Browsing by Author "McNamee, Cassidy"

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    Acceptance of Automated Social Risk Scoring in the Emergency Department: Clinician, Staff, and Patient Perspectives
    (University of California, 2024) Mazurenko, Olena; Hirsh, Adam T.; Harle, Christopher A.; McNamee, Cassidy; Vest, Joshua R.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Introduction: Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients' health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED. Methods: Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding. Results: Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases. Conclusion: Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED.
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    Community Mental Health Needs Assessment Report for Marion County
    (Richard M. Fairbanks School of Public Health, 2023-06-23) Greene, Marion; Sanner, Lindsey; McNamee, Cassidy
    The purpose of this project was to conduct a community mental health needs assessment (CMHNA) specific to the services available for serious mental illness and substance use disorders across the lifespan in Marion County, Indiana. The CMHNA was developed in collaboration with Sandra Eskenazi Mental Health Center, Community Fairbanks Behavioral Health, Aspire Indiana Health, and Adult and Child Health. These four organizations are Division of Mental Health and Addiction (DMHA) designated Community Mental Health Centers (CMHCs) as well as grantees of the Substance Abuse and Mental Health Services Administration’s Certified Community Behavioral Health Center (CCBHC) award. This report contains all findings from the assessment, including: a. Identification of the level of need, including cultural, linguistic, treatment and staffing needs b. Identification of the level of services available, including resources to address transportation, income, culture, and other barriers c. Identification of the gaps between needs and services d. Recommendations derived from the interviews and other data.
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    Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department
    (Public Library of Science, 2024-11-20) Mazurenko, Olena; Hirsh, Adam T.; Harle, Christopher A.; Shen, Joanna; McNamee, Cassidy; Vest, Joshua R.; Health Policy and Management, Richard M. Fairbanks School of Public Health
    Background: Health-related social needs (HRSNs), such as housing instability, food insecurity, and financial strain, are increasingly prevalent among patients. Healthcare organizations must first correctly identify patients with HRSNs to refer them to appropriate services or offer resources to address their HRSNs. Yet, current identification methods are suboptimal, inconsistently applied, and cost prohibitive. Machine learning (ML) predictive modeling applied to existing data sources may be a solution to systematically and effectively identify patients with HRSNs. The performance of ML predictive models using data from electronic health records (EHRs) and other sources has not been compared to other methods of identifying patients needing HRSN services. Methods: A screening questionnaire that included housing instability, food insecurity, transportation barriers, legal issues, and financial strain was administered to adult ED patients at a large safety-net hospital in the mid-Western United States (n = 1,101). We identified those patients likely in need of HRSN-related services within the next 30 days using positive indications from referrals, encounters, scheduling data, orders, or clinical notes. We built an XGBoost classification algorithm using responses from the screening questionnaire to predict HRSN needs (screening questionnaire model). Additionally, we extracted features from the past 12 months of existing EHR, administrative, and health information exchange data for the survey respondents. We built ML predictive models with these EHR data using XGBoost (ML EHR model). Out of concerns of potential bias, we built both the screening question model and the ML EHR model with and without demographic features. Models were assessed on the validation set using sensitivity, specificity, and Area Under the Curve (AUC) values. Models were compared using the Delong test. Results: Almost half (41%) of the patients had a positive indicator for a likely HRSN service need within the next 30 days, as identified through referrals, encounters, scheduling data, orders, or clinical notes. The screening question model had suboptimal performance, with an AUC = 0.580 (95%CI = 0.546, 0.611). Including gender and age resulted in higher performance in the screening question model (AUC = 0.640; 95%CI = 0.609, 0.672). The ML EHR models had higher performance. Without including age and gender, the ML EHR model had an AUC = 0.765 (95%CI = 0.737, 0.792). Adding age and gender did not improve the model (AUC = 0.722; 95%CI = 0.744, 0.800). The screening questionnaire models indicated bias with the highest performance for White non-Hispanic patients. The performance of the ML EHR-based model also differed by race and ethnicity. Conclusion: ML predictive models leveraging several robust EHR data sources outperformed models using screening questions only. Nevertheless, all models indicated biases. Additional work is needed to design predictive models for effectively identifying all patients with HRSNs.