Poster presented June 4, 2026, at the Nursing Knowledge Big Data Science Conference held in Minneapolis.
Learning Objectives
- Identify three nurse-documented data elements beyond standardized assessment instruments that contribute to hospital-acquired pressure injury (HAPI) prediction.
- State one reason nurse-documented data elements contribute to HAPI prediction models.
Introduction: Hospital-acquired pressure injuries (HAPIs) remain a persistent and costly patient safety problem, with average hospitalization costs for affected patients exceeding $100,000 and associated increases in length of stay, readmission, and mortality.1,2 Nurses generate the data most relevant to HAPI prevention (e.g., standardized assessment instruments, skin observations, and preventive interventions). However, the incremental predictive value of nurse-documented data for HAPI prediction remains unknown. This scoping review synthesizes evidence on nurse-documented data elements in machine learning HAPI prediction models.
Full abstract available in the conference proceedings, available online and as a downloadable PDF. See link below.