Developing an Inpatient Electronic Medical Record Phenotype for Hospital-Acquired Pressure Injuries: Case Study Using Natural Language Processing Models.

ABSTRACT:

Background

Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs.

Objective

This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes.

Methods

Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute c

SUBMITTER: Nurmambetova E 

PROVIDER: S-EPMC11041460 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature