{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T17:36:18Z","timestamp":1792431378068,"version":"4.5.0"},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":365,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.jbi.2024.104683","type":"journal-article","created":{"date-parts":[[2024,6,24]],"date-time":"2024-06-24T20:17:57Z","timestamp":1719260277000},"page":"104683","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":15,"special_numbering":"C","title":["Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models"],"prefix":"10.1016","volume":"156","author":[{"given":"H. Echo","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan P.","family":"Weiner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suchi","family":"Saria","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7698-219X","authenticated-orcid":false,"given":"Harold","family":"Lehmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1481-4323","authenticated-orcid":false,"given":"Hadi","family":"Kharrazi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jbi.2024.104683_b0005","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1257\/aer.p20171084","article-title":"Does Machine Learning Automate Moral Hazard and Error?","volume":"107","author":"Mullainathan","year":"2017","journal-title":"The American Economic Review"},{"key":"10.1016\/j.jbi.2024.104683_b0010","article-title":"Does, \u201cAI\u201d stand for augmenting inequality in the era of covid-19 healthcare?","volume":"372","author":"Leslie","year":"2021","journal-title":"BMJ (online)"},{"key":"10.1016\/j.jbi.2024.104683_b0015","article-title":"For Black kidney patients, an algorithm may help perpetuate harmful racial disparities","author":"Bichell","year":"2021","journal-title":"The Washington Post"},{"key":"10.1016\/j.jbi.2024.104683_b0020","first-page":"671","article-title":"Big Data's Disparate Impact","volume":"104","author":"Barocas","year":"2016","journal-title":"California Law Review"},{"key":"10.1016\/j.jbi.2024.104683_b0025","unstructured":"The Guardian. Amazon ditched AI recruiting tool that favored men for technical jobs. 2018. Available at https:\/\/www.theguardian.com\/technology\/2018\/oct\/10\/amazon-hiring-ai-gender-bias-recruiting-engine; Accessed on Sept 20, 2023."},{"key":"10.1016\/j.jbi.2024.104683_b0030","article-title":"How Costly is Noise?","author":"Blattner","year":"2021","journal-title":"Data and Disparities in Consumer Credit."},{"key":"10.1016\/j.jbi.2024.104683_b0035","doi-asserted-by":"crossref","first-page":"866","DOI":"10.7326\/M18-1990","article-title":"Ensuring Fairness in Machine Learning to Advance Health Equity","volume":"169","author":"Rajkomar","year":"2018","journal-title":"Annals of Internal Medicine"},{"key":"10.1016\/j.jbi.2024.104683_b0040","doi-asserted-by":"crossref","unstructured":"Paulus JK, Kent DM. Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities. npj Digital Medicine 2020;3:99.","DOI":"10.1038\/s41746-020-0304-9"},{"key":"10.1016\/j.jbi.2024.104683_b0045","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3457607","article-title":"A Survey on Bias and Fairness in Machine Learning","volume":"54","author":"Mehrabi","year":"2021","journal-title":"ACM Computing Surveys"},{"key":"10.1016\/j.jbi.2024.104683_b0050","series-title":"Suchi Saria","author":"Halamka","year":"2022"},{"key":"10.1016\/j.jbi.2024.104683_b0055","unstructured":"The White House. Algorithmic Discrimination Protections, Blueprint for an AI Bill of Rights: Making Automated Systems Work for the American People. Available at: https:\/\/www.whitehouse.gov\/ostp\/ai-bill-of-rights\/algorithmic-discrimination-protections-2\/; Accessed Sept 20, 2023."},{"key":"10.1016\/j.jbi.2024.104683_b0060","unstructured":"Agency for Healthcare Research and Quality (AHRQ). Impact of Healthcare Algorithms on Racial and Ethnic Disparities in Health and Healthcare. 2022. Available at: https:\/\/effectivehealthcare.ahrq.gov\/products\/racial-disparities-health-healthcare\/protocol#field_report_title_1; Accessed Sept 20, 2023."