The use of clinical, behavioral, and social determinants of health to improve identification of patients in need of advanced care for depression

Indiana University-Purdue University Indianapolis (IUPUI)Depression is the most commonly occurring mental illness the world over. It poses a significant health and economic burden across the individual and community. Not all occurrences of depression require the same level of treatment. However, identifying patients in need of advanced care has been challenging and presents a significant bottleneck in providing care. We developed a knowledge-driven depression taxonomy comprised of features representing clinical, behavioral, and social determinants of health (SDH) that inform the onset, progression, and outcome of depression. We leveraged the depression taxonomy to build decision models that predicted need for referrals across: (a) the overall patient population and (b) various high-risk populations. Decision models were built using longitudinal, clinical, and behavioral data extracted from a population of 84,317 patients seeking care at Eskenazi Health of Indianapolis, Indiana. Each decision model yielded significantly high predictive performance. However, models predicting need of treatment across high-risk populations (ROC’s of 86.31% to 94.42%) outperformed models representing the overall patient population (ROC of 78.87%). Next, we assessed the value of adding SDH into each model. For each patient population under study, we built additional decision models that incorporated a wide range of patient and aggregate-level SDH and compared their performance against the original models. Models that incorporated SDH yielded high predictive performance. However, use of SDH did not yield statistically significant performance improvements. Our efforts present significant potential to identify patients in need of advanced care using a limited number of clinical and behavioral features. However, we found no benefit to incorporating additional SDH into these models. Our methods can also be applied across other datasets in response to a wide variety of healthcare challenges

Improving Community Adaptation Outcomes for Youth Graduating from Residential Mental Health Programs: A Synthesis Review (FULL REPORT)

The focus of this synthesis review was to understand the capacity of systems of care and integrated program models to foster successful community adaptation for children and youth graduating from children\u27s residential mental health treatment

Creating the Organizational Capacity to Serve Families with Parental Mental Illness: The Implementation of Family Options

Summary: The purpose of this presentation is to present preliminary findings describing the organizational context of a traditionally adult-serving community mental health program, Employment Options, Inc., as they implement a family-centered, strengths-based intervention for families living with parental mental illness

An Analysis of the Current United States and State of Washington\u27s Mental Health Policies Serving Children and Families

Due to continued fragmentation and gaps in mental health services and the increase in the prevalence of mental health problems for children, youth, and their families, these populations remain underserved. In 2003, the federal New Freedom Commission (Commission) responded by publishing policies to address these concerns. As directed in 2005, the Substance Abuse and Mental Health Services Administration (SAMHSA) funded planning incentive grants to states to transform their delivery of care. The study reviewed the federal policy, specifically the recommendations of the Commission’s Subcommittee on Children and Families, and Washington State’s policy and implementation actions of its five-year SAMHSA incentive grant. The method included searching, reviewing, and analyzing the literature on the topic published sinceapproximately 2002. The analysis distilled the recommended determinants in children’s mental health care transformation: prevention, early intervention, and screening in child welfare (juvenile justice and foster care) strategies; evidence-based practices; geographic disparities; workforce barriers; cultural competence aspirations; and consumer, schoolbased mental health, and primary care providers’ role expectations. Despite innumerable studies, policies and services remain fragmented with gaps. The following topics from the outcome data require continuing attention: increasing the 2 cultural competency of professional services that are efficacious, and designing and promulgating measures for evidence-based practice specific to children. Three themes emerged regarding how to serve children’s mental health needs in Washington State in a more efficacious manner. Within the penumbras of cultural competency and outcome-based measures, constructs for evidence-based practice for children need to be age-developmentally appropriate. Simultaneously, both the family role and venues of service delivery need to be considered, e.g., schools, out-of-home placement, and clinics. Access to mental health care through schools and primary care providers needs to be collaborative with behavioral health professionals. School policy needs to link students’ attendance and achievement with their physical and behavioral health. Training for the mental health workforce requires increased cultural competency. Rural mental health care requires incentives to train and retain a workforce reflective of the demographics, particularly in the areas populated by persons of color. Also, the number of prescribers needs to increase through certification of nurse practitioners and psychologists

Generative Adversarial Networks for Creating Synthetic Free-Text Medical Data: A Proposal for Collaborative Research and Re-use of Machine Learning Models

