Yearbook of medical informatics最新文献

{"title":"Human Factors and Organizational Issues in Health Informatics: Review of Recent Developments and Advances.","authors":"Andre Kushniruk, David Kaufman","doi":"10.1055/s-0044-1800744","DOIUrl":"10.1055/s-0044-1800744","url":null,"abstract":"<p><strong>Objective: </strong>In this paper we focus on a review of key articles published in the past two years (2022 and 2023) in the areas of human factors and organizational issues in health informatics.</p><p><strong>Methods: </strong>We reviewed manuscripts that were published in primary human factors, human factors engineering and health informatics journals. This involved conducting a series of searches using PubMed, Web of Science, and Google Scholar for articles related to human factors in healthcare published in 2022 and 2023.</p><p><strong>Results: </strong>The range of applications that have been designed and analyzed using human factors approaches has been rapidly expanding, including increased number of articles around topics such as the following: AI in healthcare, patient-centered design, usability of mHealth, organizational issues, and work around ensuring system safety. This includes study of applications designed for use by both patients and health providers applying both qualitative and quantitative approaches to user requirements, user-centered system design and human factors analysis and evaluation.</p><p><strong>Conclusion: </strong>The importance of human factors is becoming recognized as new forms of health technology appear. A multi-level perspective on human factors, that considers human factors at multiple levels, from the individual user to the complex social and organizational context, was described to consider the range and diversity of human factors approaches in healthcare. Such an approach will be needed to drive the design and evaluation of useful and usable healthcare information technologies.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"196-209"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sensors, Signals, and Imaging Informatics: Best contributions from 2023.","authors":"Leticia Rittner, Christian Baumgartner, Thomas M Deserno","doi":"10.1055/s-0044-1800757","DOIUrl":"10.1055/s-0044-1800757","url":null,"abstract":"<p><strong>Objectives: </strong>To identify and highlight research papers that represent the advances and trends in the field of sensors, signals, and imaging informatics in 2023.</p><p><strong>Method: </strong>We performed a bibliographic search on Scopus and PubMed databases using Medical Sub-ject Heading (MeSH) terms combined with keywords. Our aim was to build specific queries for sen-sors, signals, and imaging informatics. We disregarded journals that returned less than three papers on the query and then evaluated titles and abstracts of the papers using a 3-point Likert scale, ranging from 1 (do not include) to 3 (should be included). Only the papers with a total score of 8 or more were re-evaluated again, this time considering the full text, and the top 14 papers with the highest scores were then reviewed by external reviewers and editors of the International Medical Informatics Association (IMIA) Yearbook.</p><p><strong>Results: </strong>Among the 643 returned papers published in 2023 in the various areas of sensors, signals, and imaging informatics (SSII), we selected 58 papers with at least 8 Likert points (in total). After a comprehensive evaluation, we identified 14 papers as the best contributions and sent them to eight external reviewers. The full review process resulted in a selection of the four best papers, which were then approved by consensus by the IMIA Yearbook Editorial Board. Although the imaging informatics sub-search returned all of these four papers, one is about sensorless freehand 3D ultrasound recon-struction (representing sensors), and another deals with video-based pulse rate estimation (representing signals).</p><p><strong>Conclusions: </strong>Sensors, signals, and imaging informatics is a dynamic field of intensive research. The four best papers in 2023 represent advanced approaches focusing on DL-based image processing, analysis, and indicate a shift in the research field from sensor technology development to biosignal and image analysis.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"277-279"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Behavioral Components and Their Tailoring in Participatory Health Interventions for Precision Prevention.","authors":"Kerstin Denecke, Octavio Rivera Romero, Carlos Luis Sanchez Bocanegra, Talya Miron-Shatz, Rolf Wynn","doi":"10.1055/s-0044-1800715","DOIUrl":"10.1055/s-0044-1800715","url":null,"abstract":"<p><strong>Objective: </strong>To study which behavioral components are implemented within participatory health interventions for precision prevention, specifically how they are realized as part of the interventions and how the tailoring of the interventions is implemented.</p><p><strong>Methods: </strong>We selected three case studies of participatory health interventions for precision prevention for three different target groups (children, parents, older adults with chronic conditions). One author with a background in psychology mapped the interventions and the digital functionalities to the 9 intervention functions of the behavioral change wheel (education, persuasion, incentivisation, coercion, training, enablement, modeling, environmental restructuring, restrictions).</p><p><strong>Results: </strong>While the intervention functions persuasion, incentivisation, education, modeling and coercion are implemented in all three interventions under considerations, two techniques (restrictions, and environmental restructuring) were not implemented in any of the three solutions. Training was only applied in one application and enablement in two interventions. We identified significant evidence gaps in both the tailoring process and the effectiveness of behavior change techniques in precision prevention.</p><p><strong>Conclusion: </strong>We conclude that there is a need for more focused studies on the effects of behavior interventions functions in digital health interventions and for design guidelines to improve these interventions for personalized health outcomes, thereby advancing precision prevention in digital health.