Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach

This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N  = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual pr edictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Neverthele ss, uncertainty in the prediction of high-cost patients remains a challenge.
Source: Administration and Policy in Mental Health and Mental Health Services Research - Category: Psychiatry Source Type: research