Clinically Meaningful Laboratory Protocols Reduce Hospital Charges Based on Institutional and ACS-NSQIP® Risk Calculators in Hepatopancreatobiliary Surgery. Issue 8 (August 2019)

• Record Type: Journal Article
• Authors: Pickens, Ryan C.; King, Lacey; Barrier, Misty; Tezber, Kendra; Sulzer, Jesse K.; Cochran, Allyson; Lyman, William B.; Mcclune, Garth; Iannitti, David A.; Martinie, John B.; Baker, Erin H.; Ocuin, Lee M.; Hanley, Matthew; Vrochides, Dionisios
• Is Part Of: American surgeon. Volume 85:Issue 8(2019)
• Page Start: 883
• Page End: 894
• Publication Date: 2019-08
• Subjects: Surgery -- Periodicals; Surgery -- United States -- Periodicals; 617.0973
• DOI: 10.1177/000313481908500843
• Languages: English
• ISSNs: 0003-1348

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.