{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T19:25:09Z","timestamp":1750361109915},"reference-count":13,"publisher":"Georg Thieme Verlag KG","issue":"04","funder":[{"name":"National Institutes of Health and National Library of Medicine","award":["NIH\/NLM 1R01 LM013323\u201301"],"award-info":[{"award-number":["NIH\/NLM 1R01 LM013323\u201301"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>\n          Objectives\u2003This study aimed to compare the concordance of pressure injury (PI) site, stage, and count documented in electronic health records (EHRs); explore if PI count during each patient hospitalization is consistent based on PI site or stage count in the diagnosis or chart event records; and examine if discrepancies in PI count were associated with patient characteristics.<\/jats:p><jats:p>\n          Methods\u2003Hospitalization records with the International Classification of Diseases ninth edition (ICD-9) codes, chart events from two systems (CareVue, MetaVision), and clinical notes on PI were extracted from the Medical Information Mart for Intensive Care (MIMIC)-III database. PI site and stage counts from individual hospitalization were computed. Hospitalizations with the same or different counts of site and stage according to ICD-9 codes (site and stage), CareVue (site and stage), or MetaVision (stage) charts were defined as consistent or discrepant reporting. Chi-squared, independent t-, and Kruskal\u2013Wallis tests were examined if the count discrepancy was associated with patient characteristics. ICD-9 codes and charts were also compared for people with one site or stage.<\/jats:p><jats:p>\n          Results\u2003A total of 31,918 hospitalizations had PI data. Within hospitalizations with ICD-9-coded sites and stages, 55.9% reported different counts. Within hospitalizations with CareVue charts on PI, 99.3% reported the same count. For hospitalizations with stages based on ICD-9 codes or MetaVision chart data, only 42.9% reported the same count. Discrepancies in counts were consistently and significantly associated with variables including PI recording in clinical notes, dead\/hospice at discharge, more caregivers, longer hospitalization or intensive care unit stays, and more days to first transfer. Discrepancies between ICD-9 code and chart values on the site and stage were also reported.<\/jats:p><jats:p>\n          Conclusion\u2003Patient characteristics associated with PI count discrepancies identified patients at risk of having discrepant PI counts or worse outcomes. PI documentation quality could be improved with better communication, care continuity, and integrity. Clinical research using EHRs should adopt systematic data quality analysis to inform limitations.<\/jats:p>","DOI":"10.1055\/s-0041-1735179","type":"journal-article","created":{"date-parts":[[2021,9,30]],"date-time":"2021-09-30T02:21:54Z","timestamp":1632968514000},"page":"897-909","source":"Crossref","is-referenced-by-count":4,"title":["Examining the Concordance in the Documented Pressure Injury Site, Stage, and Count in Medical Information Mart for Intensive Care-III"],"prefix":"10.1055","volume":"12","author":[{"given":"Wenhui","family":"Zhang","sequence":"additional","affiliation":[{"name":"Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mani","family":"Sotoodeh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Arts and Sciences, Emory University, Atlanta, Georgia, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joyce C.","family":"Ho","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Arts and Sciences, Emory University, Atlanta, Georgia, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roy L.","family":"Simpson","sequence":"additional","affiliation":[{"name":"Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vicki S.","family":"Hertzberg","sequence":"additional","affiliation":[{"name":"Center for Data Science, Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, Georgia, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"issue":"06","key":"ref1","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1097\/WON.0000000000000281","article-title":"Revised National Pressure Ulcer Advisory Panel Pressure Injury Staging System: revised pressure injury staging system","volume":"43","author":"L E Edsberg","year":"2016","journal-title":"J Wound Ostomy Continence Nurs"},{"key":"ref2","volume-title":"Prevention and treatment of pressure ulcers: clinical practice guideline","author":"National Pressure Ulcer Advisory Panel"},{"issue":"11","key":"ref4","first-page":"30","article-title":"Pressure ulcers in the United States' inpatient population from 2008 to 2012: results of a retrospective nationwide study","volume":"62","author":"K Bauer","year":"2016","journal-title":"Ostomy Wound 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and dimensions of electronic health record data quality assessment: enabling reuse for clinical research","volume":"20","author":"N G Weiskopf","year":"2013","journal-title":"J Am Med Inform Assoc"},{"issue":"01","key":"ref12","first-page":"14","article-title":"A data quality assessment guideline for electronic health record data reuse","volume":"5","author":"N G Weiskopf","year":"2017","journal-title":"EGEMS (Wash DC)"},{"issue":"04","key":"ref13","doi-asserted-by":"crossref","first-page":"mmrr.003.04.b03","DOI":"10.5600\/mmrr.003.04.b03","article-title":"Examination of the accuracy of coding hospital-acquired pressure ulcer stages","volume":"3","author":"N M Coomer","year":"2013","journal-title":"Medicare Medicaid Res Rev"},{"issue":"03","key":"ref14","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1136\/bmjqs-2017-006726","article-title":"Consistency of pressure injury documentation across interfacility transfers","volume":"27","author":"L Squitieri","year":"2018","journal-title":"BMJ Qual Saf"},{"issue":"04","key":"ref15","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1177\/0193945915602259","article-title":"Predictive validity of pressure ulcer risk assessment tools for elderly: a meta-analysis","volume":"38","author":"S-H Park","year":"2016","journal-title":"West J Nurs Res"},{"issue":"01","key":"ref16","doi-asserted-by":"crossref","first-page":"160035","DOI":"10.1038\/sdata.2016.35","article-title":"MIMIC-III, a freely accessible critical care database","volume":"3","author":"A EW Johnson","year":"2016","journal-title":"Sci Data"},{"issue":"04","key":"ref17","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1093\/jamia\/ocaa004","article-title":"A customizable deep learning model for nosocomial risk prediction from critical care notes with indirect supervision","volume":"27","author":"T R Goodwin","year":"2020","journal-title":"J Am Med Inform Assoc"}],"container-title":["Applied Clinical 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