Restrictions in sharing Patient Health Identifiers (PHI) limit cross-organizational re-use of free-text medical data. We leverage Generative Adversarial Networks (GAN) to produce synthetic unstructured free-text medical data with low re-identification risk, and assess the suitability of these datasets to replicate machine learning models. We trained GAN models using unstructured free-text laboratory messages pertaining to salmonella, and identified the most accurate models for creating synthetic datasets that reflect the informational characteristics of the original dataset. Natural Language Generation metrics comparing the real and synthetic datasets demonstrated high similarity. Decision models generated using these datasets reported high performance metrics. There was no statistically significant difference in performance measures reported by models trained using real and synthetic datasets. Our results inform the use of GAN models to generate synthetic unstructured free-text data with limited re-identification risk, and use of this data to enable collaborative research and re-use of machine learning models

An Evaluation of Computational Methods to Support the Clinical Management of Chronic Disease Populations

Innovative primary care models that deliver comprehensive primary care to address medical and social needs are an established means of improving health outcomes and reducing healthcare costs among persons living with chronic disease. Care management is one such approach that requires providers to monitor their respective patient panels and intervene on patients requiring care. Health information technology (IT) has been established as a critical component of care management and similar care models. While there exist a plethora of health IT systems for facilitating primary care, there is limited research on their ability to support care management and its emphasis on monitoring panels of patients with complex needs. In this dissertation, I advance the understanding of how computational methods can better support clinicians delivering care management, and use the management of human immunodeficiency virus (HIV) as an example scenario of use. The research described herein is segmented into 3 aims; the first was to understand the processes and barriers associated with care management and assess whether existing IT can support clinicians in this domain. The second and third aim focused on informing potential solutions to the technological shortcomings identified in the first aim. In the studies of the first aim, I conducted interviews and observations in two HIV primary care programs and analyzed the data generated to create a conceptual framework of population monitoring and identify challenges faced by clinicians in delivering care management. In the studies of the second aim, I used computational methods to advance the science of extracting from the patient record social and behavioral determinants of health (SBDH), which are not easily accessible to clinicians and represent an important barrier to care management. In the third aim, I conducted a controlled experimental evaluation to assess whether data visualization can improve clinician’s ability to maintain awareness of their patient panels

Neighbourhood-level socioeconomic status and prevalence of teacher-reported health disorders among Canadian kindergarten children

BackgroundThe evidence on the association between neighborhood-level socioeconomic status (SES) and health disorders in young children is scarce. This study examined the prevalence of health disorders in Canadian kindergarten (5–6 years old) children in relation to neighborhood SES in 12/13 Canadian jurisdictions.MethodsData on child development at school entry for an eligible 1,372,980 children out of the total population of 1,435,428 children from 2004 to 2020, collected using the Early Development Instrument (EDI), were linked with neighborhood sociodemographic data from the 2006 Canadian Census and the 2005 Taxfiler for 2,058 neighborhoods. We examined the relationship using linear regressions. Children’s HD included special needs, functional impairments limiting a child’s ability to participate in classroom activities, and diagnosed conditions.ResultsThe neighborhood prevalence of health disorders across Canada ranged from 1.8 to 46.6%, with a national average of 17.3%. The combined prevalence of health disorders was 16.4%, as 225,711 children were identified as having at least one health disorder. Results of an unadjusted linear regression showed a significant association between neighborhood-level SES and prevalence of health disorders (F(1, 2051) = 433.28, p < 0.001), with an R2 of 0.17. When province was added to the model, the R2 increased to 0.40 (F(12, 2040) = 115.26, p < 0.001). The association was strongest in Newfoundland & Labrador and weakest in Ontario.ConclusionOur study demonstrated that the prevalence of health disorders among kindergarten children was higher in lower SES neighborhoods and varied by jurisdiction in Canada, which has implications for practice and resource allocation

Risk Management for Persons with Serious Mental Illness: A Process Analysis of Washington State Department of Corrections\u27 Tools

Although many evidence-based techniques are outlined in the literature, systems often assess, plan, and mitigate risk for Persons with Serious Mental Illness (PSMI) in significantly divergent ways. For more than 20 years now, the Washington State Department of Corrections has relied on the Offender Reentry Community Safety Program (ORCSP) to appraise dangerousness and presence of mental disorder, utilizing a staged process that considers a wide-ranging set of criminogenic and non-criminogenic variables. A growing body of research suggests that the ORCSP is effectively decreasing recidivism through collaborative reentry planning and mitigation between mental health and criminal justice professionals; however, whether ORCSP participant screening methods are valid or reliable remains untested. Without a cohesive assessment theory or comprehensive exploration of recidivism trends, increased scrutiny must be given to findings. In an effort to clarify these issues, this dissertation evaluates current and historical ORCSP assessment processes, overviews national standards and best-practices for PSMI risk management, and provides a set of practical recommendations to improve selection efficiency