medicine approaches and decision support. |
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Epizitone, A.; Moyane, S.P.; Agbehadji, I.E. A Systematic Literature Review of Health Information Systems for Healthcare. Healthcare 2023 , 11 , 959. https://doi.org/10.3390/healthcare11070959
Epizitone A, Moyane SP, Agbehadji IE. A Systematic Literature Review of Health Information Systems for Healthcare. Healthcare . 2023; 11(7):959. https://doi.org/10.3390/healthcare11070959
Epizitone, Ayogeboh, Smangele Pretty Moyane, and Israel Edem Agbehadji. 2023. "A Systematic Literature Review of Health Information Systems for Healthcare" Healthcare 11, no. 7: 959. https://doi.org/10.3390/healthcare11070959
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IMAGES
COMMENTS
Files Patient's Health Records based on terminal digit system Files health information certificates and records based on daily transactions Assembles patient health record based on forms arranged in order upon admission of patient Adopts computerized documentation Releases records based on citizen's charter 3.70 3.58 3.59 3.62 3.51
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