},{"key":"10.1016\/j.jbi.2024.104683_b0065","unstructured":"Agency for Healthcare Research and Quality (AHRQ). Assessing Algorithmic Bias and Fairness in Clinical Prediction Models for Preventive Services. 2023. Available at: https:\/\/uspreventiveservicestaskforce.org\/uspstf\/sites\/default\/files\/inline-files\/assessing-algorithmic-bias-fairness_0.pdf. Accessed Nov 1, 2023."},{"key":"10.1016\/j.jbi.2024.104683_b0070","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1038\/s43856-021-00028-w","article-title":"Mitigating bias in machine learning for medicine","volume":"1","author":"Vokinger","year":"2021","journal-title":"Communications Medicine"},{"key":"10.1016\/j.jbi.2024.104683_b0075","unstructured":"National Institute of Standards and Technology, (NIST). AI Risk Management Framework (AI RMF 1.0). March 30, 2023. Available at: https:\/\/www.nist.gov\/itl\/ai-risk-management-framework; Accessed Nov 11, 2023."},{"key":"10.1016\/j.jbi.2024.104683_b0080","doi-asserted-by":"crossref","unstructured":"The White House. EXECUTIVE ORDER ON THE SAFE, SECURE, AND TRUSTWORTHY DEVELOPMENT AND USE OF ARTIFICIAL INTELLIGENCE. Oct 30, 2023. Available at: https:\/\/www.whitehouse.gov\/briefing-room\/presidential-actions\/2023\/10\/30\/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence\/; Accessed Nov 11, 2023.","DOI":"10.35467\/cal\/187256"},{"key":"10.1016\/j.jbi.2024.104683_b0085","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1093\/jamia\/ocac065","article-title":"A bias evaluation checklist for predictive models and its pilot application for 30-day hospital readmission models","volume":"29","author":"Wang","year":"2022","journal-title":"Journal of the American Medical Informatics Association : JAMIA"},{"key":"10.1016\/j.jbi.2024.104683_b0090","unstructured":"Smedley BD, Stith AY, Care, Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health. Unequal Treatment. Washington: National Academies Press, 2002."},{"key":"10.1016\/j.jbi.2024.104683_b0095","doi-asserted-by":"crossref","first-page":"635","DOI":"10.7326\/0003-4819-141-8-200410190-00011","article-title":"Differences, Disparities, and Biases: Clarifying Racial Variations in Health Care Use","volume":"141","author":"Rathore","year":"2004","journal-title":"Annals of Internal Medicine"},{"key":"10.1016\/j.jbi.2024.104683_b0100","doi-asserted-by":"crossref","unstructured":"Verma S, Rubin J. Fairness definitions explained. Proceedings of the International Workshop on software fairness May 29, 2018:1-7.","DOI":"10.1145\/3194770.3194776"},{"key":"10.1016\/j.jbi.2024.104683_b0105","volume":"84","author":"Xu","year":"2022","journal-title":"Algorithmic Fairness in Computational Medicine. Ebiomedicine"},{"key":"10.1016\/j.jbi.2024.104683_b0110","author":"Jain","year":"2020","journal-title":"Biased Models Have Biased Explanations"},{"key":"10.1016\/j.jbi.2024.104683_b0115","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1146\/annurev-statistics-042720-125902","article-title":"Algorithmic Fairness: Choices, Assumptions, and Definitions","volume":"8","author":"Mitchell","year":"2021","journal-title":"Annual Review of Statistics and Its Application"},{"key":"10.1016\/j.jbi.2024.104683_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2020.103621","article-title":"An empirical characterization of fair machine learning for clinical risk prediction","volume":"113","author":"Pfohl","year":"2021","journal-title":"Journal of Biomedical Informatics"},{"key":"10.1016\/j.jbi.2024.104683_b0125","article-title":"The Measure and Mismeasure of Fairness: A Critical Review of Fair","author":"Corbett-Davies","year":"2018","journal-title":"Machine Learning"},{"key":"10.1016\/j.jbi.2024.104683_b0130","first-page":"447","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science (american Association for the Advancement of Science)"},{"key":"10.1016\/j.jbi.2024.104683_b0135","article-title":"Fair regression for health care spending","author":"Zink","year":"2019","journal-title":"Biometrics"},{"key":"10.1016\/j.jbi.2024.104683_b0140","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1001\/amajethics.2019.167","article-title":"Can AI Help Reduce Disparities in General Medical and Mental Health Care?","volume":"21","author":"Chen","year":"2019","journal-title":"AMA Journal of Ethics"},{"key":"10.1016\/j.jbi.2024.104683_b0145","article-title":"Evaluating Algorithmic Bias in 30-Day Hospital Readmission Models: Retrospective","volume":"26","author":"Wang","year":"2024","journal-title":"Analysis"},{"key":"10.1016\/j.jbi.2024.104683_b0150","unstructured":"Agency for Healthcare Research and Quality. HCUP State Databases. Healthcare Cost and Utilization Project (HCUP). 