Abstract
OBJECTIVES: This 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. METHODS: Hospitalization 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-Wallis 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. RESULTS: A 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. CONCLUSION: Patient 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.