{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:42:36Z","timestamp":1775770956175,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100002957","name":"Technische Universit\u00e4t Dresden","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002957","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>To gain insight into the real-life care of patients in the healthcare system, data from hospital information systems and insurance systems are required. Consequently, linking clinical data with claims data is necessary. To ensure their syntactic and semantic interoperability, the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) from the Observational Health Data Sciences and Informatics (OHDSI) community was chosen. However, there is no detailed guide that would allow researchers to follow a generic process for data harmonization, i.e. the transformation of local source data into the standardized OMOP CDM format. Thus, the aim of this paper is to conceptualize a generic data harmonization process for OMOP CDM.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>For this purpose, we conducted a literature review focusing on publications that address the harmonization of clinical or claims data in OMOP CDM. Subsequently, the process steps used and their chronological order as well as applied OHDSI tools were extracted for each included publication. The results were then compared to derive a generic sequence of the process steps.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>From 23 publications included, a generic data harmonization process for OMOP CDM was conceptualized, consisting of nine process steps: dataset specification, data profiling, vocabulary identification, coverage analysis of vocabularies, semantic mapping, structural mapping, extract-transform-load-process, qualitative and quantitative data quality analysis. Furthermore, we identified seven OHDSI tools which supported five of the process steps.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>The generic data harmonization process can be used as a step-by-step guide to assist other researchers in harmonizing source data in OMOP CDM.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-024-02458-7","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T11:02:33Z","timestamp":1708945353000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Conceptual design of a generic data harmonization process for OMOP common data model"],"prefix":"10.1186","volume":"24","author":[{"given":"Elisa","family":"Henke","sequence":"first","affiliation":[]},{"given":"Michele","family":"Zoch","sequence":"additional","affiliation":[]},{"given":"Yuan","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Ines","family":"Reinecke","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Sedlmayr","sequence":"additional","affiliation":[]},{"given":"Franziska","family":"Bathelt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"issue":"Suppl 1","key":"2458_CR1","first-page":"e50","volume":"57","author":"SC Semler","year":"2018","unstructured":"Semler SC, Wissing F, Heyder R. German Medical Informatics Initiative - A National Approach To Integrating Health Data from Patient Care and Medical Research. Methods Inf Med. 2018;57(Suppl 1):e50\u20136.","journal-title":"Methods Inf Med"},{"issue":"26","key":"2458_CR2","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.1056\/NEJM200106283442611","volume":"344","author":"LA Green","year":"2001","unstructured":"Green LA, Fryer GE, Yawn BP, Lanier D, Dovey SM. The Ecology of Medical Care Revisited. N Engl J Med. 2001;344(26):2021\u20135.","journal-title":"N Engl J Med"},{"key":"2458_CR3","doi-asserted-by":"publisher","unstructured":"Thun S, Dewenter H. Syntaktische und semantische Interoperabilit\u00e4t. In: M\u00fcller-Mielitz S, Lux T, editors. E-Health-\u00d6konomie [Internet]. Wiesbaden: Springer Fachmedien; 2017 [cited 2023 Mar 14]. p. 669\u201382. https:\/\/doi.org\/10.1007\/978-3-658-10788-8_34.","DOI":"10.1007\/978-3-658-10788-8_34"},{"issue":"17","key":"2458_CR4","doi-asserted-by":"publisher","first-page":"8275","DOI":"10.3390\/app11178275","volume":"11","author":"G