2019."},{"key":"10.1016\/j.jbi.2024.104683_b0155","unstructured":"Healthcare Cost and Utilization Project (HCUP). Statistical Brief No. 248. Characteristics of 30-Day All-Cause Hospital Readmissions, 2010\u20132016. 2019."},{"key":"10.1016\/j.jbi.2024.104683_b0160","unstructured":"Center for Medicare and Medicaid Services (CMS). Hospital-Wide All-Cause Risk-Standardized Readmission Measure: Measure Methodology Report. 2012. Available at: https:\/\/www.qualitynet.org\/inpatient\/measures\/readmission\/methodology; Accessed Dec 10, 2022."},{"key":"10.1016\/j.jbi.2024.104683_b0165","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1002\/jhm.2416","article-title":"Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data","volume":"10","author":"Horwitz","year":"2015","journal-title":"Journal of Hospital Medicine"},{"key":"10.1016\/j.jbi.2024.104683_b0170","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1503\/cmaj.091117","article-title":"Derivation and validation of an index to predict early death or unplanned readmission after discharge from hospital to the community","volume":"182","author":"van Walraven","year":"2010","journal-title":"Canadian Medical Association Journal (CMAJ)"},{"key":"10.1016\/j.jbi.2024.104683_b0175","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1001\/jamainternmed.2013.3023","article-title":"Potentially Avoidable 30-Day Hospital Readmissions in Medical Patients: Derivation and Validation of a Prediction Model","volume":"173","author":"Donz\u00e9","year":"2013","journal-title":"JAMA Internal Medicine"},{"key":"10.1016\/j.jbi.2024.104683_b0180","first-page":"e104","article-title":"Unplanned readmissions after hospital discharge among patients identified as being at high risk for readmission using a validated predictive algorithm","volume":"5","author":"Gruneir","year":"2011","journal-title":"Open Medicine"},{"key":"10.1016\/j.jbi.2024.104683_b0185","first-page":"1","article-title":"Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore","volume":"2015","author":"Low","year":"2015","journal-title":"BioMed Research International"},{"key":"10.1016\/j.jbi.2024.104683_b0190","doi-asserted-by":"crossref","first-page":"784","DOI":"10.1093\/ageing\/afs073","article-title":"Predicting readmissions: poor performance of the LACE index in an older UK population","volume":"41","author":"Cotter","year":"2012","journal-title":"Age and Ageing"},{"key":"10.1016\/j.jbi.2024.104683_b0195","first-page":"e3137","article-title":"The HOSPITAL score and LACE index as predictors of 30 day readmission in a retrospective study at a university-affiliated community hospital","volume":"5","author":"Robinson","year":"2017","journal-title":"PeerJ (san Francisco, CA)"},{"key":"10.1016\/j.jbi.2024.104683_b0200","first-page":"e2441","article-title":"The HOSPITAL score as a predictor of 30 day readmission in a retrospective study at a university affiliated community hospital","volume":"4","author":"Robinson","year":"2016","journal-title":"PeerJ (san Francisco, CA)"},{"key":"10.1016\/j.jbi.2024.104683_b0205","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1001\/jamainternmed.2015.8462","article-title":"International Validity of the HOSPITAL Score to Predict 30-Day Potentially Avoidable Hospital Readmissions","volume":"176","author":"Donz\u00e9","year":"2016","journal-title":"JAMA Internal Medicine"},{"key":"10.1016\/j.jbi.2024.104683_b0210","doi-asserted-by":"crossref","first-page":"e016921","DOI":"10.1136\/bmjopen-2017-016921","article-title":"Evaluating the predictive strength of the LACE index in