Clinically Meaningful Laboratory Protocols Reduce Hospital Charges Based on Institutional and ACS-NSQIP® Risk Calculators in Hepatopancreatobiliary Surgery

Abstract

Postoperative laboratory testing is an underrecognized but substantial contributor to health-care costs. We aimed to develop and validate a clinically meaningful laboratory (CML) protocol with individual risk stratification using generalizable and institution-specific predictive analytics to reduce laboratory testing and maximize cost savings for low-risk patients. An institutionally based risk model was developed for pancreaticoduodenectomy and hepatectomy, and an ACS-NSQIP®–based model was developed for distal pancreatectomy. Patients were stratified in each model to the CML by individual risk of major complications, readmission, or death. Clinical outcomes and estimated cost savings were compared with those of a historical cohort with standard of care. Over 34 months, 394 patients stratified to the CML for pancreaticoduodenectomy or hepatectomy saved an estimated $803,391 (44.4%). Over 13 months, 52 patients stratified to the CML for distal pancreatectomy saved an estimated $81,259 (30.5%). Clinical outcomes for 30-day major complications, readmission, and mortality were unchanged after implementation of either model. Predictive analytics can target low-risk patients to reduce laboratory testing and improve cost savings, regardless of whether an institutional or a generalized risk model is implemented. Broader application is important in patient-centered health care and should transition from predictive to prescriptive analytics to guide individual care in real time.

The role of disruptive technologies and approaches in ERAS®: erupting change through disruptive means

“…Amazon Pharmacy Mail service prescription medication; pre- and post-operative treatment delivery (i.e., colon bundle, nutrition) to improve compliance and access Schwab et al (2019) [ 7 ] 6. Prescriptive analytics Use of data mining, predictive modeling and machine learning to risk-stratify and improve patient care Pickens et al (2019) [ 8 ] 7. Prehabilitation smart device sensors Smart sensors (i.e., Fitbits) to monitor at-home prehabilitation targets such as VO2 max to improve physical capacity prior to surgery Baimas-George et al (2020) [ 9 ] C: Intra- and post-operative care delivery 8.…”

“…Other disruptive technologies encountered in conjunction with ERAS® include but are not limited to simulation for team building, cumulative sum analytics (CUSUM), and prescriptive analytics [ 8 , 20 , 26 ]. While each of these and the presented disruptive technologies begin through, yes, disruption, and perhaps, yes, significant frustration, each offers benefit and improvement if given the chance.…”