Predicting healthcare utilisation outcomes with artificial intelligence: a large scoping review

Carlos Gallego-Moll, Lucía A. Carrasco-Ribelles, Marc Casajuana, Laia Maynou, Pablo Arocena, Concepción Violán and Edurne Zabaleta-Del-Olmo

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Objectives: To broadly map the research landscape to identify trends, gaps, and opportunities in data sets, methodologies, outcomes, and reporting standards for artificial intelligence (AI)-based healthcare utilization prediction. Methods: We conducted a scoping review following the Joanna Briggs Institute methodology. We searched 3 major international databases (from inception to January 2025) for studies applying AI in predictive healthcare utilization. Extracted data were categorized into data sets characteristics, AI methods and performance metrics, predicted outcomes, and adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) + AI reporting guidelines. Results: Among 1116 records, 121 met inclusion criteria. Most were conducted in the United States (62%). No study incorporated all 6 relevant variable groups: demographic, socioeconomic, health status, perceived need, provider characteristics, and prior utilization. Only 7 studies included 5 of these groups. The main data sources were electronic health records (60%) and claims (28%). Ensemble models were the most frequently used (66.9%), whereas deep learning models were less common (16.5%). AI methods were primarily used to predict future events (90.1%), with hospitalizations (57.9%) and visits (33.1%) being the most predicted outcomes. Adherence to general reporting standards was moderate; however, compliance with AI-specific TRIPOD + AI items was limited. Conclusions: Future research should broaden predicted outcomes to include process- and logistics-oriented events, extend applications beyond prediction—such as cohort selection and matching—and explore underused AI methods, including distance-based algorithms and deep neural networks. Strengthening adherence to TRIPOD-AI reporting guidelines is also essential to enhance the reliability and impact of AI in healthcare planning and economic evaluation.

Keywords: artificial intelligence; health economics; healthcare utilisation outcomes; resource allocation; review (search for similar items in EconPapers)
JEL-codes: J1 (search for similar items in EconPapers)
Date: 2025-08-01
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Published in Value in Health, 1, August, 2025. ISSN: 1098-3015

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