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Background: Patients of geriatrics are often treated by several health care providers at the same time. The spatial, informational, and organizational separation of these health care providers can hinder the effective treatment of these patients.
Objective: This study aimed to develop a regional health information exchange (HIE) system to improve HIE in geriatric treatment. This study also evaluated the usability of the regional HIE system and sought to identify barriers to and facilitators of its implementation.
Methods: The development of the regional HIE system followed the community-based participatory research approach. The primary outcomes were the usability of the regional HIE system, expected implementation barriers and facilitators, and the quality of the developmental process. Data were collected and analyzed using a mixed methods approach.
Results: A total of 3 focus regions were identified, 22 geriatric health care providers participated in the development of the regional HIE system, and 11 workshops were conducted between October 2019 and September 2020. In total, 12 participants responded to a questionnaire. The main results were that the regional HIE system should support the exchange of assessments, diagnoses, medication, assistive device supply, and social information. The regional HIE system was expected to be able to improve the quality and continuity of care. In total, 5 adoption facilitators were identified. The main points were adaptability of the regional HIE system to local needs, availability to different patient groups and treatment documents, web-based design, trust among the users, and computer literacy. A total of 13 barriers to adoption were identified. The main expected barriers to implementation were lack of resources, interoperability issues, computer illiteracy, lack of trust, privacy concerns, and ease-of-use issues.
Conclusions: Participating health care professionals shared similar motivations for developing the regional HIE system, including improved quality of care, reduction of unnecessary examinations, and more effective health care provision. An overly complicated registration process for health care professionals and the patients’ free choice of their health care providers hinder the effectiveness of the regional HIE system, resulting in incomplete patient health information. However, the web-based design of the system bridges interoperability problems that exist owing to the different technical and organizational structures of the health care facilities involved. The regional HIE system is better accepted by health care professionals who are already engaged in an interdisciplinary, geriatric-focused network. This might indicate that pre-existing cross-organizational structures and processes are prerequisites for using HIE systems. The participatory design supports the development of technologies that are adaptable to regional needs. Health care providers are interested in participating in the development of an HIE system, but they often lack the required time, knowledge, and resources.
Guidelines and Standard Frameworks for AI in Medicine: Protocol for a Systematic Literature Review
(2023)
Background: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the “black box” nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation.
Objective: This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine.
Methods: We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented.
Results: The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023.
Conclusions: Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks—as will be done in the proposed review—will provide comprehensive insights into current gaps and help to formulate future research directions.
International Registered Report Identifier (IRRID): DERR1-10.2196/47105
Background: Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.
Objective: This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.
Methods: The Arksey and O’Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.
Results: A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic.
Conclusions: This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing.
International Registered Report Identifier (IRRID): RR2-10.2196/22505