All individual patient data used for the analysis were collected by providers after obtaining appropriate consents and agreements.A third party processed the provider data to remove any personal information to protect patient privacy.Predictive models linked non-modifiable and modifiable risk factors and outcomes and predicted the probability of re-arrests with fair accuracy (area under the receiver operating characteristic curve of 0.67).The mental healthcare system in the United States is fragmented, inconsistent, and underfunded.The resulting de-identified data sets were then made available for analysis.
Patients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations.
Our approach employed an association analysis and a machine learning analysis to explore the answers to the first and second questions, respectively.
Additionally, we tested the association of risk factors to crisis stabilization unit (CSU) admission.
This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system.
Data were combined from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system.
With fragmented, discontinuous care, and different agencies maintaining isolated datasets, there is a lack of access to continuous patient-level data.