Yearbook of medical informatics最新文献

Objective: To identify notable research contributions relevant to digital health applications for precision prevention published in 2023.

Methods: An extensive search was conducted to identify peer-reviewed articles published in 2023 that examined ways that informatics approaches and digital health applications could facilitate precision prevention. The selection process comprised three steps: 1) candidate best papers were first selected by the two section editors; 2) a diverse, international group of external informatics subject matter experts reviewed each candidate best paper; and 3) the final selection of four best papers was conducted by the editorial committee of the Yearbook. The section editors attempted to balance selection by authors' global region and areas with clinical medicine and public health.

Results: Selected best papers represent studies that advanced knowledge surrounding the use of digital health applications to facilitate precision prevention. In general, papers identified in the search fell into one of the following categories: 1) applications in precision nutrition; 2) applications in precision medicine; and 3) applications in precision public health. The best papers spanned several disease targets, including Alzheimer's disease, HIV, and COVID-19. Several candidate papers sought to improve prediction of disease onset, whereas others focused on predicting response to interventions.

Conclusion: Although the selected papers are notable, significant work is needed to realize the full potential for precision prevention using digital health. Current data and applications only scratch the surface of the potential that information technologies can bring to support primary and secondary prevention in support of health and well-being for all populations globally.

Objectives: In this paper, we discuss current trends in consumer-mediated health information exchange (HIE) within the U.S. and globally, including new approaches, relevant standards that support HIE and interoperability centered around the patient, remaining challenges, and potential future directions.

Methods: We conducted a narrative review of the peer-reviewed and gray literature to characterize the current HIE landscape in relation to patient-centered data. Our searches targeted literature in three key areas related to consumer-mediated HIE: policy and initiatives, standards, and the technology landscape.

Results: We discuss current trends in consumer-mediated exchange within the U.S. and globally, focusing on policies, standards, and technology that support information exchange centered around the patient. We also outline remaining challenges and potential future directions.

Conclusions: The current landscape in the U.S. and globally supports a more patient-centered care model. Ongoing advances in technology and data standards provide the technical infrastructure to empower consumers to electronically exchange their information with different stakeholders in ways not possible just a few years ago. These advancements hold great promise for patients to play a more central role in sharing their information in support of more patient-centered care. Additional research and analyzes along with public policies are needed.

Objectives: To review publications in the field of Human Factors and Organisational Issues (HF&OI) in the year 2023 and to assess major contributions to the subject.

Methods: A bibliographic search was conducted following further refinement of standardized queries used in previous years. Sources used were PubMed, Web of Science, and referral via references from other papers. The search was carried out in February 2024, and (using the PubMed article type inclusion functionality) included clinical trials, meta-analyses, randomized controlled trials, reviews, case reports, classical articles, clinical studies, observational studies, comparative studies, and pragmatic clinical trials.

Results: Among the 513 returned papers published in 2023 in the various areas of HF&OI, 87 were identified for full review that resulted in a shortlist of 12 finalists and finally three best papers from among these. As in previous years, topics showed development including increased use of Artificial Intelligence (AI) and digital health tools, advancement of methodological frameworks for implementation and evaluation as well as design, and trials of specific digital tools.

Conclusions: Recent literature in HF&OI continues to focus on both theoretical advances and practical deployment, with focus on areas of patient-facing digital health, methods for design and evaluation, and attention to implementation barriers.

Objective: Recent advances in the implementation of healthcare artificial intelligence (AI) have drawn attention toward design methods to address the impacts on workflow. Lesser known than human-centered design, Value Sensitive Design (VSD) is an established framework integrating values into conceptual, technical, and empirical investigations of technology. We sought to study the current state of the literature intersecting elements of VSD with practical applications of healthcare AI.

Methods: Using a modified VSD framework attentive to AI-specific values, we conducted a narrative review informed by PRISMA guidelines and assessed VSD elements across design and implementation case studies.

Results: Our search produced 819 articles that went through multiple rounds of review. Nine studies qualified for full-text review. Most of the studies focused on values for the individual or professional practice such as trust and autonomy. Attention to organizational (e.g., stewardship, employee well-being) and societal (e.g., equity, justice) values was lacking. Studies were primarily from the U.S. and Western Europe.

Conclusion: Future design studies might better incorporate components of VSD by considering larger domains, organizational and societal, in value identification and to bridge to design processes that are not just human-centered but value sensitive. The small number of heterogeneous studies underlines the importance of broader studies of elements of VSD to inform healthcare AI in practice.

Objectives: This synopsis gives insights into scientific publications from 2023 in Natural Language Processing for the biomedical domain. We present the process we followed to identify candidates for NLP's best papers and the two best papers of this year. We also analyze the current trends in the 2023 publications.

Methods: We queried two bibliographic databases (Medline and the ACL anthology) and refined the outputs through automatic scoring. We then manually shortlisted publications to review and selected candidate papers through an adjudication process. External reviewers assessed the interest of the 13 selected candidates. At last, the section editors chose the best NLP papers.

Results: We collected 2,148 papers published in 2023, of which two were the best and selected as part of this NLP synopsis. Both address language models and propose solutions for data augmenta-tion, domain-specific model adaptation, and model distillation. Work is done on social media con-tent and electronic health records, using deep learning approaches such as ChatGPT and large lan-guage models.