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"25-31"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020619/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143811837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating Information Technology-enabled Precision Prevention Initiatives in Health and Care.","authors":"Kathrin Cresswell, Michael Rigby, Stephanie Medlock, Mirela Prgomet, Elske Ammenwerth","doi":"10.1055/s-0044-1800719","DOIUrl":"10.1055/s-0044-1800719","url":null,"abstract":"<p><p>Information technology-enabled precision prevention is a relatively new approach designed to improve population health. It forms an organic development linking principles of optimizing added value from health-related information technology and data systems with clinical aspirations to add longer-term problem prevention to immediate illness treatment. It includes drawing on information technology to identify persons at risk for developing certain conditions and then developing targeted behavioral and psychosocial approaches to modifying the behaviors of individuals or specific groups. We here discuss evaluation challenges associated with information technology-enabled precision prevention approaches to facilitate the development of an empirical evidence base. Challenges associated with measuring the impact of information technology-enabled precision prevention initiatives include considerations surrounding the relevance and fit of external data sources, the accuracy of prediction models, establishing added benefits of preventative activities, measuring pre-post outcomes at individual and population levels, and considerations surrounding cost-benefit analysis. Challenges associated with assessing processes of information technology-enabled precision prevention initiatives include the quality of data used to create underlying data models, exploring processes not necessarily related to each other, evolving social and environmental determinants of health and individual circumstances, the evolving nature of needs and interventions over time, and ethical considerations. If these challenges are attended to in evaluation activities, this will help to ensure that information technology-enabled approaches to precision prevention will have a positive impact on individual and population health.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"58-63"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Consumer Health Informatics to Advance Precision Prevention.","authors":"Oliver J Canfell, Leanna Woods, Deborah Robins, Clair Sullivan","doi":"10.1055/s-0044-1800735","DOIUrl":"10.1055/s-0044-1800735","url":null,"abstract":"<p><strong>Objective: </strong>Consumer health informatics (CHI) has the potential to disrupt traditional but unsustainable break-fix models of healthcare and catalyse precision prevention of chronic disease - a preventable global burden. This perspective article reviewed how consumer health informatics can advance precision prevention across four research and practice areas: (1) public health policy and practice (2) individualised disease risk assessment (3) early detection and monitoring of disease (4) tailored intervention of modifiable health determinants.</p><p><strong>Methods: </strong>We review and narratively synthesise methods and published recent (2018 onwards) research evidence of interventional studies of consumer health informatics for precision prevention. An analysis of research trends, ethical considerations, and future directions is presented as a guide for consumers, researchers, and practitioners to collectively prioritise advancing two interlinked fields towards high-quality evidence generation to support practice translation. A health consumer co-author provided critical review at all stages of manuscript preparation, moderating the allied health, medical and nursing researcher perspectives represented in the authorship team.</p><p><strong>Results: </strong>Precision prevention of chronic disease is enabled by consumer health informatics methods and interventions in population health surveillance using real-world data (e.g., genomics) (public health policy and practice), disease prognosis (regression modelling, machine learning) (individualized disease risk assessment), wearable devices and mobile health (mHealth) applications that generate digital phenotypes (early detection and monitoring), and targeted behaviour change interventions based upon personalized risk algorithms (tailored intervention of modifiable health determinants). In our disease case studies, there was mixed evidence for the effectiveness of consumer health informatics to improve risk-stratified or behavioural prevention-related health outcomes. Research trends comprise both consumer-centred and healthcare-centred innovations, with emphasis on inclusive design methodologies, social licence of health data use, and federated learning to preserve data sovereignty and maximise cross-jurisdictional analytical power.</p><p><strong>Conclusions: </strong>Together, CHI and precision prevention represent a potential future vanguard in shifting from traditional and inefficient break-fix to predict-prevent models of healthcare. Meaningful researcher, practitioner, and consumer partnerships must focus on generating high-quality evidence from methodologically robust study designs to support consumer health informatics as a core enabler of precision prevention.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"149-157"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision Prevention through Social Media: Report of Four Cases.","authors":"Elia Gabarron, Guillermo Lopez-Campos, Shauna Davies, Taridzo Chomutare, Iris Thiele Isip Tan, Carolyn Petersen","doi":"10.1055/s-0044-1800718","DOIUrl":"10.1055/s-0044-1800718","url":null,"abstract":"<p><strong>Background: </strong>Precision prevention involves using biological, behavioral, socioeconomic, and epidemiological data to improve health for a particular individual or group. With almost 63% of the global population using social media, these platforms show promise to deliver tailored messaging and personalized interventions to individuals.