Redesigning aggregate patient data to be made available at the point of care improves value for patients undergoing congenital heart surgery

Andrew Shin drewshin@stanford.edu

Abstract

Background:
Health care in the United States is increasingly losing value. Decreasing variation is central to reducing waste and cost. The recent rapid adoption of information technology is regarded as an important means to promote high value care. Historically, aggregate patient data are examined retrospectively and separate from the current, dynamic patient-provider interface. We describe a Clinical Effectiveness program that redesigns aggregate patient data for personalized comparative effectiveness information at the point of care to provide target hospital goals for patients following congenital heart surgery.

Methods:
Using an observational pre-post-intervention design, patients undergoing one of the 10 core congenital heart surgeries as defined by the Society of Thoracic Surgeons were included. The setting was a tertiary university-affiliated academic children’s hospital between September 6, 2016 and December 19, 2016. Personalized comparative cohorts encompassing 2 years of pre-intervention data were constructed utilizing the electronic health record (EHR). Based on the aggregate data, target hospitalization goals (e.g., target extubation time) were tailored for every patient and made visible peri-operatively for healthcare providers in real-time. Outcome metrics included intensive care unit (ICU), total post-operative length of stay (LOS), and associated cost.

Results: A total of 51 patients were enrolled with 47 (92%) completing the program. We found an aggregate reduction in LOS and variance for both ICU (median 3 [IQR 3-4] vs 5 [IQR 3-7] days, p<0.001; mean 3.6+1.9 vs 6.5+6.5 days, p=0.003), and total postoperative LOS (median 6 [IQR 5-8] vs 8 [IQR 6-12] days, p<0.001; mean 6.5+2.3 vs 10.9+9.9 days, p=0.003), compared with the pre-intervention period. Mortality, reintubation, ICU, and hospital readmission rates were unchanged. The annualized cost savings is estimated to be approximately $2.5 million.

Conclusions: We describe a clinical practice redesign that leverages EHR data for real-time comparative effectiveness information to ascribe real-time benchmarks. We surmise that inpatient providers, by having transparent benchmarks for their patients, were able to have a shared mental model in their healthcare delivery. We found that achieving a shared mental model decreased variation and safely reduced length of stay and associated cost. 
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