identifying patients at high risk of hospital readmission following an inpatient episode: a retrospective cohort study","volume":"7","author":"Damery","year":"2017","journal-title":"BMJ Open"},{"key":"10.1016\/j.jbi.2024.104683_b0215","article-title":"Prospective validation and adaptation of the HOSPITAL score to predict high risk of unplanned readmission of medical patients","volume":"146","author":"Aubert","year":"2016","journal-title":"Swiss Medical Weekly"},{"key":"10.1016\/j.jbi.2024.104683_b0220","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1093\/qjmed\/hcv130","article-title":"Readmissions of medical patients: an external validation of two existing prediction scores","volume":"109","author":"Cooksley","year":"2016","journal-title":"QJM : Monthly Journal of the Association of Physicians"},{"key":"10.1016\/j.jbi.2024.104683_b0225","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1021\/acs.jcim.1c00160","article-title":"GHOST: Adjusting the Decision Threshold to Handle Imbalanced Data in Machine Learning","volume":"61","author":"Esposito","year":"2021","journal-title":"Journal of Chemical Information and Modeling"},{"key":"10.1016\/j.jbi.2024.104683_b0230","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision Curve Analysis: A Novel Method for Evaluating Prediction Models","volume":"26","author":"Vickers","year":"2006","journal-title":"Medical Decision Making"},{"key":"10.1016\/j.jbi.2024.104683_b0235","article-title":"Applicability Area: A novel utility-based approach for evaluating predictive models, beyond discrimination","author":"Liu","year":"2023","journal-title":"MedRxiv"},{"key":"10.1016\/j.jbi.2024.104683_b0240","article-title":"AI Fairness 360: An Extensible Toolkit for Detecting","author":"Bellamy","year":"2018","journal-title":"Understanding, and Mitigating Unwanted Algorithmic Bias."},{"key":"10.1016\/j.jbi.2024.104683_b0245","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1093\/jamia\/ocaa283","article-title":"Addressing bias in prediction models by improving subpopulation calibration","volume":"28","author":"Barda","year":"2021","journal-title":"Journal of the American Medical Informatics Association : JAMIA"},{"key":"10.1016\/j.jbi.2024.104683_b0250","unstructured":"Berk R, Heidari H, Jabbari S, Joseph M, Kearns M, Morgenstern J et al. A Convex Framework for Fair Regression. 2017. arXiv:1706.02409."},{"key":"10.1016\/j.jbi.2024.104683_b0255","article-title":"R package: fairness","author":"Kozodoi","year":"2020","journal-title":"Algorithmic Fairness Metrics."},{"key":"10.1016\/j.jbi.2024.104683_b0260","unstructured":"IBM. AI Fairness 360. 2020. Available at: https:\/\/aif360.mybluemix.net\/. Accessed April 2021."},{"key":"10.1016\/j.jbi.2024.104683_b0265","year":"2019","journal-title":"Agency for Healthcare Research and Quality. Clinical Classifications Software (CCS) for ICD-10-PCS."},{"key":"10.1016\/j.jbi.2024.104683_b0270","unstructured":"Center of Medicare and Medicaid Services. Risk Adjustment 2020 Model Software\/ICD-10 Mappings. 2020."},{"key":"10.1016\/j.jbi.2024.104683_b0275","doi-asserted-by":"crossref","unstructured":"Speicher T, Heidari H, Grgic-Hlaca N, Gummadi K, Singla A, Weller A et al. A Unified Approach to Quantifying Algorithmic Unfairness. Proceedings of the 24th ACM SIGKDD International Conference on knowledge discovery & data mining Jul 19, 2018:2239-48.","DOI":"10.1145\/3219819.3220046"},{"key":"10.1016\/j.jbi.2024.104683_b0280","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1145\/3376898","article-title":"A snapshot of the frontiers of fairness in machine learning","volume":"63","author":"Chouldechova","year":"2020","journal-title":"Communications of the ACM"},{"key":"10.1016\/j.jbi.2024.104683_b0285","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1093\/jamia\/ocad013","article-title":"Healthcare utilization is a collider: an introduction to collider bias in EHR data reuse","volume":"30","author":"Weiskopf","year":"2023","journal-title":"Journal of the American Medical Informatics Association : JAMIA"},{"key":"10.1016\/j.jbi.2024.104683_b0290","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1093\/jamia\/ocz040","article-title":"Underserved populations with missing race ethnicity data differ significantly from those with structured race\/ethnicity documentation","volume":"26","author":"Sholle","year":"2019","journal-title":"Journal of the American Medical Informatics Association : JAMIA"},{"key":"10.1016\/j.jbi.2024.104683_b0295","doi-asserted-by":"crossref","first-page":"1878","DOI":"10.1093\/jamia\/ocaa164","article-title":"Reporting of demographic data and representativeness in machine learning models using electronic health records","volume":"27","author":"Bozkurt","year":"2020","journal-title":"Journal of the American Medical Informatics Association : JAMIA"},{"key":"10.1016\/j.jbi.2024.104683_b0300","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1001\/jama.2011.307","article-title":"Geographic Variation in Diagnosis Frequency and Risk of Death Among Medicare Beneficiaries","volume":"305","author":"Welch","year":"2011","journal-title":"JAMA : the Journal of the American Medical Association"},{"key":"10.1016\/j.jbi.2024.104683_b0305","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","article-title":"A simple, step-by-step guide to interpreting decision curve analysis","volume":"3","author":"Vickers","year":"2019","journal-title":"Diagnostic and Prognostic Research"},{"key":"10.1016\/j.jbi.2024.104683_b0310","series-title":"Race after technology : abolitionist tools for the new Jim code","author":"Benjamin","year":"2019"},{"key":"10.1016\/j.jbi.2024.104683_b0315","doi-asserted-by":"crossref","first-page":"e367","DOI":"10.2196\/jmir.8775","article-title":"Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies","volume":"19","author":"Greenhalgh","year":"2017","journal-title":"Journal of Medical Internet Research"},{"key":"10.1016\/j.jbi.2024.104683_b0320","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1136\/bmjinnov-2019-000359","article-title":"Bridging the implementation gap of machine learning in healthcare","volume":"6","author":"Seneviratne","year":"2020","journal-title":"BMJ Innovations"},{"key":"10.1016\/j.jbi.2024.104683_b0325","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1093\/jamia\/ocaa094","article-title":"Latent bias and the implementation of artificial intelligence in medicine","volume":"27","author":"DeCamp","year":"2020","journal-title":"Journal of the American Medical Informatics Association : JAMIA"},{"key":"10.1016\/j.jbi.2024.104683_b0330","unstructured":"Center for Medicare & Medicaid Services (CMS). Maryland All-Payer Model. 2022. Available at: https:\/\/innovation.cms.gov\/innovation-models\/maryland-all-payer-model; Accessed March 20, 2022."},{"key":"10.1016\/j.jbi.2024.104683_b0335","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1186\/1472-6947-14-51","article-title":"Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research","volume":"14","author":"Rusanov","year":"2014","journal-title":"BMC Medical Informatics and Decision Making"},{"key":"10.1016\/j.jbi.2024.104683_b0340","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.jbi.2013.06.010","article-title":"Defining and measuring completeness of electronic health records for secondary use","volume":"46","author":"Weiskopf","year":"2013","journal-title":"Journal of Biomedical Informatics"}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046424001011?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046424001011?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T07:09:45Z","timestamp":1754032185000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046424001011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":68,"alternative-id":["S1532046424001011"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2024.104683","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Assessing racial bias in healthcare predictive models: Practical lessons from an empirical evaluation of 30-day hospital readmission models","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2024.104683","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"104683"}}