Yearbook of medical informatics最新文献

Artificial intelligence (AI) promises to revolutionize healthcare. Currently there is a proliferation of new AI applications that are being developed and beginning to be deployed across many areas in healthcare to streamline and make healthcare processes more efficient. In addition, AI has the potential to support personalized and customized precision healthcare by providing intelligent interaction with end users. However, to achieve the goal of precision AI issues and concerns related to the safety of AI, as with any new technology, must be addressed. In this article we first describe the link between AI and safety and then describe the relation of AI to the emerging study of technology-induced error. An overview of published safety issues that have been associated with introduction of AI are described and categorized. These include potential for error to arise from varied sources, including the data used to drive AI applications, and the design process of AI applications itself. In addition, lack of appropriate and rigorous testing and limited analysis of AI applications during procurement processes has also been reported. Recommendations for ensuring the safe adoption of AI technology in healthcare are discussed, focusing on the need for more rigorous testing and evaluation of AI applications, ranging from laboratory testing through to naturalistic evaluation. The application of such approaches will support safety and precision AI for a modern digital health system.

Introduction: Precision prevention has shown an upsurge in popularity among epidemiologists in both developed and developing countries in the past decade.

Objectives: Initially practiced in oncology, this approach is increasingly adopted in public health to guard against other common non-communicable diseases (NCDs), such as diabetes and cardiovascular diseases. It aims to tailor preventive measures according to each individual's unique characteristics, such as genomic data, socio-demographic features, environmental factors, and cultural background.

Methods: Healthcare information technologies, including telehealth and artificial intelligence (AI), have served as a vital catalyst in the expansion of this field in the past decade. Under this framework, real-time contemporaneous clinical data is collected via a wide range of digital health devices, such as telehealth monitors, wearables, etc., and then analyzed by AI or non-AI prediction models, which then generate preventive recommendations.

Results: The utilization of telehealth technologies in the precision prevention of cardiovascular diseases (CVDs) is a very illustrative application. This paper explores these topics as well as certain limitations and unintended consequences (UICs) and outlines telehealth as a core enabler of precision prevention as well as public health.

Objectives: Objective: Precision medicine uses individualized patient data, including genomic and social determinants of health SDoH), to provide optimized personalized patient treatment. In this scoping review, we summarize studies published in the last two years that reported on implementation of precision medicine in clinical decision support (CDS) related to precision medicine.

Methods: We searched PubMed for manuscripts published in 2022 and 2023 to retrieve publications that included CDS and precision medicine keywords and Mesh terms. We reviewed the abstracts and full texts to apply the inclusion criteria that the study must have described the implementation of precision medicine related CDS within electronic health records. We extracted the domain, type of data used in CDS, target population included in the implementation from the final set of included manuscripts.

Results: Our search retrieved 285 manuscripts and papers. Sixteen (16) papers met inclusion criteria after manual review of the full text. Eight of the reviewed papers studied the successful implementation of pharmacogenomics in CDS, four studies investigated the implementation of disease risk, and only one paper described the implementation of CDS integrating social determinants of health.

Conclusion: Our scoping review of recent literature highlighted several findings. Pharmacogenomics is the most implemented precision medicine intervention based on published studies. Few reports describing disease risk and polygenic risk scores were found and no study addressed CDS for continuous biometric monitoring. Despite the increasing attention to social determinants of health as a key predictor of health outcomes, only one CDS incorporating SDoH have been publicly reported. Regular updates to scoping reviews can investigate barriers to implementation and identify solutions.

Objectives: We aim to identify, select, and summarize the best papers published in 2023 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.

Methods: We performed PubMed queries and adhered to the IMIA Yearbook guidelines for conducting biomedical informatics literature review to select the best papers in KRM published in 2023.

Results: Our search yielded a total of 1,666 publications from PubMed. From these, we identified 15 papers as potential candidates for the best papers, and three of them were finally selected as the best papers in the KRM section. The candidate best papers covered three main topics: knowledge graph, knowledge interoperability, and ontology. Notably, two of the three selected best papers explored the potential of knowledge graph embeddings for predicting intensive care unit readmissions and measuring disease distances, respectively.

Conclusions: The selection process for the best papers in the KRM section for 2023 showcased a wide spectrum of topics, with knowledge graph embeddings emerging as a promising area for supporting machine learning applications in biomedicine.

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select the best papers published in 2023.

Methods: A bibliographic search using a combination of MeSH descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between the two section editors and the editorial team was organized to finally conclude on the selected three best papers.

Results: Among the 1,119 papers returned by the search, published in 2023, that were in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes the process undertaken in Germany, under the national Medical Informatics Initiative, to define and validate a provenance metadata framework to enable the interpretation including quality assessment of health data reused for research. The authors of the second-best paper present a methodology for the generation of computable phenotypes and the covariates associated with success rates in e-phenotype validation. The third-best presents a review of published and accessible tools that enable the assessment of health data quality through an automated process. This year's survey paper marks the tenth anniversary of the CRI section of the Yearbook by reviewing the dominant themes within CRI over the past decade and the major milestone innovations within this field.

Conclusions: The literature relevant to CRI in 2023 has largely been populated by publications that assess and enhance the reusability of health data for clinical research, in particular data quality assessment and metadata management.

