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ABSTRACT: Data-Driven Interdisciplinary Interventions to Improve Inpatient Pain Management

Abstract

Pain during hospitalization and dissatisfaction with pain management are common. This project consisted of 4 phases: identifying a pain numeric rating scale (NRS) metric associated with patient satisfaction, identifying independent predictors of maximum NRS, implementing interventions, and evaluating trends in NRS and satisfaction. Maximum NRS was inversely associated with favorable pain satisfaction for both efficacy (n = 4062, χ2 = 66.2, P < .001) and staff efforts (n = 4067, χ2 = 30.3, P < .001). Independent predictors of moderate-to-severe maximum NRS were younger age, female sex, longer hospital stay, admitting department, psychoactive medications, and 10 diagnostic codes. After interventions, moderate-to-severe maximum NRS declined by 3.6% per quarter in 2010 compared with 2009. Satisfaction data demonstrated improvements in nursing units meeting goals (5.3% per quarter, r 2 = 0.67) and favorable satisfaction answers (0.36% per quarter, r 2 = 0.31). Moderate-to-severe maximum NRS was an independent predictor of lower likelihood of hospital discharge (likelihood ratio = 0.62; 95% confidence interval = 0.61-0.64). Targeted interventions were associated with improved inpatient pain management.

via Data-Driven Interdisciplinary Interventions to Improve Inpatient Pain Management.

Brian S McGowan, PhD

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Brian is a research scientist and educational technologist. He helped transform Pfizer’s Medical Education Group and previously served in educational leadership roles at HealthAnswers, Inc.; Acumentis, LLC.; Cephalon; and Wyeth. He taught graduate medical education programs at Arcadia University for 10 years. Dr. McGowan recently authored the book "#socialQI: Simple Solutions for Improving Your Healthcare" and has been invited to speak internationally on the subject of information flow, technology, and learning in healthcare.

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