Yearbook of medical informatics最新文献

Objectives: Existing individual-level human data cover large populations on many dimensions such as lifestyle, demography, laboratory measures, clinical parameters, etc. Recent years have seen large investments in data catalogues to FAIRify data descriptions to capitalise on this great promise, i.e. make catalogue contents more Findable, Accessible, Interoperable and Reusable. However, their valuable diversity also created heterogeneity, which poses challenges to optimally exploit their richness.

Methods: In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records.

Results: We observe that while these catalogues are heterogeneous, have various scopes, and use different terminologies, still the underlying concepts seem potentially harmonizable. We propose a unified framework to enable catalogue data sharing, with catalogues of multi-center cohorts nested as a special case in catalogues of real-world data sources. Moreover, we list recommendations to create an integrated community of metadata catalogues and an open catalogue ecosystem to sustain these efforts and maximise impact.

Conclusions: We propose to embrace the autonomy of motivated catalogue teams and invest in their collaboration via minimal standardisation efforts such as clear data licensing, persistent identifiers for linking same records between catalogues, minimal metadata 'common data elements' using shared ontologies, symmetric architectures for data sharing (push/pull) with clear provenance tracks to process updates and acknowledge original contributors. And most importantly, we encourage the creation of environments for collaboration and resource sharing between catalogue developers, building on international networks such as OpenAIRE and research data alliance, as well as domain specific ESFRIs such as BBMRI and ELIXIR.

Objectives: To identify and summarize the top bioinformatics and translational informatics papers published in 2021 for the IMIA Yearbook.

Methods: We performed a broad literature search to retrieve Bioinformatics and Translational Informatics (BTI) papers and coupled this with a series of editorial and peer reviews to identity the top papers in the area.

Results: We identified a final candidate list of 15 BTI papers for peer-review; from these candidates, the top three papers were chosen to highlight in this synopsis. These papers expand the integration of multi-omics data with electronic health records and use advanced machine learning approaches to tailor models to individual patients. In addition, our honorable mention paper foreshadows the growing impact of BTI research on precision medicine through the continued development of large clinical consortia.

Conclusion: In the top BTI papers this year, we observed several important trends, including the use of deep-learning approaches to analyse diverse data types, the development of integrative and web-accessible bioinformatics pipelines, and a continued focus on the power of individual genome sequencing for precision health.

Objectives: To summarize significant research contributions published in 2021 in the field of clinical decision support (CDS) systems and select the best papers for the Decision Support section of the International Medical Informatics Association (IMIA) Yearbook.

Methods: The authors searched the MEDLINE® database for papers focused on clinical decision support (CDS) systems. From search results, section editors established a list of candidate best papers, which were then peer-reviewed by at least three external reviewers. The IMIA Yearbook editorial committee selected the best papers on the basis of all reviews including the section editors' evaluation.

Results: A total of 337 articles were retrieved from which 13 candidate papers were identified. Finally, from the candidate papers, the top three papers were selected. The first paper introduces an innovative evaluation approach to CDS systems, the second compares six health institutions on how they are measuring CDS alert fatigue and the last one adds new evidence on how CDS can help to reduce unnecessary interventions.

Objectives: There is growing attention to health equity in health informatics research. However, the literature lacks a comprehensive framework outlining critical considerations for health informatics research with marginalized groups.

Methods: Literature review and experiences from nine equity-focused health informatics conducted in the United States and Canada. Studies focus on disparities related to age, disability or chronic illness, gender/sex, place of residence (rural/urban), race/ethnicity, sexual orientation, and socioeconomic status.

Results: We found four key equity-related methodological considerations. To assist informaticists in addressing equity, we contribute a novel framework to synthesize these four considerations: PRAXIS (Participation and Representation, Appropriate methods and interventions, conteXtualization and structural competence, Investigation of Systematic differences). Participation and representation refers to the necessity for meaningful participation of marginalized groups in research, to elevate the voices of marginalized people, and to represent marginalized people as they are comfortable (e.g., asset-based versus deficit-based). Appropriate methods and interventions mean targeting methods, instruments, and interventions to reach and engage marginalized people. Contextualization and structural competence mean avoiding individualization of systematic disparities and targeting social conditions that (re-)produce inequities. Investigation of systematic differences highlights that experiences of people marginalized according to specific traits differ from those not so marginalized, and thus encourages studying the specificity of these differences and investigating and preventing intervention-generated inequality. We outline guidance for operationalizing these considerations at four research stages.

Conclusions: This framework can assist informaticists in systematically addressing these considerations in their research in four research stages: project initiation; sampling and recruitment; data collection; and data analysis. We encourage others to use these insights from multiple studies to advance health equity in informatics.

Objectives: To review current studies about designing and implementing clinician-facing clinical decision support (CDS) integrated or interoperable with an electronic health record (EHR) to improve health care for populations facing disparities.

Methods: We searched PubMed to identify studies published between January 1, 2011 and October 22, 2021 about clinician-facing CDS integrated or interoperable with an EHR. We screened abstracts and titles and extracted study data from articles using a protocol developed by team consensus. Extracted data included patient population characteristics, clinical specialty, setting, EHR, clinical problem, CDS type, reported user-centered design, implementation strategies, and outcomes.

Results: There were 28 studies (36 articles) included. Most studies were performed at safety net institutions (14 studies) or Indian Health Service sites (6 studies). CDS tools were implemented in primary care outpatient settings in 24 studies (86%) for screening or treatment. CDS included point-of-care alerts (93%), order facilitators (46%), workflow support (39%), relevant information display (36%), expert systems (11%), and medication dosing support (7%). Successful outcomes were reported in 19 of 26 studies that reported outcomes (73%). User-centered design was reported during CDS planning (39%), development (32%), and implementation phase (25%). Most frequent implementation strategies were education (89%) and consensus facilitation (50%).

Conclusions: CDS tools may improve health equity and outcomes for patients who face disparities. The present review underscores the need for high-quality analyses of CDS-associated health outcomes, reporting of user-centered design and implementation strategies used in low-resource settings, and methods to disseminate CDS created to improve health equity.

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021.

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

Results: Among the 1,096 papers (published in 2021) returned by the search and in the scope of the various areas of CRI, the full review process selected three best papers. The first best paper describes an operational and scalable framework for generating EHR datasets based on a detailed clinical model with an application in the domain of the COVID-19 pandemics. The authors of the second best paper present a secure and scalable platform for the preprocessing of biomedical data for deep data-driven health management applied for the detection of pre-symptomatic COVID-19 cases and for biological characterization of insulin-resistance heterogeneity. The third best paper provides a contribution to the integration of care and research activities with the REDCap Clinical Data and Interoperability sServices (CDIS) module improving the accuracy and efficiency of data collection.

Conclusions: The COVID-19 pandemic is still significantly stimulating research efforts in the CRI field to improve the process deeply and widely for conducting real-world studies as well as for optimizing clinical trials, the duration and cost of which are constantly increasing. The current health crisis highlights the need for healthcare institutions to continue the development and deployment of Big Data spaces, to strengthen their expertise in data science and to implement efficient data quality evaluation and improvement programs.

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.