Conclusion: Trends from 2023 cover classical NLP tasks (information extraction, text categoriza-tion, sentiment analysis), existing topics from several years (medical education), mainstream applications (Chatbots, generative approaches), and specific issues (cancer, COVID-19, mental health). Specifically for COVID-19, current researches deal with post-COVID-19 conditions, and they explore the understanding of how this pandemic has been managed and welcomed by populations. In addition, due to language models, a few works have been done to process languages other than English, especially using language portability approaches.

Objectives: The emergence of large language models has resulted in a significant shift in informatics research and carries promise in clinical cancer care. Here we provide a narrative review of the recent use of large language models (LLMs) to support cancer care, prevention, and research.

Methods: We performed a search of the Scopus database for studies on the application of bidirectional encoder representations from transformers (BERT) and generative-pretrained transformer (GPT) LLMs in cancer care published between the start of 2021 and the end of 2023. We present salient and impactful papers related to each of these themes.

Results: Studies identified focused on aspects of clinical decision support (CDS), cancer education, and support for research activities. The use of LLMs for CDS primarily focused on aspects of treatment and screening planning, treatment response, and the management of adverse events. Studies using LLMs for cancer education typically focused on question-answering, assessing cancer myths and misconceptions, and text summarization and simplification. Finally, studies using LLMs to support research activities focused on scientific writing and idea generation, cohort identification and extraction, clinical data processing, and NLP-centric tasks.

Conclusions: The application of LLMs in cancer care has shown promise across a variety of diverse use cases. Future research should utilize quantitative metrics, qualitative insights, and user insights in the development and evaluation of LLM-based cancer care tools. The development of open-source LLMs for use in cancer care research and activities should also be a priority.

Objectives: To review recent literature on health information exchange (HIE), focusing on the policy approach of five case study nations: the United States of America, the United Kingdom, Germany, Israel, and Portugal, as well as synthesize lessons learned across countries and provide recommendations for future research.

Methods: A narrative review of each nation's HIE policy frameworks, current state, and future HIE strategy.

Results: Key themes that emerged include the importance of both central decision-making as well as local innovation, the multiple and complex challenges of broad HIE adoption, and the varying role of HIE across different national health system structures.

Conclusion: HIE is an increasingly important capability and policy priority as electronic health record (EHR) adoption becomes more common and care delivery is increasingly digitized. While all five case study nations have adopted some level of HIE, there are significant differences across their level of data sharing infrastructure and maturity, and each nation took a different policy approach. While identifying generalizable strategies across disparate international systems is challenging, there are several common themes across successful HIE policy frameworks, such as the importance of central government prioritization of data sharing. Finally, we make several recommendations for future research to expand the breadth and depth of the literature on HIE and guide future decision-making by policymakers and practitioners.

Objectives: Despite growing enthusiasm surrounding the utility of clinical informatics to improve cancer outcomes, data availability remains a persistent bottleneck to progress. Difficulty combining data with protected health information often limits our ability to aggregate larger more representative datasets for analysis. With the rise of machine learning techniques that require increasing amounts of clinical data, these barriers have magnified. Here, we review recent efforts within clinical informatics to address issues related to safely sharing cancer data.

Methods: We carried out a narrative review of clinical informatics studies related to sharing protected health data within cancer studies published from 2018-2022, with a focus on domains such as decentralized analytics, homomorphic encryption, and common data models.

Results: Clinical informatics studies that investigated cancer data sharing were identified. A particular focus of the search yielded studies on decentralized analytics, homomorphic encryption, and common data models. Decentralized analytics has been prototyped across genomic, imaging, and clinical data with the most advances in diagnostic image analysis. Homomorphic encryption was most often employed on genomic data and less on imaging and clinical data. Common data models primarily involve clinical data from the electronic health record. Although all methods have robust research, there are limited studies showing wide scale implementation.

Conclusions: Decentralized analytics, homomorphic encryption, and common data models represent promising solutions to improve cancer data sharing. Promising results thus far have been limited to smaller settings. Future studies should be focused on evaluating the scalability and efficacy of these methods across clinical settings of varying resources and expertise.

Objectives: To analyse the content of publications within the medical Natural Language Processing (NLP) domain in 2022.

Methods: Automatic and manual preselection of publications to be reviewed, and selection of the best NLP papers of the year. Analysis of the important issues.

Results: Three best papers have been selected. We also propose an analysis of the content of the NLP publications in 2022, stressing on some of the topics.

Conclusion: The main trend in 2022 is certainly related to the availability of large language models, especially those based on Transformers, and to their use by non-NLP researchers. This leads to the democratization of the NLP methods. We also observe the renewal of interest to languages other than English, the continuation of research on information extraction and prediction, the massive use of data from social media, and the consideration of needs and interests of patients.

Objectives: To select, present, and summarize the best papers in 2022 for the Knowledge Representation and Management (KRM) section of the International Medical Informatics Association (IMIA) Yearbook.

Methods: We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2022.

Results: We retrieved 1,847 publications from PubMed. We nominated 15 candidate best papers, and two of them were finally selected as the best papers in the KRM section. The topics covered by the candidate papers include ontology and knowledge graph creation, ontology applications, ontology quality assurance, ontology mapping standard, and conceptual model.

Conclusions: In the KRM best paper selection for 2022, the candidate best papers encompassed a broad range of topics, with ontology and knowledge graph creation remaining a considerable research focus.