Effectiveness of a novel and scalable clinical decision support intervention to improve venous thromboembolism prophylaxis: a quasi-experimental study

Abstract

BackgroundVenous thromboembolism (VTE) causes morbidity and mortality in hospitalized patients, and regulators and payors are encouraging the use of systems to prevent them. Here, we examine the effect of a computerized clinical decision support (CDS) intervention implemented across a multi-hospital academic health system on VTE prophylaxis and events.MethodsThe study included 223,062 inpatients admitted between April 2007 and May 2010, and used administrative and clinical data. The intervention was integrated into a commercial electronic health record (EHR) in an admission orderset used for all admissions. Three time periods were examined: baseline (period 1), and the time after implementation of the first CDS intervention (period 2) and a second iteration (period 3). Providers were prompted to accept or decline prophylaxis based on patient risk. Time series analyses examined the impact of the intervention on VTE prophylaxis during time periods two and three compared to baseline, and a simple pre-post design examined impact on VTE events and bleeds secondary to anticoagulation. VTE prophylaxis and events were also examined in a prespecified surgical subset of our population meeting the public reporting criteria defined by the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator (PSI).ResultsUnadjusted analyses suggested that “recommended”, “any”, and “pharmacologic” prophylaxis increased from baseline to the last study period (27.1% to 51.9%, 56.7% to 78.1%, and 42.0% to 54.4% respectively; p < 0.01 for all comparisons). Results were significant across all hospitals and the health system overall. Interrupted time series analyses suggested that our intervention increased the use of “recommended” and “any” prophylaxis by 7.9% and 9.6% respectively from baseline to time period 2 (p < 0.01 for both comparisons); and 6.6% and 9.6% respectively from baseline to the combined time periods 2 and 3 (p < 0.01 for both comparisons). There were no significant changes in “pharmacologic” prophylaxis in the adjusted model. The overall percent of patients with VTE increased from baseline to the last study period (2.0% to 2.2%; p = 0.03), but an analysis excluding patients with VTE “present on admission” (POA) demonstrated no difference in events (1.3% to 1.3%; p = 0.80). Overall bleeds did not significantly change. An analysis examining VTE prophylaxis and events in a surgical subset of patients defined by the AHRQ PSI demonstrated increased “recommended”, “any”, and “pharmacologic” prophylaxis from baseline to the last study period (32.3% to 60.0%, 62.8% to 85.7%, and 47.9% to 63.3% respectively; p < 0.01 for all comparisons) as well as reduced VTE events (2.2% to 1.7%; p < 0.01).ConclusionsThe CDS intervention was associated with an increase in “recommended” and “any” VTE prophylaxis across the multi-hospital academic health system. The intervention was also associated with increased VTE rates in the overall study population, but a subanalysis using only admissions with appropriate POA documentation sugge...

Hospital Performance for Pharmacologic Venous Thromboembolism Prophylaxis and Rate of Venous Thromboembolism

“…A recent report 21 describing use of a clinical decision support intervention at a 3-hospital system found a significant increase in pharmacologic prophylaxis use for all patients, but a reduction in VTE was only found in the subset of surgical patients. However, another report 22 that evaluated the effect of hospital performance on the Surgical Care Improvement Project VTE measure failed to find an association between prophylaxis rates and VTE outcomes.…”

Automating Venous Thromboembolism Risk Calculation Using Electronic Health Record Data Upon Hospital Admission: The Automated Padua Prediction Score

“…Other risk scores that use only discrete scalar data, such as vital signs and lab results to predict early recognition of sepsis, have been successfully automated and implemented within electronic health records (EHRs). [9][10][11] Successful automation of scores requiring input of diagnoses, recent medical events, and current clinical status such as the PPS remains difficult. 12 Data representing these characteristics are more prone to error, and harder to translate clearly into a single data field than discrete elements like heart rate, potentially impacting validity of the calculated result.…”

Using a 10-step framework to support the implementation of an evidence-based clinical pathways programme

“…Our ultimate goal is to develop a robust evaluation and reporting infrastructure that will enable us to systematically evaluate the impact of many of our individual pathways on process measures, clinical outcomes and cost, but we would envision such evaluations to be provided or shared at the level of the individual pathway, rather than at the programme level as a whole. Similar to how we have evaluated the impact of individual CDS interventions to understand the impact of our CDS programme,30 31 we will likewise evaluate the impact of individual pathways to gauge the clinical effectiveness of our pathways programme.…”