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

A mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare.

Is change over time in psychotic symptoms related to social functioning?

Objective: In psychosis, treatment often focuses on symptom reduction whereas social functioning is also essential. In this study, we investigate positive psychotic symptoms and medication use in relation to social functioning over a 3-year time-period in 531 patients diagnosed with psychosis. Furthermore, relations of positive symptoms with needs for care and quality of life were also investigated.

Using machine learning to predict mental healthcare consumption in non-affective psychosis.

Objective: The main goal of the study was to predict individual patients' future mental healthcare consumption, and thereby enhancing the design of an efficient demand-oriented mental healthcare system by focusing on a patient population associated with intensive mental healthcare consumption. Factors that affect the mental healthcare consumption of service users with non-affective psychosis were identified, and subsequently used in a prognostic model to predict future healthcare consumption.

Method: This study was a secondary analysis of an existing dataset from the GROUP study.

Symptomatic and Functional Remission in Young Adults with a Psychotic Disorder in a Rehabilitation Focused Team.

The aim of this study is to assess symptomatic remission (SR) and functional remission (FR) in a rehabilitation focused program for young adults with a psychotic disorder in the Netherlands, and to investigate which individual and mental health care factors are associated with SR and/or FR, by using Routine Outcome Monitoring data and data on met needs and unmet needs for care. Data of 287 young adults were collected. Almost 40% achieved or maintained SR, 34% FR, and 26% achieved or maintained both.