Kumar","year":"2021","unstructured":"Kumar G, Basri S, Imam AA, Khowaja SA, Capretz LF, Balogun AO. Data harmonization for heterogeneous datasets: a systematic literature review. Appl Sci. 2021;11(17):8275.","journal-title":"Appl Sci"},{"key":"2458_CR5","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.jbi.2016.10.016","volume":"64","author":"M Garza","year":"2016","unstructured":"Garza M, Del Fiol G, Tenenbaum J, Walden A, Zozus MN. Evaluating common data models for use with a longitudinal community registry. J Biomed Inf. 2016;64:333\u201341.","journal-title":"J Biomed Inf"},{"key":"2458_CR6","first-page":"95","volume":"283","author":"I Reinecke","year":"2021","unstructured":"Reinecke I, Zoch M, Reich C, Sedlmayr M, Bathelt F. The usage of OHDSI OMOP - A Scoping Review. Stud Health Technol Inf. 2021;283:95\u2013103.","journal-title":"Stud Health Technol Inf"},{"key":"2458_CR7","unstructured":"EHDEN. European Health Data & Evidence Network [Internet]. 2022 [cited 2022 May 20]. Available from: https:\/\/www.ehden.eu\/."},{"key":"2458_CR8","unstructured":"European Medical Agency. Data Analysis and Real World Interrogation Network (DARWIN EU) [Internet]. 2023 [cited 2022 May 20]. Available from: https:\/\/www.ema.europa.eu\/en\/about-us\/how-we-work\/big-data\/data-analysis-real-world-interrogation-network-darwin-eu."},{"key":"2458_CR9","unstructured":"Observational Health Data Sciences and Informatics. HL7 International and OHDSI Announce Collaboration to Provide Single Common Data Model for Sharing Information in Clinical Care and Observational Research [Internet]. 2021 [cited 2022 May 6]. Available from: https:\/\/www.ohdsi.org\/ohdsi-hl7-collaboration\/."},{"key":"2458_CR10","unstructured":"Observational Health Data Sciences and Informatics. The Book of OHDSI. In: The Book of OHDSI [Internet]. 2021 [cited 2022 Apr 19]. Available from: https:\/\/ohdsi.github.io\/TheBookOfOhdsi\/."},{"key":"2458_CR11","doi-asserted-by":"publisher","first-page":"104925","DOI":"10.1016\/j.ijmedinf.2022.104925","volume":"169","author":"Y Peng","year":"2023","unstructured":"Peng Y, Henke E, Reinecke I, Zoch M, Sedlmayr M, Bathelt F. An ETL-process design for data harmonization to participate in international research with German real-world data based on FHIR and OMOP CDM. Int J Med Informatics. 2023;169:104925.","journal-title":"Int J Med Informatics"},{"key":"2458_CR12","unstructured":"Digital Scholar. Zotero [Internet]. Zotero. 2023 [cited 2023 Mar 31]. Available from: https:\/\/www.zotero.org\/."},{"issue":"5","key":"2458_CR13","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"Fleiss JL. Measuring nominal scale agreement among many raters. Psychol Bull. 1971;76(5):378\u201382.","journal-title":"Psychol Bull"},{"key":"2458_CR14","unstructured":"Signorell A, Aho K, Alfons A, Anderegg N, Aragon T, Arachchige C et al. DescTools: Tools for Descriptive Statistics [Internet]. 2023 [cited 2023 Oct 25]. Available from: https:\/\/cran.r-project.org\/web\/packages\/DescTools\/index.html."},{"issue":"2","key":"2458_CR15","doi-asserted-by":"publisher","first-page":"e1230","DOI":"10.1002\/cl2.1230","volume":"18","author":"NR Haddaway","year":"2022","unstructured":"Haddaway NR, Page MJ, Pritchard CC, McGuinness LA. PRISMA2020: an R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and open synthesis. Campbell Syst Reviews. 2022;18(2):e1230.","journal-title":"Campbell Syst Reviews"},{"key":"2458_CR16","doi-asserted-by":"publisher","first-page":"b2700","DOI":"10.1136\/bmj.b2700","volume":"339","author":"A Liberati","year":"2009","unstructured":"Liberati A, Altman DG, Tetzlaff J, Mulrow C, G\u00f8tzsche PC, Ioannidis JPA, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.","journal-title":"BMJ"},{"key":"2458_CR17","doi-asserted-by":"crossref","unstructured":"Klann JG, Joss MAH, Embree K, Murphy SN. Data model harmonization for the all of Us Research Program: transforming i2b2 data into the OMOP common data model. PLoS ONE. 2019;14(2).","DOI":"10.1371\/journal.pone.0212463"},{"key":"2458_CR18","doi-asserted-by":"crossref","unstructured":"Lamer A, Abou-Arab O, Bourgeois A, Parrot A, Popoff B, Beuscart JB, et al. Development and Usability Study. J Med Internet Res. 2021;23(10):e29259. Transforming Anesthesia Data Into the Observational Medical Outcomes Partnership Common Data Model:.","DOI":"10.2196\/29259"},{"key":"2458_CR19","doi-asserted-by":"crossref","unstructured":"Hripcsak G, Shang N, Peissig PL, Rasmussen LV, Liu C, Benoit B et al. Facilitating phenotype transfer using a common data model. J Biomed Inform. 