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

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.

Risk stratification of syncope: Current syncope guidelines and beyond

“…AI-based interventions can match or even outperform physicians' skills in predictive modeling because of the ability to process multiple variables simultaneously across large datasets by adequately trained and validated machine learning (ML) algorithms (D'Ascenzo et al, 2021). AI solutions have shown potential to improve patient care, reduce the frequency of adverse events (Bates et al, 2021), decrease the rate of hospital admission and costs of inappropriate treatments and hospitalization, overall ensuring more effective and equitable use of resources (Romero-Brufau et al, 2020). There are good opportunities to implement successfully alternative prediction tools based on artificial neural networks that can be leveraged to improve the risk stratification of syncope patients in the ED.…”

Effect of a Real-Time Risk Score on 30-day Readmission Reduction in Singapore

“…A real-time high-risk flag was configured in EMR storyboards and flow sheets to meet the needs of the inpatient medical, nursing, case management, ED, and postdischarge care teams. 5,29 The list of 30-day readmission high-risk patients was flagged to clinicians and case managers in real time for early interventions during the patients' stay and postdischarge (►Fig. 1C).…”

“…[19][20][21][22][23][24][25][26][27] These technologies serve as drivers for clinical decision support systems that enable informed data-driven decision-making, support clinical research, and improve quality of care. 28,29 Many health systems have developed 30-day readmission models to identify patients with high risk of readmissions for risk stratification and predictive modeling. [30][31][32][33][34][35] These tools relied on retrospective or real-time administrative data, with cstatistics ranging from 0.55 to 0.72.…”

Augmented Intelligence to Predict 30-day Mortality in Patients with Cancer

“…The technique to identify relevance of patients is similar to identifying signal-to-noise ratio in signal processing systems [26]. The results of this approach have been shown to change clinician behavior and improve outcomes in other clinical areas, such as 30-day readmissions [27].…”