title: Integrated multisystem analysis in a mental health and criminal justice ecosystem.
abstract: 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. These gaps in care may lead to increased mental health disease burden and relapse, as well as repeated incarcerations. Further, the complex health, social service, and criminal justice ecosystem within which the patient may be embedded makes it difficult to examine the role of modifiable risk factors and delivered services on patient outcomes, particularly given that agencies often maintain isolated sets of relevant data. Here we describe an approach to creating a multisystem analysis that derives insights from an integrated data set including patient access to case management services, medical services, and interactions with the criminal justice system. We combined data 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. We applied Cox models to test the associations between delivery of services and re-incarceration. Using this approach, we found an association between arrests and crisis stabilization services in this population. We also found that delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. Additionally, we used machine learning to train and validate a predictive model linking non-modifiable and modifiable risk factors and outcomes. A predictive model, constructed using elastic net regularized logistic regression, and considering age, past arrests, mental health diagnosis, as well as use of a jail diversion program, outpatient, medical and case management services predicted the probability of re-arrests with fair accuracy (AUC=.67). By modeling the complex interactions between risk factors, service delivery and outcomes, we may better enable systems of care to meet patient needs and improve outcomes.
authors.affiliation: Medical Affairs, Otsuka America Pharmaceutical, Inc., Princeton, NJ, USA.
authors.forename: Erin
authors.initials: E
authors.lastname: Falconer
authors.affiliation: IBM Research - Haifa, Israel.
authors.forename: Tal
authors.initials: T
authors.lastname: El-Hay
authors.affiliation: IBM Global Business Services, West Chester, PA, USA.
authors.forename: Dimitris
authors.initials: D
authors.lastname: Alevras
authors.affiliation: Medical Affairs, Otsuka America Pharmaceutical, Inc., Princeton, NJ, USA.
authors.forename: John
authors.initials: J
authors.lastname: Docherty
authors.affiliation: IBM Research - Haifa, Israel.
authors.forename: Chen
authors.initials: C
authors.lastname: Yanover
authors.affiliation: IBM Research - Africa, Nairobi, Kenya.
authors.forename: Alan
authors.initials: A
authors.lastname: Kalton
authors.affiliation: IBM Research - Haifa, Israel.
authors.forename: Yaara
authors.initials: Y
authors.lastname: Goldschmidt
authors.affiliation: IBM Research - Haifa, Israel.
authors.forename: Michal
authors.initials: M
authors.lastname: Rosen-Zvi
mesh_terms: D001185:Artificial Intelligence; D003416:Criminal Law; D066264:Datasets as Topic; D006297:Health Services Accessibility; D006801:Humans; D035846:Law Enforcement; D001523:Mental Disorders; D008605:Mental Health Services; D011329:Prisoners / Q000523:psychology* / Q000706:statistics & numerical data; D011330:Prisons; D016016:Proportional Hazards Models; D012307:Risk Factors; D014481:United States