Yearbook of medical informatics最新文献

Objectives: To summarize the recent literature and research and present a selection of the best papers published in 2021 related to health information exchange (HIE).

Methods: A systematic review of the literature was performed by the two section editors with the help of a medical librarian. We searched bibliographic databases for HIE-related papers using both MeSH headings and keywords in titles and abstracts. A shortlist of candidate 15 best papers was first selected by section editors before being peer-reviewed by independent external reviewers.

Results: Major themes of the set of 15 articles included the issues to be addressed in building and maintaining HIEs, HIE implementation barriers and facilitators, and the outcomes of using HIEs. The outcomes of using HIE encompassed the impact on patient care and the ability of HIEs to provide a repository of data for further research.

Conclusions: The growth of HIE has followed a course very similar to the growth of electronic health records (EHRs). Initial foci of research included technical issues in the deployment, followed by research on barriers to use. Now that EHRs are more widely implemented and used, the newer research involves the use of the electronic data contained in them. Although HIEs are currently at an earlier stage of maturity and development than EHRs and most of the articles in this review focused on implementation barriers, we have seen the beginning of research on the large amount of longitudinal and diverse data that HIEs can make available. As the implementation and use of HIEs continue to increase and become more widely deployed, we can expect that research about HIE and leveraging HIEs and the data they collect, will continue to increase.

Objectives: Patients' experiences are increasingly gaining interest in multiple research fields. Researchers have applied various approaches to studying patient experience (PX); however, there is no commonly agreed-upon definition of PX. This scoping review focuses on PX from an eHealth perspective. Our aim was to: 1) describe how PX has been defined, 2) investigate which factors influencing PX and components of PX have been identified and researched, 3) explore the methods used in studying PX, and 4) find out the recent trends in PX research from an eHealth perspective.

Methods: We selected six major journals covering the fields of health informatics, PX, and nursing informatics. Using the search terms "patient experience" and technology-related terms (e.g., digital, eHealth), we searched for articles published between 2019 and 2021. From 426 articles, 44 were included in the analysis.

Results: Multiple concepts and meanings are used to refer to PX. Few articles include vague descriptions of the concept. Numerous eHealth factors are influencing PX, as well as components considering PX. The influencing factors were related to eHealth solutions' type and quality, and care process, when the components of PX were related to communication, remote interaction, risks and concerns, and patients' attitudes towards telehealth. Surveys were the main method used to study PX, followed by interviews.

Conclusions: PX is a complex and multifaceted phenomenon, and it is described as a synonym for patient satisfaction and telehealth experiences. Further multidisciplinary research is needed to understand PX as a phenomenon and to outline a framework for the research.

Objectives: Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion.

Methods: We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years.

Results: We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine.

Conclusion: Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all.

Objectives: Provide a systematic review of literature pertaining to health information exchange (HIE) since 2018. Summarize HIE-associated literature for most frequently occurring topics, as well as within the context of the COVID-19 pandemic and health equity. Finally, provide recommendations for how HIE can advance the vision of a digital healthcare ecosystem.

Methods: A computer program was developed to mediate a literature search of primary literature indexed in MEDLINE that was: (1) indexed with "Health Information Exchange" MeSH descriptor as a major topic; and (2) published between January 2018 and December 2021. Frequency of MeSH descriptors was then used to identify and to rank topics associated with the retrieved literature. COVID-19 literature was identified using the general COVID-19 PubMed Clinical Query filter. Health equity literature was identified using additional MeSH descriptor-based searches. The retrieved literature was then reviewed and summarized.

Results: A total of 256 articles were retrieved and reviewed for this survey. The major thematic areas summarized were: (1) Information Dissemination; (2) Delivery of Health Care; (3) Hospitals; (4) Hospital Emergency Service; (5) COVID-19; (6) Health Disparities; and (7) Computer Security and Confidentiality. A common theme across all areas examined for this survey was the maturity of HIE to support data-driven healthcare delivery. Recommendations were developed based on opportunities identified across the reviewed literature.