2019;96.","DOI":"10.1016\/j.jbi.2019.103253"},{"key":"2458_CR20","doi-asserted-by":"crossref","unstructured":"Yu Y, Jiang G, Brandt E, Forsyth T, Dhruva SS, Zhang S et al. Integrating real-world data to assess cardiac ablation device outcomes in a multicenter study using the OMOP common data model for regulatory decisions: implementation and evaluation. JAMIA OPEN. 2023;6(1).","DOI":"10.1093\/jamiaopen\/ooac108"},{"key":"2458_CR21","doi-asserted-by":"crossref","unstructured":"Papez V, Moinat M, Payralbe S, Asselbergs FW, Lumbers RT, Hemingway H et al. Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure. JAMIA OPEN. 2021;4(3).","DOI":"10.1093\/jamiaopen\/ooab001"},{"key":"2458_CR22","doi-asserted-by":"crossref","unstructured":"Tan HX, Teo DCH, Lee D, Kim C, Neo JW, Sung C, et al. Healthc Inf Res. 2022;28(2):112\u201322. Applying the OMOP Common Data Model to Facilitate Benefit-Risk Assessments of Medicinal Products Using Real-World Data from Singapore and South Korea.","DOI":"10.4258\/hir.2022.28.2.112"},{"key":"2458_CR23","doi-asserted-by":"crossref","unstructured":"Papez V, Moinat M, Voss EA, Bazakou S, Van Winzum A, Peviani A, et al. Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond. Volume 30. JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION; 2022. pp. 103\u201311. 1.","DOI":"10.1093\/jamia\/ocac203"},{"key":"2458_CR24","doi-asserted-by":"crossref","unstructured":"Jung H, Yoo S, Kim S, Heo E, Kim B, Lee HY, et al. Patient-level fall risk prediction using the Observational Medical Outcomes Partnership\u2019s Common Data Model. Volume 10. Pilot Feasibility Study. JMIR MEDICAL INFORMATICS; 2022. 3.","DOI":"10.2196\/35104"},{"key":"2458_CR25","doi-asserted-by":"crossref","unstructured":"Almeida JR, Silva JF, Matos S, Oliveira JL. A two-stage workflow to extract and harmonize drug mentions from clinical notes into observational databases. J Biomed Inform. 2021;120.","DOI":"10.1016\/j.jbi.2021.103849"},{"key":"2458_CR26","first-page":"233","volume-title":"MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL. NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM","author":"DM Lima","year":"2019","unstructured":"Lima DM, Rodrigues-Jr JF, Traina AJM, Pires FA, Gutierrez MA. Transforming two decades of ePR Data to OMOP CDM for Clinical Research. In: OhnoMachado L, Seroussi B, editors. MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL. NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM. Studies in Health Technology and Informatics. Volume 264. NETHERLANDS: IOS; 2019. pp. 233\u20137."},{"key":"2458_CR27","doi-asserted-by":"crossref","unstructured":"Ji H, Kim S, Yi S, Hwang H, Kim JW, Yoo S. Converting clinical document architecture documents to the common data model for incorporating health information exchange data in observational health studies: CDA to CDM. J Biomed Inform. 2020;107.","DOI":"10.1016\/j.jbi.2020.103459"},{"key":"2458_CR28","doi-asserted-by":"crossref","unstructured":"Kim JW, Kim S, Ryu B, Song W, Lee HY, Yoo S. Transforming electronic health record polysomnographic data into the Observational Medical Outcome Partnership\u2019s Common Data Model: a pilot feasibility study. Sci Rep. 2021;11(1).","DOI":"10.1038\/s41598-021-86564-w"},{"key":"2458_CR29","first-page":"24","volume":"11","author":"C Blacketer","year":"2021","unstructured":"Blacketer C, Voss EA, DeFalco F, Hughes N, Schuemie MJ, Moinat M, et al. Using the Data Quality Dashboard to improve the EHDEN Network. Appl SCIENCES-BASEL. 