Journal Article

Modifying Interhospital Hepatopancreatobiliary Transfers Based on Predictive Analytics: Moving from a Center of Excellence to a Health-Care System of Excellence

Martinie, John B., Murphy, Keith, Sulzer, Jesse K., Lyman, William B., Vrochides, Dionisios, Ocuin, Lee M., Pickens, Ryan C., Iannitti, David A., Baker, Erin H., Matthews, Brent D., Bloomer, Ainsley K.

2019

Overview

Regionalization of complex surgical care has increased interhospital transfers to quaternary centers within large health-care systems. Risk-based patient selection is imperative to improve resource allocation without compromising care. This study aimed to develop predictive models for identifying low-risk patients for transfer to a fully integrated satellite hepatopancreatobiliary (HPB) service in the northeast region of the health-care system. HPB transfers to the quaternary center over 15 months from hospitals in proximity to the satellite HPB center. A predictive tool was developed based on simple pretransfer variables and outcomes for 30-day major complications (Clavien grade ≥ 3), readmission, and mortality. Thresholds for “low risk” were set at different SDs below mean for each model. Predictive models were developed from 51 eligible northeast region patient transfers for major complications (Brier score 0.1948, receiver operator characteristic (ROC) 0.7123, P = 0.0009), readmission (Brier score 0.0615, ROC 0.7368, P = 0.0020), and mortality (Brier score 0.0943, ROC 0.7989, P = 0.0023). Thresholds set from 2 SD below the mean for all models identified 2 as “low risk.” Adjusting the threshold for the serious complication model to only 1 SD below the mean increased the “low-risk” cohort to five patients. These models demonstrate an easy-to-use tool to assist surgeons in identifying low-risk patients for diversion to a fully integrated satellite center. Improved interhospital transfers within a region could begin a transition from centers of excellence toward health-care systems of excellence.

• Publisher: SAGE Publications,SAGE PUBLICATIONS, INC
• Subject: Analytics / Clinical outcomes / Complications / Decision making / Health risks / Hospitals / Intensive care / Medical referrals / Mortality / Patients / Pediatrics / Prediction models / Predictive analytics / Resource allocation / Risk / Surgeons / Surgery / Surgical outcomes / Thresholds