Yearbook of medical informatics最新文献

{"title":"Cancer Informatics 2022: Real-World Data Yields Important Insights into the Conduct of Clinical Trials and Registries.","authors":"Jeremy L Warner, Michael K Rooney, Debra Patt","doi":"10.1055/s-0042-1742521","DOIUrl":"10.1055/s-0042-1742521","url":null,"abstract":"<p><strong>Objective: </strong>To summarize significant research contributions on cancer informatics published in 2021.</p><p><strong>Methods: </strong>An extensive search using PubMed/MEDLINE and Altmetric scores was conducted to identify the scientific contributions published in 2021 that address topics in cancer informatics. The selection process comprised three steps: (i) 15 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 two best papers was conducted by the editorial board of the IMIA Yearbook.</p><p><strong>Results: </strong>The two selected best papers demonstrate some of the promises and shortcomings of real-world data.</p><p><strong>Conclusion: </strong>Cancer informatics is a maturing subfield of biomedical informatics. Applications of informatics methods to real-world data are especially notable in 2021.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"131-134"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/b0/ee/10-1055-s-0042-1742521.PMC9719767.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10328992","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":"A Literature Review on Ethics for AI in Biomedical Research and Biobanking.","authors":"Michaela Kargl,&nbsp;Markus Plass,&nbsp;Heimo Müller","doi":"10.1055/s-0042-1742516","DOIUrl":"https://doi.org/10.1055/s-0042-1742516","url":null,"abstract":"<p><strong>Background: </strong>Artificial Intelligence (AI) is becoming more and more important especially in datacentric fields, such as biomedical research and biobanking. However, AI does not only offer advantages and promising benefits, but brings about also ethical risks and perils. In recent years, there has been growing interest in AI ethics, as reflected by a huge number of (scientific) literature dealing with the topic of AI ethics. The main objectives of this review are: (1) to provide an overview about important (upcoming) AI ethics regulations and international recommendations as well as available AI ethics tools and frameworks relevant to biomedical research, (2) to identify what AI ethics can learn from findings in ethics of traditional biomedical research - in particular looking at ethics in the domain of biobanking, and (3) to provide an overview about the main research questions in the field of AI ethics in biomedical research.</p><p><strong>Methods: </strong>We adopted a modified thematic review approach focused on understanding AI ethics aspects relevant to biomedical research. For this review, four scientific literature databases at the cross-section of medical, technical, and ethics science literature were queried: PubMed, BMC Medical Ethics, IEEE Xplore, and Google Scholar. In addition, a grey literature search was conducted to identify current trends in legislation and standardization.</p><p><strong>Results: </strong>More than 2,500 potentially relevant publications were retrieved through the initial search and 57 documents were included in the final review. The review found many documents describing high-level principles of AI ethics, and some publications describing approaches for making AI ethics more actionable and bridging the principles-to-practice gap. Also, some ongoing regulatory and standardization initiatives related to AI ethics were identified. It was found that ethical aspects of AI implementation in biobanks are often like those in biomedical research, for example with regards to handling big data or tackling informed consent. The review revealed current 'hot' topics in AI ethics related to biomedical research. Furthermore, several published tools and methods aiming to support practical implementation of AI ethics, as well as tools and frameworks specifically addressing complete and transparent reporting of biomedical studies involving AI are described in the review results.</p><p><strong>Conclusions: </strong>The review results provide a practically useful overview of research strands as well as regulations, guidelines, and tools regarding AI ethics in biomedical research. Furthermore, the review results show the need for an ethical-mindful and balanced approach to AI in biomedical research, and specifically reveal the need for AI ethics research focused on understanding and resolving practical problems arising from the use of AI in science and society.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"152-160"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/75/e4/10-1055-s-0042-1742516.PMC9719772.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10328997","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":"A Systematic Review of Automated Segmentation Methods and Public Datasets for the Lung and its Lobes and Findings on Computed Tomography Images.","authors":"Diedre Carmo,&nbsp;Jean Ribeiro,&nbsp;Sergio Dertkigil,&nbsp;Simone Appenzeller,&nbsp;Roberto Lotufo,&nbsp;Leticia Rittner","doi":"10.1055/s-0042-1742517","DOIUrl":"https://doi.org/10.1055/s-0042-1742517","url":null,"abstract":"<p><strong>Objectives: </strong>Automated computational segmentation of the lung and its lobes and findings in X-Ray based computed tomography (CT) images is a challenging problem with important applications, including medical research, surgical planning, and diagnostic decision support. With the increase in large imaging cohorts and the need for fast and robust evaluation of normal and abnormal lungs and their lobes, several authors have proposed automated methods for lung assessment on CT images. In this paper we intend to provide a comprehensive summarization of these methods.</p><p><strong>Methods: </strong>We used a systematic approach to perform an extensive review of automated lung segmentation methods. We chose Scopus, PubMed, and Scopus to conduct our review and included methods that perform segmentation of the lung parenchyma, lobes or internal disease related findings. The review was not limited by date, but rather by only including methods providing quantitative evaluation.</p><p><strong>Results: </strong>We organized and classified all 234 included articles into various categories according to methodological similarities among them. We provide summarizations of quantitative evaluations, public datasets, evaluation metrics, and overall statistics indicating recent research directions of the field.