</p><p><strong>Objectives: </strong>To describe the personalization elements and behavior components used in a sample of precision prevention interventions delivered through social media.</p><p><strong>Methods: </strong>To identify examples of cases, a search was done on clinicaltrials.gov, searching for 'other terms: prevention' + 'Intervention/Treatment: social media intervention' + 'study results: With results. The selected cases were described, personalization elements reported, and their adopted intervention components were coded according to the Behavior Change Wheel (BCW) framework.</p><p><strong>Results: </strong>A total of four cases employing personalization in their interventions were identified. Three of these cases targeted women's health. The intervention period varied from two to eight months, with participant commitment ranging from active involvement on five out of seven days to monthly participation. The BCW interventions of persuasion and incentivization, were most frequently utilized, while education and coercion were used sparingly in the selected cases. Notably, none of the four cases reported the use of training, restrictions, or modeling.</p><p><strong>Conclusions: </strong>Social media has the potential to serve as a tool for digital phenotyping and contribute to the advancement of precision prevention. Challenges include the social media platform set-up and ensuring all ethical considerations are met.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"52-57"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision in Prevention and Health Surveillance: How Artificial Intelligence May Improve the Time of Identification of Health Concerns through Social Media Content Analysis.","authors":"Pascal Staccini, Annie Y S Lau","doi":"10.1055/s-0044-1800736","DOIUrl":"10.1055/s-0044-1800736","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To explore how artificial intelligence (AI) methodologies, particularly through the analysis of social media content, can enhance \"precision in prevention and health surveillance\" (2024 Yearbook topic). The focus is on leveraging advanced data analytics to improve the timeliness and accuracy of identifying emerging health concerns, thus enabling more proactive and effective health interventions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A comprehensive literature search strategy was conducted on PubMed, focusing on papers published in 2023 related to consumer health informatics, precision prevention, and the intersection with social media. The search aimed to identify studies that utilized AI and machine learning techniques to analyse social media data for health surveillance purposes. Bibliometric analyses were applied to the retrieved articles, and tools such as \"Bibliometrix\" were used to assess keyword frequencies, co-occurrence networks, and thematic maps. The studies were then independently reviewed and screened for relevance, with a final selection of 10 articles made based on their alignment with the 2024 Yearbook topic and their methodological innovation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The analysis of 89 articles revealed several key themes and findings. Social media data offers a rich source of real-time insights into public health trends, and encompasses diverse demographic groups. AI methodologies, including machine learning, natural language processing (NLP), and deep learning, play a crucial role in extracting and analysing health-related information from social media content. The integration of AI in health surveillance can provide early warnings of potential health crises, as demonstrated by studies on topics such as suicide prevention, mental health, and the impact of social media use on e-cigarette consumption among youth. Ethical and privacy considerations are paramount, necessitating robust data anonymization and transparent data handling practices. Advanced AI techniques, such as transformer-based topic modelling and federated learning, enhance the precision and security of health surveillance systems. The document highlights several case studies that demonstrate the practical applications of AI in health surveillance, such as monitoring public discussions about delta-8 THC and assessing suicide-related tweets and their association with help-seeking behaviour in the US.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;Integrating AI and social media content analysis in precision prevention and health surveillance has significant potential to improve public health outcomes. By leveraging real-time, comprehensive data from social media platforms, AI can enhance the timeliness and accuracy of identifying health concerns. Addressing ethical and privacy challenges is crucial to ensure responsible and effective implementation. The continuous advancement of AI technologies will play a critical role in safeguarding public hea","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"158-165"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Latest Advances in Public Health and Epidemiology Informatics.","authors":"Gayo Diallo, Georgeta Bordea, Cécilia Samieri","doi":"10.1055/s-0044-1800754","DOIUrl":"10.1055/s-0044-1800754","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this review was to identify and analyze the most recent research and prevailing trends in the field of Public Health and Epidemiology Informatics (PHEI).</p><p><strong>Methods: </strong>We adopted a methodical search approach that was similar to the one used in the previous edition of the PHEI section's synopsis. We conducted a thorough search on PubMed using an extensive range of keywords that cover topics related to public health, epidemiological surveillance, and medical informatics. As a result, there were 840 publications found on PHEI. The three section editors carefully examined the references. Afterwards, nine articles were selected as potential contenders for the \"best paper\" awards. The candidates underwent a thorough peer-review process that included six external reviewers, as well as the section editors and the two chief editors of the IMIA Yearbook of Medical Informatics. Every paper was subjected to a total of five reviews.