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. 2017 Dec;5(1):4.
doi: 10.1186/s40352-017-0049-y. Epub 2017 Mar 22.

Integrated multisystem analysis in a mental health and criminal justice ecosystem

Erin Falconer  1 , Tal El-Hay  2 , Dimitris Alevras  3 , John P Docherty  4 , Chen Yanover  2 , Alan Kalton  5   6 , Yaara Goldschmidt  2 , Michal Rosen-Zvi  2
Affiliations

Integrated multisystem analysis in a mental health and criminal justice ecosystem

Erin Falconer et al. Health Justice. 2017 Dec.

Abstract

Background: 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. 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.

Methods: 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. 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.

Results: An 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).

Conclusions: By 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.

Keywords: Arrest; Criminal justice system; Healthcare system; Hospitalization; Risk factors; Serious mental illness.

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Figures

Fig. 1
Fig. 1
Selection of patient populations. CJIS = Criminal Justice Information Services; SMI = serious mental illness
Fig. 2
Fig. 2
Effect of case management (a) and access to medical services (b) on arrest probability. Kaplan-Meier estimators. Red line=did not receive service, blue line=received service
Fig. 3
Fig. 3
ROC curve of elastic net predictive model for re-arrest outcome. ROC = receiver operating characteristic. Basic model=dotted line; Full model=solid line
See this image and copyright information in PMC

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