Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

Santiago Romero-Brufau; Kirk D. Wyatt; Patricia Boyum; Mindy Mickelson; Matthew Moore; Cheristi Cognetta-Rieke

doi:10.1055/s-0040-1715827

Applied Clinical Informatics, Inhaltsverzeichnis

Appl Clin Inform 2020; 11(04): 570-577
DOI: 10.1055/s-0040-1715827

Research Article

Implementation of Artificial Intelligence-Based Clinical Decision Support to Reduce Hospital Readmissions at a Regional Hospital

Santiago Romero-Brufau

¹Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States

²Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, Massachusetts, United States

,

Kirk D. Wyatt

³Division of Pediatric Hematology/Oncology, Department of Pediatric and Adolescent Medicine, Mayo Clinic, Rochester, Minnesota, United States

,

Patricia Boyum

¹Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States

,

Mindy Mickelson

¹Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States

,

Matthew Moore

¹Mayo Clinic Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, United States

,

Cheristi Cognetta-Rieke

⁴Department of Nursing, Mayo Clinic Health System, La Crosse, La Crosse, Wisconsin, United States

› Institutsangaben

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.

Keywords

intelligence - artificial - patient readmission - delivery of health care - quality of health care technology assessment - biomedical - clinical decision-making - decision-making

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