</p><p><strong>Results: </strong>The search yielded 840 references, and after review of the nine \"best paper\" candidates, only two papers emerged as strong contenders for the \"best paper\" award. The first candidate paper, which received a broader consensus, explored the integration of clinical language models in medicine. This model envisioned working alongside physicians, providing real-time guidance at the point of care. The second candidate fo-cused on developing personalized digital interventions to effectively increase short-term physical activity.</p><p><strong>Conclusion: </strong>The recent PHEI section review has identified a significant rise in the quantity of pertinent stud-ies in comparison to the previous edition. The search strategy for this year incorporated precision medicine-related keywords for the first time, which may have led to an increased number of retrieved publications specifically related to PHEI.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"262-264"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Primary Care EHR data on Social Determinants of Health: Quality and Fitness for Purpose in Precision/Personalised Medicine.","authors":"Anthony Paulo Sunjaya, Myron Anthony Godinho, Jitendra Jonnagaddala, Craig Kuziemsky, Karen Tu, Rafiqul Islam, Tasuku Okui, Naoki Nakashima, Javier Silva-Valencia, Leonardo Rojas-Mezarina, Alvin Marcelo, Sabrina Wong Kay Wye, Chien-Yeh Hsu, Uy Hoang, Jack Westfall, Simon de Lusignan, Siaw-Teng Liaw","doi":"10.1055/s-0044-1800716","DOIUrl":"10.1055/s-0044-1800716","url":null,"abstract":"<p><strong>Introduction: </strong>Precision and personalised medicine requires comprehensive genetic, epigenetic, lifestyle, social, community and environmental knowledge of the patient. This approach highlights the importance of the social determinants of health (SDoH), described by the World Health Organization (WHO) as 'the non-medical factors that influence health outcomes, the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life such as economic policies and systems, development agendas, social norms, social policies and political systems'.</p><p><strong>Methods: </strong>This study examined if countries collect SDoH indicators and, if they do, the quality of the data and whether they are fit for clinical and population health purposes. The sources of data were EHR networks and, where not available, national data collections.</p><p><strong>Results: </strong>While demographic details (age, gender) and rurality were well documented in most countries, we found that data availability and quality for education, occupation, income, socio-economic status, and residential care varied considerably between countries. Data for smoking, obesity, alcohol use, mental health, and substance use were generally poorly recorded.</p><p><strong>Conclusion: </strong>Recommendations include a universal set of indicators and taxonomy for SDoH; common data model and metadata standards for national and global harmonisation and monitoring; benchmarks for data quality and fitness-for-purpose; capacity building at national and subnational levels in data collection, data analysis, communication and dissemination of results; ethical and transparent data stewardship; and governance, leadership and diplomacy across multiple sectors to co-create an enabling policy and regulatory environment.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"32-44"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Research Informatics: a Decade-in-Review.","authors":"Christel Daniel, Peter J Embí","doi":"10.1055/s-0044-1800732","DOIUrl":"10.1055/s-0044-1800732","url":null,"abstract":"<p><strong>Background: </strong>Clinical Research Informatics (CRI) is a subspeciality of biomedical informatics that has substantially matured during the last decade. Advances in CRI have transformed the way clinical research is conducted. In recent years, there has been growing interest in CRI, as reflected by a vast and expanding scientific literature focused on the topic. The main objectives of this review are: 1) to provide an overview of the evolving definition and scope of this biomedical informatics subspecialty over the past 10 years; 2) to highlight major contributions to the field during the past decade; and 3) to provide insights about more recent CRI research trends and perspectives.</p><p><strong>Methods: </strong>We adopted a modified thematic review approach focused on understanding the evolution and current status of the CRI field based on literature sources identified through two complementary review processes (AMIA CRI year-in-review/IMIA Yearbook of Medical Informatics) conducted annually during the last decade.</p><p><strong>Results: </strong>More than 1,500 potentially relevant publications were considered, and 205 sources were included in the final review. The review identified key publications defining the scope of CRI and/or capturing its evolution over time as illustrated by impactful tools and methods in different categories of CRI focus. The review also revealed current topics of interest in CRI and prevailing research trends.</p><p><strong>Conclusion: </strong>This scoping review provides an overview of a decade of research in CRI, highlighting major changes in the core CRI discoveries as well as increasingly impactful methods and tools that have bridged the principles-to-practice gap. Practical CRI solutions as well as examples of CRI-enabled large-scale, multi-organizational and/or multi-national research projects demonstrate the maturity of the field. Despite the progress demonstrated, some topics remain challenging, highlighting the need for ongoing CRI development and research, including the need of more rigorous evaluations of CRI solutions and further formalization and maturation of CRI services and capabilities across the research enterprise.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"33 1","pages":"127-142"},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12020646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143812342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}