Browsing by Author "Kwon, Hyejean"

Now showing 1 - 6 of 6
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    CANS and ANSA Outcome Reports Reference Guide, version 3
    (2024) Walton, Betty; Moynihan, Stephanie; Hong, Stephanie; Kwon, Hyejean
    Although behavioral health disorders are common, the quality of care has not kept pace with the quality of physical health care. Measuring behavioral health care quality has slowly evolved. Suggested quality of care initiatives include a routine process (fidelity) and outcome feedback, which has been linked to improved symptoms, quality of life, and lower readmission rates. Regularly discussing and measuring personal change is recommended. To support data-informed decisions based on personal change and to improve service quality, outcome management reports based on the Child and Adolescent Needs and Strengths (CANS) and Adult Needs and Strengths Assessment (ANSA) data were developed in DARMHA, the Indiana Division of Mental Health and Addiction's data collection and reporting platform. This reference guide describes each individual or aggregate report and provides tips to access and utilize the information.
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    Exploring Disparities in Behavioral Health Service Use in the Early Stages of the Covid-19 Pandemic
    (2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, Stephanie
    This research brief highlights the findings and takeaways from a published study comparing behavioral health service use by adults during the early COVID-19 pandemic and the previous year. From 2019 to 2020, admissions increased by 46%. Although individuals with co-occurring mental health and substance use disorders experienced the most complex challenges, the greatest increase in accessing treatment was by people with mental health concerns. More women accessed services than men. Service use increased for Multiracial and Hispanic adults, decreased for African American and White people, and remained stable for American Indians. Different service access patterns and complexity may have been related to pandemic and existing factors.
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    Improving Treatment Completion for Young Adults with Substance Use Disorder: Machine Learning-Based Prediction Algorithms
    (2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, Stephanie
    Substance Use Disorder treatment completion has been associated with positive outcomes, such as reduced relapse rates and longer periods of abstinence. A study identified factors influencing SUD treatment completion among young adults (aged 18–25) receiving publicly funded outpatient services. This research brief describes how a machine learning decision tree model explored interactions between functional behavioral health needs and strengths, criminal justice system involvement, and completing treatment. A machine learning approach made it possible to identify complex relationships among many factors, improving our understanding on where to focus treatment.
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    Longitudinal Patterns of Strengths among Youth with Psychiatric Disorders: A Latent Profile Transition Analysis
    (2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, Stephanie
    Human service agencies have historically prioritized interventions mitigating risks rather than leveraging youth and family strengths. For youth with psychiatric disorders, better understanding the variability of strengths is crucial. Strength-based interventions include many dimensions: family strengths, interpersonal relationships, optimism, spirituality, talents and interests, educational setting, involvement in care, natural supports, community engagement, and resilience. A study examined how strengths were related to recovery. This research brief summarizes the study's findings and implications for child behavioral health services.
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    Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach
    (2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, Stephanie
    As experiencing pandemic related hardships (social isolation, financial distress, and health anxiety) and racial discrimination worsened Asian American’s mental health, a study examined unique behavioral health characteristics of Asian Americans compared to White and Black Americans in behavioral health treatment. Assessment data was analyzed using descriptive and chi-squared automatic interaction detection (CHAID), a machine learning approach, to detect additional differences among groups. Asian Americans had distinct patterns of behavioral health needs compared to White and African Americans. Key takeaways inform culturally responsive practice.
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    The Intersectionality of Factors Predictng Co-occurring Disorders: A Decision Tree Model
    (2024-09) Walton, Betty; Hong, Saahoon; Kwon, Hyejean; Kim, Hea-Won; Moynihan, Stephanie
    Individuals with co-occurring psychiatric and substance use disorders (COD) face challenges accessing care, accurate diagnoses, and effective treatment. To better understand factors other than substance use, which differentiates COD from psychiatric disorders PD, a study examined the combined effects of age, gender identity, race/ethnicity, pandemic, behavioral health needs, useful strengths, and COD. Involvement in recovery, active participation in treatment and managing one’s health, was the strongest predictor of having COD. This research brief highlights finding and key takeaways with implication for creating accessible, effective services, building life functioning skills, identifying risky behavior, and person-centered recovery planning.