Objectives: To summarize significant research contributions on cancer informatics published in 2023, an extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2023 that address topics in cancer. The selection process comprised three steps: (i) ten candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook.

Results: The two selected papers demonstrate advances in the clinical implementation of cancer informatics methodologies. Both studies highlight translation of informatics methodologies to improve cancer outcomes.

Conclusions: Cancer informatics is a maturing subfield of bioinformatics. As novel methodologies continue to emerge, further emphasis will be placed on rigorous clinical validation and real-world scalability of such solutions to positively impact patient outcomes.

Introduction: Citizens' access to personal health information and information on prescription medication, options to share personal health data, and how these personal health data are kept secure, are all important themes in health informatics and therefore elaborated upon in this paper.

Methods: The empirical data stems from a survey that examines citizens' temporal access to laboratory test results, options for sharing patient-generated health data (PGHD) with health providers, methods to obtain supplementary information on prescription medication, and security issues pertaining to national standards, education, and experienced breaches.

Results: Results are based on answers from representatives in the International Medical Informatics Association (IMIA) member countries (n=28). Data shows that citizens' online access to test results is possible as soon as they are available in ten countries whereas nine countries have no norm or standard. The most common ways to provide citizens with supplementary information on prescription medication is through package inserts from manufacturers or paper medication information from pharmacies. PGHD is shared primarily in print or by showing the device to the health provider. Regarding e-health security, most countries have national standards for the security, however, less than half of the IMIA representatives answer that health professionals are required training in the national standards. Lastly, 16 of the 28 answers reply that there has been leaks leading to unauthorized access to health data. Future research should focus on how to provide citizens access to lab results according to their needs and examine how to include digital PGHD meaningfully into clinical practice.

Introduction: Social Determinants of Health (SDoH) include factors such as economic stability, education, social and community context, healthcare access, and the physical environment, which shape an individual's health and well-being. Given that the inclusion of SDoH factors is essential in improving the quality and equity of digital health, this study aims to examine how SDoH is incorporated within digital health policies internationally.

Methods: An environmental scan of digital health policies was conducted, including relevant documents from multiple countries and global organizations. Key content related to SDoH was extracted from the documents, and a content analysis was conducted to identify seven different SDoH domains (i.e., target audience, SDoH inclusion, addressing health inequities, SDoH-related key performance indicators, data collection on SDoH, interoperability standards, and data privacy and security). Data were aggregated at the global and continental levels to integrate and synthesize information from different countries and regions.

Results: A total of 28 digital health policies or strategies were identified across 16 international regions. The comparative analysis of health policies regarding SDoH reveals a pronounced disparity between the continental regions. Although the World Health Organization recognizes the significance of key performance indicators for monitoring SDoH and emphasizes the assessment of national digital health maturity, there's a noticeable lack of continent-specific policies reflecting these global initiatives at the continental level.

Conclusion: While some regional digital health strategies recognize SDoH, integration varies, and standardization is lacking. Future research should focus on data collection frameworks and comprehensive insights for policymakers.

Objectives: This survey paper summarizes the recent trend of "Precision Prevention" in public health, focusing on significant developments in informatics to enable targeted prevention and improved public health.

Methods: Given relatively limited use of the term "Precision Prevention" in the literature to date, com-bined with significant developments in this space outside of peer reviewed literature, the topic was ill-suited for a systematic review approach. Instead, the co-authors used a narrative review approach, combining related search terms and complementary expertise to develop and refine sub-topics to be included. Each section was then written using a combination of prior knowledge and specific relevant search terms.

Results: The paper opens with an explanation of the term "precision prevention", including its origins and relationship to other concepts such as precision medicine. It then provides an overview of types of data relevant to precision prevention, as well as how those data are collected in different contexts and through different modalities. The authors then describe the HL7 Gravity Project, a multi-stakeholder public collaborative project aimed at data standardization in the social determinants space. Finally, the authors present how those data types are used across the spectrum from clinical care to target outreach for human services, to data-driven health policy.

Conclusions: Precision prevention, targeting the right intervention to the right population at the right time, is now recognized as of vital importance, particularly in light of the COVID-19 pandemic's spotlight on health disparities and societal consequences. Optimizing interventions targeted at different communities and populations will require novel and innovative collection, use, and dissemination of data, information, and knowledge. The talent and skills of the international informatics community are critical for success in this work.

Objectives: To select, present, and summarize cutting edge work in the field of Knowledge Representation and Management (KRM) published in 2022 and 2023.

Methods: A comprehensive set of KRM-relevant articles published in 2022 and 2023 was retrieved by querying PubMed. Topic modeling with Latent Dirichlet Allocation was used to further refine this query and suggest areas of focus. Selected articles were chosen based on a review of their title and abstract.

Results: An initial set of 8,706 publications were retrieved from PubMed. From these, fifteen papers were ultimately selected matching one of two main themes: KRM for long COVID, and KRM approaches used in combination with generative large language models.

Conclusions: This survey shows the ongoing development and versatility of KRM approaches, both to improve our understanding of a global health crisis and to augment and evaluate cutting edge technologies from other areas of artificial intelligence.