</p><p><strong>Conclusions: </strong>We noted the rise of data-driven models in the last decade, especially due to the deep learning trend, increasing the demand for high-quality data annotation. This has instigated an increase of semi-supervised and uncertainty guided works that try to be less dependent on human annotation. In addition, the question of how to evaluate the robustness of data-driven methods remains open, given that evaluations derived from specific datasets are not general.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"277-295"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e0/68/10-1055-s-0042-1742517.PMC9719778.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10333644","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":"Informatics as Science.","authors":"Edward H Shortliffe","doi":"10.1055/s-0042-1742541","DOIUrl":"10.1055/s-0042-1742541","url":null,"abstract":"<p><p>The evolution of the informatics field, now with a well-accepted and crucial role in modern biomedicine and health care delivery, is the result of creative research over seven decades. The success is due in part to recognition that, throughout the process, investigators have documented not only what they have done but what they have learned, stimulating and guiding the next generation of projects. Such iterative experimentation, learning, sharing, and progressing is typical of all scientific disciplines. Yet progress depends on identifying key lessons, insights, and methods so that others can use them. This paper addresses the nature of scientific progress in informatics, recognizing that while the field is motivated by applications that can improve biomedicine and health, the scientific underpinnings must be identified and shared with others if the field is to progress optimally.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"303-306"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/e7/dc/10-1055-s-0042-1742541.PMC9719790.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10327770","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 Decision Support Systems: Contributions from 2021.","authors":"Damian Borbolla,&nbsp;Tiago K Colicchio","doi":"10.1055/s-0042-1742528","DOIUrl":"https://doi.org/10.1055/s-0042-1742528","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"199-201"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ab/43/10-1055-s-0042-1742528.PMC9719757.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10328998","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":"Towards an Interoperable Ecosystem of Research Cohort and Real-world Data Catalogues Enabling Multi-center Studies.","authors":"Morris Swertz,&nbsp;Esther van Enckevort,&nbsp;José Luis Oliveira,&nbsp;Isabel Fortier,&nbsp;Julie Bergeron,&nbsp;Nicolas H Thurin,&nbsp;Eleanor Hyde,&nbsp;Alexander Kellmann,&nbsp;Romin Pahoueshnja,&nbsp;Miriam Sturkenboom,&nbsp;Marianne Cunnington,&nbsp;Anne-Marie Nybo Andersen,&nbsp;Yannick Marcon,&nbsp;Gonçalo Gonçalves,&nbsp;Rosa Gini","doi":"10.1055/s-0042-1742522","DOIUrl":"https://doi.org/10.1055/s-0042-1742522","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>In this opinion review, we analyse catalogues for human subject research ranging from cohort studies to surveillance, administrative and healthcare records.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"262-272"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/7b/86/10-1055-s-0042-1742522.PMC9719789.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10333645","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":"2021 Bioinformatics and Translational Informatics Best Papers.","authors":"Mary Lauren Benton,&nbsp;Scott Patrick McGrath","doi":"10.1055/s-0042-1742538","DOIUrl":"https://doi.org/10.1055/s-0042-1742538","url":null,"abstract":"<p><strong>Objectives: </strong>To identify and summarize the top bioinformatics and translational informatics papers published in 2021 for the IMIA Yearbook.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"116-119"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/10/ef/10-1055-s-0042-1742538.PMC9719764.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10389660","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":"Equitable Research PRAXIS: A Framework for Health Informatics Methods.","authors":"Tiffany C Veinot,&nbsp;Phillipa J Clarke,&nbsp;Daniel M Romero,&nbsp;Lorraine R Buis,&nbsp;Tawanna R Dillahunt,&nbsp;Vinod V G Vydiswaran,&nbsp;Ashley Beals,&nbsp;Lindsay Brown,&nbsp;Olivia Richards,&nbsp;Alicia Williamson,&nbsp;Marcy G Antonio","doi":"10.1055/s-0042-1742542","DOIUrl":"https://doi.org/10.1055/s-0042-1742542","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"307-316"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/dd/69/10-1055-s-0042-1742542.PMC9719773.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10327772","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":"Electronic Health Record-Integrated Clinical Decision Support for Clinicians Serving Populations Facing Health Care Disparities: Literature Review.","authors":"Carole H Stipelman,&nbsp;Polina V Kukhareva,&nbsp;Elly Trepman,&nbsp;Quang-Tuyen Nguyen,&nbsp;Lourdes Valdez,&nbsp;Colleen Kenost,&nbsp;Maia Hightower,&nbsp;Kensaku Kawamoto","doi":"10.1055/s-0042-1742518","DOIUrl":"https://doi.org/10.1055/s-0042-1742518","url":null,"abstract":"<p><strong>Objectives: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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%).</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"184-198"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/67/1c/10-1055-s-0042-1742518.PMC9719761.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10328996","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.","authors":"Christel Daniel,&nbsp;Xavier Tannier,&nbsp;Dipak Kalra","doi":"10.1055/s-0042-1742530","DOIUrl":"https://doi.org/10.1055/s-0042-1742530","url":null,"abstract":"<p><strong>Objectives: </strong>To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2021.</p><p><strong>Method: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":40027,"journal":{"name":"Yearbook of medical informatics","volume":"31 1","pages":"161-164"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/55/a8/10-1055-s-0042-1742530.PMC9719780.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10333639","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}