Using Explainable Artificial Intelligence to Predict Potentially Preventable Hospitalizations

A Population-Based Cohort Study in Denmark

Bibliographic Data

ID9103477
AuthorsAnders Hammerich Riis (0000-0002-6684-4068, Horsens Regional Hospital, Horsens, Denmark, corresponding author), Pia Kjær Kristensen (0000-0001-5473-9386, Department of Clinical Epidemiology, Aarhus University Hospital), Simon Meyer Lauritsen (0000-0001-8823-5047, Enversion A/S, Aarhus, Denmark), Bo Thiesson (Enversion A/S, Aarhus, Denmark), Marianne Johansson Jørgensen (0000-0003-3932-4822, Horsens Regional Hospital, Horsens, Denmark, corresponding author)
Year2023
Volume61
Issue4
Pages226-236
Publication date2023-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueMedical Care (JOURNAL)
Journal identifiersISSN: 0025-7079 • E-ISSN: 1537-1948
PublisherOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/mlr.0000000000001830
PMID36893408
OpenAlexW4323659035
LanguageEN
Citations received1
References cited28

BACKGROUND: The increasing aging population and limited health care resources have placed new demands on the healthcare sector. Reducing the number of hospitalizations has become a political priority in many countries, and special focus has been directed at potentially preventable hospitalizations. OBJECTIVES: We aimed to develop an artificial intelligence (AI) prediction model for potentially preventable hospitalizations in the coming year, and to apply explainable AI to identify predictors of hospitalization and their interaction. METHODS: We used the Danish CROSS-TRACKS cohort and included citizens in 2016-2017. We predicted potentially preventable hospitalizations within the following year using the citizens' sociodemographic characteristics, clinical characteristics, and health care utilization as predictors. Extreme gradient boosting was used to predict potentially preventable hospitalizations with Shapley additive explanations values serving to explain the impact of each predictor. We reported the area under the receiver operating characteristic curve, the area under the precision-recall curve, and 95% confidence intervals (CI) based on five-fold cross-validation. RESULTS: The best performing prediction model showed an area under the receiver operating characteristic curve of 0.789 (CI: 0.782-0.795) and an area under the precision-recall curve of 0.232 (CI: 0.219-0.246). The predictors with the highest impact on the prediction model were age, prescription drugs for obstructive airway diseases, antibiotics, and use of municipality services. We found an interaction between age and use of municipality services, suggesting that citizens aged 75+ years receiving municipality services had a lower risk of potentially preventable hospitalization. CONCLUSION: AI is suitable for predicting potentially preventable hospitalizations. The municipality-based health services seem to have a preventive effect on potentially preventable hospitalizations

MEDLINE · Political science · Chronic Disease Management Strategies · Computer Science · Frailty in Older Adults · Primary Care and Health Outcomes

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