Santiago Romero-Brufau1,2, Kirk D Wyatt3, Patricia Boyum1, Mindy Mickelson1, Matthew Moore1, Cheristi Cognetta-Rieke4. 1. Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States. 2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts, United States. 3. Division of Pediatric Hematology/Oncology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, United States. 4. Department of Nursing, Mayo Clinic Health System, La Crosse, La Crosse, Wisconsin, United States.
Abstract
BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. OBJECTIVE: The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. METHODS: A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. RESULTS: Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. CONCLUSION: We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions. Georg Thieme Verlag KG Stuttgart · New York.
BACKGROUND: Hospital readmissions are a key quality metric, which has been tied to reimbursement. One strategy to reduce readmissions is to direct resources to patients at the highest risk of readmission. This strategy necessitates a robust predictive model coupled with effective, patient-centered interventions. OBJECTIVE: The aim of this study was to reduce unplanned hospital readmissions through the use of artificial intelligence-based clinical decision support. METHODS: A commercially vended artificial intelligence tool was implemented at a regional hospital in La Crosse, Wisconsin between November 2018 and April 2019. The tool assessed all patients admitted to general care units for risk of readmission and generated recommendations for interventions intended to decrease readmission risk. Similar hospitals were used as controls. Change in readmission rate was assessed by comparing the 6-month intervention period to the same months of the previous calendar year in exposure and control hospitals. RESULTS: Among 2,460 hospitalizations assessed using the tool, 611 were designated by the tool as high risk. Sensitivity and specificity for risk assignment were 65% and 89%, respectively. Over 6 months following implementation, readmission rates decreased from 11.4% during the comparison period to 8.1% (p < 0.001). After accounting for the 0.5% decrease in readmission rates (from 9.3 to 8.8%) at control hospitals, the relative reduction in readmission rate was 25% (p < 0.001). Among patients designated as high risk, the number needed to treat to avoid one readmission was 11. CONCLUSION: We observed a decrease in hospital readmission after implementing artificial intelligence-based clinical decision support. Our experience suggests that use of artificial intelligence to identify patients at the highest risk for readmission can reduce quality gaps when coupled with patient-centered interventions. Georg Thieme Verlag KG Stuttgart · New York.
Authors: Soy Chen; Danielle Bergman; Kelly Miller; Allison Kavanagh; John Frownfelter; John Showalter Journal: Am J Manag Care Date: 2020-01 Impact factor: 2.229
Authors: Daniel J Morgan; Bill Bame; Paul Zimand; Patrick Dooley; Kerri A Thom; Anthony D Harris; Soren Bentzen; Walt Ettinger; Stacy D Garrett-Ray; J Kathleen Tracy; Yuanyuan Liang Journal: JAMA Netw Open Date: 2019-03-01
Authors: Dean F Sittig; Carolyn Petersen; Stephen M Downs; Jenna S Lehmann; Christoph U Lehmann Journal: Appl Clin Inform Date: 2022-03-09 Impact factor: 2.342
Authors: Malvika Sharma; Carl Savage; Monika Nair; Ingrid Larsson; Petra Svedberg; Jens M Nygren Journal: J Med Internet Res Date: 2022-10-05 Impact factor: 7.076
Authors: Christine Xia Wu; Ernest Suresh; Francis Wei Loong Phng; Kai Pik Tai; Janthorn Pakdeethai; Jared Louis Andre D'Souza; Woan Shin Tan; Phillip Phan; Kelvin Sin Min Lew; Gamaliel Yu-Heng Tan; Gerald Seng Wee Chua; Chi Hong Hwang Journal: Appl Clin Inform Date: 2021-05-19 Impact factor: 2.342