UNASSIGNED: Medication errors are the third leading cause of death. There are several methods to prevent prescription errors, one of which is to use a Computerized Physician Order Entry system (CPOE). In a CPOE system, necessary data needs to be collected so that making decisions about prescribing medications and treatment plans could be made. Although many CPOE systems have been developed worldwide, studies have yet to identify the necessary data and data elements of CPOE systems. This study aims to identify data elements of CPOE and standardize these data with Fast Healthcare Interoperability Resources (FHIR) to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity.
UNASSIGNED: PubMed, Web of Science, Embase, and Scopus databases for studies up to October 2019 were searched. Two reviewers independently assessed original articles to determine eligibility for inclusion in this review. All articles describing data elements of a COPE system were included. Data elements were obtained from the included articles\' text, tables, and figures.Classification of the extracted data elements and mapping them to FHIR was done to facilitate data sharing and integration with the electronic health record (EHR) system and reduce data diversity. The final data elements of CPOE were categorized into five main categories of FHIR (foundation, base, clinical, financial, and specialized) and 146 resources, where possible. One of the researchers did mapping and checked and verified by the second researcher. If a data element could not be mapped to any FHIR resources, this data element was considered an extension to the most relevant resource.
UNASSIGNED: We retrieved 5162 articles through database searches. After the full-text assessment, 21 articles were included. In total, 270 data elements were identified and mapped to the FHIR standard. These elements have been reported in 26 FHIR resources of 146 ones (18%). In total, 71 data elements were considered an extension.
UNASSIGNED: The results of this study showed that the same data elements were not used in the CPOE systems, and the degree of homogeneity of these systems is limited. The mapping of extracted data with data elements used in the FHIR standard shows the extent to which these systems comply with existing standards. Considering the standards in these systems\' design helps developers design more coherent systems that can share data with other systems.

UNASSIGNED: Machine learning-enabled clinical information systems (ML-CISs) have the potential to drive health care delivery and research. The Fast Healthcare Interoperability Resources (FHIR) data standard has been increasingly applied in developing these systems. However, methods for applying FHIR to ML-CISs are variable.
UNASSIGNED: This study evaluates and compares the functionalities, strengths, and weaknesses of existing systems and proposes guidelines for optimizing future work with ML-CISs.
UNASSIGNED: Embase, PubMed, and Web of Science were searched for articles describing machine learning systems that were used for clinical data analytics or decision support in compliance with FHIR standards. Information regarding each system\'s functionality, data sources, formats, security, performance, resource requirements, scalability, strengths, and limitations was compared across systems.
UNASSIGNED: A total of 39 articles describing FHIR-based ML-CISs were divided into the following three categories according to their primary focus: clinical decision support systems (n=18), data management and analytic platforms (n=10), or auxiliary modules and application programming interfaces (n=11). Model strengths included novel use of cloud systems, Bayesian networks, visualization strategies, and techniques for translating unstructured or free-text data to FHIR frameworks. Many intelligent systems lacked electronic health record interoperability and externally validated evidence of clinical efficacy.
UNASSIGNED: Shortcomings in current ML-CISs can be addressed by incorporating modular and interoperable data management, analytic platforms, secure interinstitutional data exchange, and application programming interfaces with adequate scalability to support both real-time and prospective clinical applications that use electronic health record platforms with diverse implementations.

BACKGROUND: The standard Fast Healthcare Interoperability Resources (FHIR) is widely used in health information technology. However, its use as a standard for health research is still less prevalent. To use existing data sources more efficiently for health research, data interoperability becomes increasingly important. FHIR provides solutions by offering resource domains such as \"Public Health & Research\" and \"Evidence-Based Medicine\" while using already established web technologies. Therefore, FHIR could help standardize data across different data sources and improve interoperability in health research.
OBJECTIVE: The aim of our study was to provide a systematic review of existing literature and determine the current state of FHIR implementations in health research and possible future directions.
METHODS: We searched the PubMed/MEDLINE, Embase, Web of Science, IEEE Xplore, and Cochrane Library databases for studies published from 2011 to 2022. Studies investigating the use of FHIR in health research were included. Articles published before 2011, abstracts, reviews, editorials, and expert opinions were excluded. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and registered this study with PROSPERO (CRD42021235393). Data synthesis was done in tables and figures.
RESULTS: We identified a total of 998 studies, of which 49 studies were eligible for inclusion. Of the 49 studies, most (73%, n=36) covered the domain of clinical research, whereas the remaining studies focused on public health or epidemiology (6%, n=3) or did not specify their research domain (20%, n=10). Studies used FHIR for data capture (29%, n=14), standardization of data (41%, n=20), analysis (12%, n=6), recruitment (14%, n=7), and consent management (4%, n=2). Most (55%, 27/49) of the studies had a generic approach, and 55% (12/22) of the studies focusing on specific medical specialties (infectious disease, genomics, oncology, environmental health, imaging, and pulmonary hypertension) reported their solutions to be conferrable to other use cases. Most (63%, 31/49) of the studies reported using additional data models or terminologies: Systematized Nomenclature of Medicine Clinical Terms (29%, n=14), Logical Observation Identifiers Names and Codes (37%, n=18), International Classification of Diseases 10th Revision (18%, n=9), Observational Medical Outcomes Partnership common data model (12%, n=6), and others (43%, n=21). Only 4 (8%) studies used a FHIR resource from the domain \"Public Health & Research.\" Limitations using FHIR included the possible change in the content of FHIR resources, safety, legal matters, and the need for a FHIR server.
CONCLUSIONS: Our review found that FHIR can be implemented in health research, and the areas of application are broad and generalizable in most use cases. The implementation of international terminologies was common, and other standards such as the Observational Medical Outcomes Partnership common data model could be used as a complement to FHIR. Limitations such as the change of FHIR content, lack of FHIR implementation, safety, and legal matters need to be addressed in future releases to expand the use of FHIR and, therefore, interoperability in health research.

Information technology has shifted paper-based documentation in the health care sector into a digital form, in which patient information is transferred electronically from one place to another. However, there remain challenges and issues to resolve in this domain owing to the lack of proper standards, the growth of new technologies (mobile devices, tablets, ubiquitous computing), and health care providers who are reluctant to share patient information. Therefore, a solid systematic literature review was performed to understand the use of this new technology in the health care sector. To the best of our knowledge, there is a lack of comprehensive systematic literature reviews that focus on Fast Health Interoperability Resources (FHIR)-based electronic health records (EHRs). In addition, FHIR is the latest standard, which is in an infancy stage of development. Therefore, this is a hot research topic with great potential for further research in this domain.
The main aim of this study was to explore and perform a systematic review of the literature related to FHIR, including the challenges, implementation, opportunities, and future FHIR applications.
In January 2020, we searched articles published from January 2012 to December 2019 via all major digital databases in the field of computer science and health care, including ACM, IEEE Explorer, Springer, Google Scholar, PubMed, and ScienceDirect. We identified 8181 scientific articles published in this field, 80 of which met our inclusion criteria for further consideration.
The selected 80 scientific articles were reviewed systematically, and we identified open questions, challenges, implementation models, used resources, beneficiary applications, data migration approaches, and goals of FHIR.
The literature analysis performed in this systematic review highlights the important role of FHIR in the health care domain in the near future.