Browsing by Subject "Clinical Global Impression (CGI)"

Now showing 1 - 2 of 2
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    Assessing Outcomes of Evidence-Based Practices Through Violence Risk Assessment and Clinical Global Impression (CGI) Analysis in Children with Autism Spectrum Disorder
    (2023-04-28) Darsanapu, Archana; Neal, Tiffany; Deodhar, Aditi; Swiezy, Naomi
    This practicum at HANDS in Autism®, in collaboration with the Indiana NeuroDiagnostic Institute (NDI), aimed to evaluate the effectiveness of an evidence-based coordinated care approach for youth with Autism Spectrum Disorder (ASD) receiving inpatient psychiatric services. The study compared pre-admission data collected via Cerner and post-discharge data collected through REDCap for 42 individuals. Using the Clinical Global Impression (CGI) scale and Violence Risk Assessment (VRA), the project measured behavioral outcomes following treatment. Data were coded, analyzed using Python and Excel, and visualized to assess changes in severity and improvement scores. Results showed that 31 of 42 patients demonstrated measurable improvement. The findings support the use of integrated data systems and standardized behavioral tools to monitor treatment impact and reduce risks associated with behavioral health crises. This project contributes to ongoing evaluation of treatment interventions and development of comprehensive, individualized care plans for youth with ASD.
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    Stabilizing Behavioral Changes and Preventing Readmission in Individuals with Autism Spectrum Disorder Through Transitional Support Strategies
    (2022-08) Achanti, Sai Yashashwini; Neal, Tiffany; Deodhar, Aditi; Swiezy, Naomi
    Youth with Autism Spectrum Disorder (ASD) often experience behavioral challenges requiring coordinated and sustained inpatient support. This practicum, conducted at the Neurodiagnostic Institute (NDI) in partnership with HANDS in Autism®, focused on evaluating stabilization outcomes and the effectiveness of post-discharge planning in preventing readmissions. Electronic health record data were extracted from Cerner and categorized using the Aberrant Behavior Checklist (ABC) and Clinical Global Impression (CGI) scales. The data were further aligned with moderator variables to examine behavioral outcomes across inpatient care and post-discharge periods. Tools including R, REDCap, and Excel were used to manage, code, and visualize the data. Findings revealed patterns in behavior change trajectories that may inform individualized care planning and system-wide improvements. This analysis supports the use of evidence-based behavioral classifications and quantitative tracking to enhance continuity of care, reduce readmissions, and improve long-term outcomes for individuals with ASD in psychiatric inpatient settings.