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

Eilon Gabel, MD; John Shin, MD; Ira Hofer, MD; Tristan Grogan, MS; Keren Ziv, MD; Joe Hong, MD; Anahat Dhillon, MD; James Moore, MD; Aman Mahajan, MD, PhD; Maxime Cannesson, MD, PhD


Anesth Analg. 2019;128(5):867-876. 

In This Article

Abstract and Introduction


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.


Postoperative nausea and vomiting (PONV) is one of the most frequent adverse effects observed in patients undergoing anesthesia for surgical procedures, affecting approximately 30% of all surgical patients, and >70% of patients in high-risk patient groups.[1–4] A number of published comprehensive reviews, risk scoring systems, and evidence-based guidelines have been developed for the prevention and management of PONV.[5–9] However, evidence-based guidelines in general have poor physician compliance.[10,11] Specifically with PONV, previous attempts to change anesthesiologists' practice habits and improve guideline compliance have included didactic sessions reviewing the most current PONV literature and retrospective quarterly reports of individual and department-wide compliance rates.[12–14]

Electronic medical records (EMRs) offer alternative possibilities with clinical decision support (CDS) tools that can deliver relevant, patient-specific information to the clinician at an appropriate time during patient care. Classically, CDS systems are categorized as being active (requiring some level of automatic processing at the time of an alert) or passive (simply relying on hard-stops that are preset within the EMR for all patients), then secondarily distinguished by real-time versus non–real-time processing.[15] Using unique combinations of these principles, CDS tools have successfully demonstrated benefit for many intraoperative issues such as decreasing the incidence of hypotension, controlling intraoperative glucose levels, and timing of antibiotic prophylaxis.[16–28] Many of the prior studies, especially those relating to PONV, involved highly customized and proprietary EMRs that are not widely used.[12–14] Some are even specific to a single institution.[12–14,16–18,20,25,26,28–31] Because our health system utilizes one of the most popular EMRs in the United States, this provides the opportunity to more easily share our CDS with other hospitals that use the same EMR.[32] Furthermore, our PONV CDS implementation was approved as an Epic (EPIC Systems, Verona, WI) Clinical Program, meaning that it meets the necessary requirements for being easily portable to other Epic installations, has an accompanying build instructions for local installation, and can be distributed to customers for free.[33–35]

We hypothesized that using a CDS system both with real-time intraoperative feedback using EMR integration and, concurrently, with non–real-time checkpoint-triggered elements would successfully reduce the incidence of PONV in patients undergoing general anesthesia. We designed and implemented a comprehensive real-time and non–real-time mixed CDS tool out of EPIC in conjunction with a personalized feedback reporting system. Success was determined by comparing outcomes against a historical control group of risk-matched patients.