Advancing event log preparation with quality optimization for hospital process mining

Advancing event log preparation with quality optimization for hospital process mining | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Advancing event log preparation with quality optimization for hospital process mining Ruihua Guo, Angus Richie, Yang Lu, S. T. Boris Choy, Ross Smith, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7796031/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Time-dependent clinical events collected from the electronic health records (EHR), known as event logs, provided enriched information yet lack of systematic approach to address quality problems. A multi-layer approach has been proposed to enhance the quality of hospital event logs to assess unplanned readmission risk in patients with heart failure (HF). Method Eligible patients were identified from DREAM, a multi-site hospital dataset encompasses routinely collected EHR within a large metropolitan health system in Australia. At source level, the Weiskopf and Weng framework was adopted to evaluate the quality across five dimensions—currency, correctness, completeness, concordance, plausibility, alignment with the study objective, and at multiple analytical levels. Results were benchmarked against the publicly available Medical Information Mart for Intensive Care IV (MIMIC-IV) hospital database. A biodiversity framework has been employed to assess the quality at log level and compared to MIMIC-IV logs. Results Our findings showed that DREAM provided a timely, area-specific source of information with superior currency and source completeness compared to the benchmark database. The correctness and plausibility were comparable for both sources. Both datasets showed higher coverage than log completeness, with MIMIC-IV logs demonstrating greater complexity, reflected by higher diversity across all subpopulation groups. Conclusion This multi-layer approach aligned closely with the study objective, enabling domain-specific contextual awareness and mitigating bias at multiple levels. By incorporating an additional layer of event log quality evaluation based on biodiversity theory, the approach enhanced external validity and internal fairness, improving log comparability across data sources and within subpopulation groups. Event Log Process Mining Quality Evaluation Hospital Electronic Health Records Evidence Appraisal Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementaryfile.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Mar, 2026 Reviews received at journal 01 Dec, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 10 Nov, 2025 Editor invited by journal 14 Oct, 2025 Editor assigned by journal 10 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 07 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Event Log, Process Mining, Quality Evaluation, Hospital, Electronic Health Records, Evidence Appraisal","lastPublishedDoi":"10.21203/rs.3.rs-7796031/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7796031/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTime-dependent clinical events collected from the electronic health records (EHR), known as event logs, provided enriched information yet lack of systematic approach to address quality problems. A multi-layer approach has been proposed to enhance the quality of hospital event logs to assess unplanned readmission risk in patients with heart failure (HF).\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eEligible patients were identified from DREAM, a multi-site hospital dataset encompasses routinely collected EHR within a large metropolitan health system in Australia. At source level, the Weiskopf and Weng framework was adopted to evaluate the quality across five dimensions\u0026mdash;currency, correctness, completeness, concordance, plausibility, alignment with the study objective, and at multiple analytical levels. Results were benchmarked against the publicly available Medical Information Mart for Intensive Care IV (MIMIC-IV) hospital database. A biodiversity framework has been employed to assess the quality at log level and compared to MIMIC-IV logs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur findings showed that DREAM provided a timely, area-specific source of information with superior currency and source completeness compared to the benchmark database. The correctness and plausibility were comparable for both sources. Both datasets showed higher coverage than log completeness, with MIMIC-IV logs demonstrating greater complexity, reflected by higher diversity across all subpopulation groups.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis multi-layer approach aligned closely with the study objective, enabling domain-specific contextual awareness and mitigating bias at multiple levels. By incorporating an additional layer of event log quality evaluation based on biodiversity theory, the approach enhanced external validity and internal fairness, improving log comparability across data sources and within subpopulation groups.\u003c/p\u003e","manuscriptTitle":"Advancing event log preparation with quality optimization for hospital process mining","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-20 05:10:33","doi":"10.21203/rs.3.rs-7796031/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-06T07:49:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T13:42:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T10:55:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27149966565279194626304724238390625291","date":"2025-11-17T05:40:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151012097145897708820366178968778441477","date":"2025-11-11T07:49:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249988587668304270160637432247282580351","date":"2025-11-11T06:15:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-10T20:02:21+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T12:04:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T08:12:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T08:09:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Research Methodology","date":"2025-10-07T05:31:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-research-methodology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmrm","sideBox":"Learn more about [BMC Medical Research Methodology](http://bmcmedresmethodol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmrm/default.aspx","title":"BMC Medical Research Methodology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9cc4eee2-ac94-494a-b8d8-30cce4fa40d9","owner":[],"postedDate":"November 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-21T01:08:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-20 05:10:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7796031","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7796031","identity":"rs-7796031","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}