Digital Quality Improvement Approach Reduces the Need for Rescue Antiemetics in High-Risk Patients: A Comparative Effectiveness Study Using Interrupted Time Series and Propensity Score Matching Analysis

May 1, 2019

https://journals.lww.com/anesthesia-analgesia/Fulltext/2019/05000/Digital_Quality_Improvement_Approach_Reduces_the.9.aspx

Abstract

BACKGROUND:
Affecting nearly 30% of all surgical patients, postoperative nausea and vomiting (PONV) can lead to patient dissatisfaction, prolonged recovery times, and unanticipated hospital admissions. There are well-established, evidence-based guidelines for the prevention of PONV; yet physicians inconsistently adhere to them. We hypothesized that an electronic medical record–based clinical decision support (CDS) approach that incorporates a new PONV pathway, education initiative, and personalized feedback reporting system can decrease the incidence of PONV.

METHODS:
Two years of data, from February 17, 2015 to February 16, 2016, was acquired from our customized University of California Los Angeles Anesthesiology perioperative data warehouse. We queried the entire subpopulation of surgical cases that received general anesthesia with volatile anesthetics, were ≥12 years of age, and spent time recovering in any of the postanesthesia care units (PACUs). We then defined PONV as the administration of an antiemetic medication during the aforementioned PACU recovery. Our CDS system incorporated additional PONV-specific questions to the preoperative evaluation form, creation of a real-time intraoperative pathway compliance indicator, initiation of preoperative PONV risk alerts, and individualized emailed reports sent weekly to clinical providers. The association between the intervention and PONV was assessed by comparing the slopes from the incidence of PONV pre/postintervention as well as comparing observed incidences in the postintervention period to what we expected if the preintervention slope would have continued using interrupted time series analysis regression models after matching the groups on PONV-specific risk factors.

RESULTS:
After executing the PONV risk-balancing algorithm, the final cohort contained 36,796 cases, down from the 40,831 that met inclusion criteria. The incidence of PONV before the intervention was estimated to be 19.1% (95% confidence interval [CI], 17.9%–20.2%) the week before the intervention. Directly after implementation of the CDS, the total incidence decreased to 16.9% (95% CI, 15.2%–18.5%; P = .007). Within the high-risk population, the decrease in the incidence of PONV went from 29.3% (95% CI, 27.6%–31.1%) to 23.5% (95% CI, 20.5%–26.5%; P < .001). There was no significant difference in the PONV incidence slopes over the entire pre/postintervention periods in the high- or low-risk groups, despite an abrupt decline in the PONV incidence for high-risk patients within the first month of the CDS implementation.

CONCLUSIONS:
We demonstrate an approach to reduce PONV using individualized emails and anesthesia-specific CDS tools integrated directly into a commercial electronic medical record. We found an associated decrease in the PACU administration of rescue antiemetics for our high-risk patient population.

Read more: https://journals.lww.com/anesthesia-analgesia/Fulltext/2019/05000/Digital_Quality_Improvement_Approach_Reduces_the.9.aspx