Implementation of an artificial intelligence-based clinical decision support tool reduced the 30-day hospital readmission rate by an adjusted absolute 2.8% (relative 25%) compared to standard care.
Observational (n=2,460)
Yes
Does artificial intelligence-based clinical decision support reduce unplanned hospital readmissions in patients admitted to general care units?
Implementation of an AI-based clinical decision support tool significantly reduced unplanned hospital readmissions compared to control hospitals.
Effect estimate: ARR 2.8%
Absolute Event Rate: 8.1% vs 11.4%
p-value: p=<0.001
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.
Romero‐Brufau et al. (Sat,) conducted a observational in Hospital readmission (n=2,460). Artificial intelligence-based clinical decision support vs. Standard care (historical and concurrent control hospitals) was evaluated on 30-day all-cause hospital readmission rate (ARR 2.8%, p=<0.001). Implementation of an artificial intelligence-based clinical decision support tool reduced the 30-day hospital readmission rate by an adjusted absolute 2.8% (relative 25%) compared to standard care.