A Comprehensive and Improved Definition for Hospital-Acquired Pressure Injury Classification Based on Electronic Health Records: Comparative Study

• Type of Material: Article
• Authors: Mani Sotoodeh, University of Montreal; Wenhui Zhang, Emory University; Roy Simpson, Emory University; Vicki Hertzberg, Emory University; Joyce Ho, Emory University
• Language: English
• Date: 2023-01-01
• Publisher: JMIR Medical Informatics
• Publication Version: Final Published Version
• Copyright Statement: ©Mani Sotoodeh, Wenhui Zhang, Roy L Simpson, Vicki Stover Hertzberg, Joyce C Ho. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 23.02.2023.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.2196/40672
• Title of Journal or Parent Work: JMIR Medical Informatics
• Volume: 11
• Start Page: e40672
• End Page: e40672
• Grant/Funding Information: This research was supported by the National Library of Medicine of the National Institutes of Health under the award number R01LM013323-01.
• Keywords: pressure ulcer care; nursing quality; nursing; electronic health record; pressure ulcer prevention; EHR data; bedsore; EHR; nursing assessment; electronic medical records; data mining; decubitus ulcer; EHR systems; pressure ulcer
• Research Categories: Health Sciences, Nursing

Abstract

Background: Patients develop pressure injuries (PIs) in the hospital owing to low mobility, exposure to localized pressure, circulatory conditions, and other predisposing factors. Over 2.5 million Americans develop PIs annually. The Center for Medicare and Medicaid considers hospital-acquired PIs (HAPIs) as the most frequent preventable event, and they are the second most common claim in lawsuits. With the growing use of electronic health records (EHRs) in hospitals, an opportunity exists to build machine learning models to identify and predict HAPI rather than relying on occasional manual assessments by human experts. However, accurate computational models rely on high-quality HAPI data labels. Unfortunately, the different data sources within EHRs can provide conflicting information on HAPI occurrence in the same patient. Furthermore, the existing definitions of HAPI disagree with each other, even within the same patient population. The inconsistent criteria make it impossible to benchmark machine learning methods to predict HAPI. Objective: The objective of this project was threefold. We aimed to identify discrepancies in HAPI sources within EHRs, to develop a comprehensive definition for HAPI classification using data from all EHR sources, and to illustrate the importance of an improved HAPI definition. Methods: We assessed the congruence among HAPI occurrences documented in clinical notes, diagnosis codes, procedure codes, and chart events from the Medical Information Mart for Intensive Care III database. We analyzed the criteria used for the 3 existing HAPI definitions and their adherence to the regulatory guidelines. We proposed the Emory HAPI (EHAPI), which is an improved and more comprehensive HAPI definition. We then evaluated the importance of the labels in training a HAPI classification model using tree-based and sequential neural network classifiers. Results: We illustrate the complexity of defining HAPI, with <13% of hospital stays having at least 3 PI indications documented across 4 data sources. Although chart events were the most common indicator, it was the only PI documentation for >49% of the stays. We demonstrate a lack of congruence across existing HAPI definitions and EHAPI, with only 219 stays having a consensus positive label. Our analysis highlights the importance of our improved HAPI definition, with classifiers trained using our labels outperforming others on a small manually labeled set from nurse annotators and a consensus set in which all definitions agreed on the label. Conclusions: Standardized HAPI definitions are important for accurately assessing HAPI nursing quality metric and determining HAPI incidence for preventive measures. We demonstrate the complexity of defining an occurrence of HAPI, given the conflicting and incomplete EHR data. Our EHAPI definition has favorable properties, making it a suitable candidate for HAPI classification tasks.