2021;11:24.","journal-title":"Appl SCIENCES-BASEL"},{"key":"2458_CR30","first-page":"94","volume-title":"HEALTH INFORMATICS MEETS EHEALTH: BIOMEDICAL MEETS EHEALTH - FROM SENSORS TO DECISIONS. NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM","author":"C Rinner","year":"2018","unstructured":"Rinner C, Gezgin D, Wendl C, Gall W. A Clinical Data Warehouse based on OMOP and i2b2 for Austrian Health Claims Data. In: Schreier G, Hayn D, editors. HEALTH INFORMATICS MEETS EHEALTH: BIOMEDICAL MEETS EHEALTH - FROM SENSORS TO DECISIONS. NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM. Studies in Health Technology and Informatics. Volume 248. NETHERLANDS: IOS; 2018. pp. 94\u20139."},{"key":"2458_CR31","doi-asserted-by":"crossref","unstructured":"Haberson A, Rinner C, Sch\u00f6berl A, Gall W. J Med Syst. 2019;43(10):314. Feasibility of Mapping Austrian Health Claims Data to the OMOP Common Data Model.","DOI":"10.1007\/s10916-019-1436-9"},{"issue":"4","key":"2458_CR32","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1055\/s-0041-1732301","volume":"12","author":"SMK Sathappan","year":"2021","unstructured":"Sathappan SMK, Jeon YS, Dang TK, Lim SC, Shao YM, Tai ES, et al. Transformation of Electronic Health Records and Questionnaire Data to OMOP CDM: a feasibility study using SG_T2DM dataset. Appl Clin Inf. 2021;12(4):757\u201367.","journal-title":"Appl Clin Inf"},{"key":"2458_CR33","unstructured":"Michael CL, Sholle ET, Wulff RT, Roboz GJ, Campion TR. Mapping Local Biospecimen Records to the OMOP Common Data Model. AMIA Jt Summits Transl Sci Proc. 2020;2020:422\u20139."},{"key":"2458_CR34","doi-asserted-by":"crossref","unstructured":"Hong N, Zhang N, Wu H, Lu S, Yu Y, Hou L, et al. Preliminary exploration of survival analysis using the OHDSI common data model: a case study of intrahepatic cholangiocarcinoma. Volume 18. BMC MEDICAL INFORMATICS AND DECISION MAKING; 2018. 5.","DOI":"10.1186\/s12911-018-0686-7"},{"issue":"1","key":"2458_CR35","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1055\/s-0039-3402754","volume":"11","author":"A Lamer","year":"2020","unstructured":"Lamer A, Depas N, Doutreligne M, Parrot A, Verloop D, Defebvre MM, et al. Transforming French Electronic Health Records into the Observational Medical Outcome Partnership\u2019s Common Data Model: a feasibility study. Appl Clin Inf. 2020;11(1):13\u201322.","journal-title":"Appl Clin Inf"},{"issue":"8","key":"2458_CR36","doi-asserted-by":"publisher","first-page":"1605","DOI":"10.1093\/jamia\/ocab108","volume":"28","author":"LA Lenert","year":"2021","unstructured":"Lenert LA, Ilatovskiy AV, Agnew J, Rudisill P, Jacobs J, Weatherston D, et al. Automated production of research data marts from a canonical fast healthcare interoperability resource data repository: applications to COVID-19 research. J Am Med Inf Assoc. 2021;28(8):1605\u201311.","journal-title":"J Am Med Inf Assoc"},{"key":"2458_CR37","doi-asserted-by":"publisher","first-page":"104437","DOI":"10.1016\/j.jbi.2023.104437","volume":"144","author":"S Kohler","year":"2023","unstructured":"Kohler S, Bosc\u00e1 D, K\u00e4rcher F, Haarbrandt B, Prinz M, Marschollek M, et al. Eos and OMOCL: towards a seamless integration of openEHR records into the OMOP Common Data Model. J Biomed Inf. 2023;144:104437.","journal-title":"J Biomed Inf"},{"key":"2458_CR38","doi-asserted-by":"publisher","first-page":"105144","DOI":"10.1016\/j.ijmedinf.2023.105144","volume":"177","author":"D Oniani","year":"2023","unstructured":"Oniani D, Parmanto B, Saptono A, Bove A, Freburger J, Visweswaran S, et al. ReDWINE: a clinical datamart with text analytical capabilities to facilitate rehabilitation research. Int J Med Inf. 2023;177:105144.","journal-title":"Int J Med Inf"},{"key":"2458_CR39","doi-asserted-by":"publisher","first-page":"e44547","DOI":"10.2196\/44547","volume":"11","author":"S Frid","year":"2023","unstructured":"Frid S, Pastor Duran X, Bracons Cuc\u00f3 G, Pedrera-Jim\u00e9nez M, Serrano-Balazote P, Mu\u00f1oz Carrero A, et al. An ontology-based Approach for consolidating patient data standardized with European Norm\/International Organization for standardization 13606 (EN\/ISO 13606) into Joint Observational Medical Outcomes Partnership (OMOP) repositories: description of a methodology. JMIR Med Inf. 2023;11:e44547.","journal-title":"JMIR Med Inf"},{"key":"2458_CR40","unstructured":"Observational Health Data Sciences and Informatics. White Rabbit [Internet]. 