Abstract
BackgroundPatients 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. 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.MethodsData 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. Cox models were applied to test the associations between delivery of services and re-incarceration. Additionally, machine learning was used to train and validate a predictive model to examine effects of non-modifiable risk factors (age, past arrests, mental health diagnosis) and modifiable risk factors (outpatient, medical and case management services, and use of a jail diversion program) on re-arrest outcome.ResultsAn association was found between past arrests and admission to crisis stabilization services in this population (N = 10,307). Delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. 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).ConclusionsBy modeling the complex interactions between risk factors, service delivery, and outcomes, systems of care might be better enabled to meet patient needs and improve outcomes.
Highlights
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
We focused on individuals with serious mental illness (SMI) who had one of the following diagnoses: bipolar disorder (International Classification of Diseases, 9th edition [ICD-9] codes: 296 to 296.19 and 296.40 to 296.99); schizophrenic disorder (ICD-9 codes: 295 to 295.99 and 297 to 298.99); major depression (ICD-9 codes: 296.20 to 296.39)
Population selection To analyze the relations between arrest and behavioral health service events we focused on a subset of the adult population having records both in the Criminal Justice Information Services (CJIS) and mental health ecosystem datasets (n = 5148)
Summary
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. The lack of continuous care for adults with serious mental illness who are navigating the mental health, Falconer et al Health and Justice (2017) 5:4 social, and criminal justice systems limits our ability to perform research to determine ways in which we might intervene to address the risk factors for adverse outcomes. Discontinuous care, and different agencies maintaining isolated datasets, there is a lack of access to continuous patient-level data. This makes it difficult to collate data across health, social, and criminal justice agencies and to evaluate the interplay between risk factors, the delivery of services, and outcomes. A critical need exists to evaluate continuous patient-level and service-level data across multiple agencies in order to understand the mechanisms through which we may intervene to prevent or delay psychiatric crisis
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