Conclusions: HIE is an essential advance in next generation healthcare delivery. The review of the recent literature (2018-2021) indicates that successful HIE improves healthcare delivery, often resulting in improved health outcomes. There remain major opportunities for expanded use of HIE, including the active engagement of clinical and patient stakeholders. The maturity of HIE reflects the maturity of the biomedical informatics and health data science fields.

Background: Without specific attention to health equity considerations in design, implementation, and evaluation, the rapid expansion of digital health approaches threatens to exacerbate rather than ameliorate existing health disparities.

Methods: We explored known factors that increase digital health inequity to contextualize the need for equity-centered informatics. This work used a narrative review method to summarize issues about inequities in digital health and to discuss future directions for researchers and clinicians. We searched literature using a combination of relevant keywords (e.g., "digital health", "health equity", etc.) using PubMed and Google Scholar.

Results: We have highlighted strategies for addressing medical marginalization in informatics according to vectors of power such as race and ethnicity, gender identity and modality, sexuality, disability, housing status, citizenship status, and criminalization status.

Conclusions: We have emphasized collaboration with user and patient groups to define priorities, ensure accessibility and localization, and consider risks in development and utilization of digital health tools. Additionally, we encourage consideration of potential pitfalls in adopting these diversity, equity, and inclusion (DEI)-related strategies.

Objectives: In this synopsis, we identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2021.

Methods: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and imaging informatics. Except for the sensor section, we only consider papers that have been published in journals providing at least three articles in the query response. Using a three-point Likert scale (1=not include, 2=maybe include, and 3=include), we reviewed the titles and abstracts of all database returns. Only those papers which reached two times three points were further considered for full paper review using the same Likert scale. Again, we only considered works with two times three points and provided these for external reviews. Based on the external reviews, we selected three best papers, as it happens that the three highest ranked papers represent works from all three parts of this section: sensors, signals, and imaging informatics.

Results: The search for papers was executed in January 2022. After removing duplicates and conference proceedings, the query returned a set of 88, 376, and 871 papers for sensors, signals, and imaging informatics, respectively. For signals and images, we filtered out journals that had less than three papers in the query results, reducing the number of papers to 215 and 512, respectively. From this total of 815 papers, the section co-editors identified 35 candidate papers with two times three Likert points, from which nine candidate best papers were nominated after full paper assessment. At least three external reviewers then rated the remaining papers and the three best-ranked papers were selected using the composite rating of all external reviewers. By accident, these three papers represent each of the three fields of sensor, signal, and imaging informatics. They were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Deep and machine learning techniques are still a dominant topic as well as concepts beyond the state-of-the-art.

Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intense research. Current research focuses on creating and processing heterogeneous sensor data towards meaningful decision support in clinical settings.

Objectives: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2021.

Methods: Following the International Medical Informatics Association (IMIA) Yearbook guidelines, a comprehensive and standardized review of the biomedical informatics literature was performed to select the best KRM papers published in 2021, based on PubMed queries.

Results: A total of 1,231 publications were retrieved from PubMed. We nominated 15 candidate best papers, and four of them were finally selected as the best papers in the KRM section. The topics covered by these papers include knowledge graph, ontology development, ontology alignment, and the International Classification of Diseases.

Conclusion: In the KRM best paper selection for 2021, the candidate best papers covered a wider spectrum of topics compared to the last year's significant focus on ontology curation. In particular, ontology development for specific domains (e.g., Alzheimer's disease, infectious diseases, bioethics) has received the most attention.

Objectives: Analyze the content of publications within the medical natural language processing (NLP) domain in 2021.

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: Four best papers have been selected in 2021. We also propose an analysis of the content of the NLP publications in 2021, all topics included.

Conclusions: The main issues addressed in 2021 are related to the investigation of COVID-related questions and to the further adaptation and use of transformer models. Besides, the trends from the past years continue, such as information extraction and use of information from social networks.