2022 [cited 2022 Nov 11]. Available from: http:\/\/ohdsi.github.io\/WhiteRabbit\/WhiteRabbit.html."},{"issue":"1","key":"2458_CR41","first-page":"1244","volume":"4","author":"MG Kahn","year":"2016","unstructured":"Kahn MG, Callahan TJ, Barnard J, Bauck AE, Brown J, Davidson BN, et al. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. EGEMS (Wash DC). 2016;4(1):1244.","journal-title":"EGEMS (Wash DC)"},{"key":"2458_CR42","unstructured":"Observational Health Data Sciences and Informatics. Achilles [Internet]. Observational Health Data Sciences and Informatics; 2022 [cited 2023 Apr 12]. Available from: https:\/\/github.com\/OHDSI\/Achilles."},{"issue":"10","key":"2458_CR43","doi-asserted-by":"publisher","first-page":"2251","DOI":"10.1093\/jamia\/ocab132","volume":"28","author":"C Blacketer","year":"2021","unstructured":"Blacketer C, Defalco FJ, Ryan PB, Rijnbeek PR. Increasing trust in real-world evidence through evaluation of observational data quality. J Am Med Inform Assoc. 2021;28(10):2251\u20137.","journal-title":"J Am Med Inform Assoc"},{"key":"2458_CR44","unstructured":"Observational Health Data Sciences and Informatics. Atlas [Internet]. Observational Health Data Sciences and Informatics; 2023 [cited 2023 Apr 12]. Available from: https:\/\/github.com\/OHDSI\/Atlas."},{"key":"2458_CR45","unstructured":"Observational Health Data Sciences and Informatics. Athena [Internet]. Athena\u2013 OHDSI Vocabularies Repository. 2023 [cited 2022 Nov 25]. Available from: https:\/\/athena.ohdsi.org\/."},{"key":"2458_CR46","unstructured":"Schuemie M. Usagi [Internet]. Usagi. 2021. Available from: http:\/\/ohdsi.github.io\/Usagi\/."},{"key":"2458_CR47","first-page":"138","volume-title":"DIGITAL PERSONALIZED HEALTH AND MEDICINE. NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM","author":"P Fischer","year":"2020","unstructured":"Fischer P, Stoehr MR, Gall H, Michel-Backofen A, Majeed RW. Data Integration into OMOP CDM for Heterogeneous Clinical Data collections via HL7 FHIR bundles and XSLT. In: PapeHaugaard L, Lovis C, Madsen I, Weber P, Nielsen P, Scott P, editors. DIGITAL PERSONALIZED HEALTH AND MEDICINE. NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM. Studies in Health Technology and Informatics. Volume 270. NETHERLANDS: IOS; 2020. pp. 138\u201342."},{"key":"2458_CR48","doi-asserted-by":"crossref","unstructured":"Biedermann P, Ong R, Davydov A, Orlova A, Solovyev P, Sun H et al. Standardizing registry data to the OMOP Common Data Model: experience from three pulmonary hypertension databases. BMC Med Res Methodol. 2021;21(1).","DOI":"10.1186\/s12874-021-01434-3"},{"key":"2458_CR49","unstructured":"Sentinel Initiative. Sentinel Common Data Model [Internet]. 2024 [cited 2024 Jan 11]. Available from: https:\/\/sentinelinitiative.org\/methods-data-tools\/sentinel-common-data-model."},{"key":"2458_CR50","unstructured":"i2b2 TranSMART Foundation. i2b2: Informatics for Integrating Biology & the Bedside [Internet]. 2024 [cited 2024 Jan 11]. Available from: https:\/\/www.i2b2.org\/."},{"key":"2458_CR51","unstructured":"The National Patient-Centered Clinical Research Network. The National Patient-Centered Clinical Research Network - Data [Internet]. The National Patient-Centered Clinical Research Network. 2024 [cited 2024 Jan 11]. Available from: https:\/\/pcornet.org\/data\/."}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02458-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-024-02458-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-024-02458-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T11:05:49Z","timestamp":1708945549000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-024-02458-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2458"],"URL":"https:\/\/doi.org\/10.1186\/s12911-024-02458-7","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]},"assertion":[{"value":"23 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"58"}}