Prediction of outpatient visits and expenditure under the Universal Coverage Scheme in Bangkok using subscriber's attributes: A random forest analysis.

Objectives: There is limited evidence on methods to allocate budgets to healthcare providers under capitation schemes. The objective of this study was to construct and test models that predict outpatient visits and expenditure for each healthcare facility using subscriber data from the preceding year. Study design: We used the database of the Universal Coverage Scheme in Bangkok, Thailand that stores subscriber information and healthcare service utilization data. One-percent and ten-percent random samples of subscribers were selected as training and testing groups, respectively. Methods: Using data of the training group, we constructed a model using a random forest algorithm to predict outpatient visits and expenditure in 2017 from the 2016 data. The model was applied to the testing group and facility-level predicted number of visits and expenditure were compared with actual data. Results: The identically-structured training and testing groups consisted of 37,259 and 371,650 subscribers, respectively. Approximately 25% of subscribers utilized outpatient services. The R2 for models predicting facility-level utilization rate (visits/subscribers) and expenditure per subscriber in 2017 were 0.85 and 0.75, respectively. Conclusions: The model to predict outpatient visits and expenditure performed well. Such a prediction model may be useful for allocating budgets to healthcare facilities under capitation systems.