Browsing by Author "Walter-McCabe, Heather"

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    The Challenges of Conducting Intrastate Policy Surveillance: A Methods Note on County and City Laws in Indiana
    (APHA, 2021-06) Sanner, Lindsey; Grant, Sean; Walter-McCabe, Heather; Silverman, Ross D.; Social and Behavioral Sciences, School of Public Health
    Policy surveillance is critical in examining the ways law functions as a structural and social determinant of health. To date, little policy surveillance research has focused on examining intrastate variations in the structure and health impact of laws. Intrastate policy surveillance poses unique methodological challenges because of the complex legal architecture within states and inefficient curation of local laws. We discuss our experience with these intrastate policy surveillance challenges in Indiana, a state with 92 counties and several populous cities, a complicated history of home rule, systemically underfunded local governments, and variations in demography, geography, and technology adoption. In our case study, we expended significant time and resources to obtain county and city ordinances through online code libraries, jurisdiction Web sites, and (most notably) visits to offices to scan documents ourselves. A concerted effort is needed to ensure that local laws of all kinds are stored online in organized, searchable, and open access systems. Such an effort is vital to achieve the aspirational goals of policy surveillance at the intrastate level.
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    Enabling Policy to Advance Black Maternal Health Equity Through the Use of Doulas in Indiana
    (2024-08) Brown, Jenell Nicole; Archer, Sarah E.; Jackson, Emily; Walter-McCabe, Heather
    Disproportionately high maternal mortality rates (MMR) among Black women continue to be a significant public health equity concern in the United States, particularly in the state of Indiana, where Black women experienced the highest MMR at 156.3 deaths per 100,000 live births in 2021. In comparison, White women experienced 90.7 deaths per 100,000 live births in the same year (Indiana Department of Health, 2024). Equity recognizes that high-risk populations have different needs and disadvantages and aims to address disparities by providing targeted opportunities, resources, and support to account for these differences. Health equity is achieved when everyone can attain their full potential for health and well-being (World Health Organization, n.d.). Equality refers to equal access for everyone, which in turn perpetuates inequities. While not the sole solution to Indiana’s Black maternal health equity crisis, Doulas are an evidence-based solution to reducing racial disparities in maternal health outcomes. Building on the current body of literature, this dissertation explores how Indiana can enable policy to expand access to Doula care and integrate Doula services into maternal healthcare systems. Through the employment of qualitative, semi-structured interviews with Doulas and other key stakeholders, findings identified that current barriers in Indiana include a lack of a supportive environment to legitimize the profession, lack of Medicaid reimbursement and inadequate private insurance coverage, processes that “medicalize” Doula care, and the need for collaborative efforts with Doulas when crafting Doula-related policies. By leveraging a policy and stakeholder analysis, an implementation plan was developed to provide recommendations on how Indiana policy can advance Black maternal health equity through the use of Doulas. This research's importance lies in its potential to inform policy and practice in Indiana and provide foundational information for other states that have yet to begin incorporating Doula care into policy to advance maternal health equity.
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    The Role of Social Workers in Addressing Patients' Unmet Social Needs in the Primary Care Setting
    (2021-04) Bako, Abdulaziz Tijjani; Vest, Joshua R.; Blackburn, Justin; Walter-McCabe, Heather; Kasthurirathne, Suranga; Menachemi, Nir
    Unmet social needs pose significant risk to both patients and healthcare organizations by increasing morbidity, mortality, utilization, and costs. Health care delivery organizations are increasingly employing social workers to address social needs, given the growing number of policies mandating them to identify and address their patients’ social needs. However, social workers largely document their activities using unstructured or semi-structured textual descriptions, which may not provide information that is useful for modeling, decision-making, and evaluation. Therefore, without the ability to convert these social work documentations into usable information, the utility of these textual descriptions may be limited. While manual reviews are costly, time-consuming, and require technical skills, text mining algorithms such as natural language processing (NLP) and machine learning (ML) offer cheap and scalable solutions to extracting meaningful information from large text data. Moreover, the ability to extract information on social needs and social work interventions from free-text data within electronic health records (EHR) offers the opportunity to comprehensively evaluate the outcomes specific social work interventions. However, the use of text mining tools to convert these text data into usable information has not been well explored. Furthermore, only few studies sought to comprehensively investigate the outcomes of specific social work interventions in a safety-net population. To investigate the role of social workers in addressing patients’ social needs, this dissertation: 1) utilizes NLP, to extract and categorize the social needs that lead to referral to social workers, and market basket analysis (MBA), to investigate the co-occurrence of these social needs; 2) applies NLP, ML, and deep learning techniques to extract and categorize the interventions instituted by social workers to address patients’ social needs; and 3) measures the effects of receiving a specific social work intervention type on healthcare utilization outcomes.
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    Using natural language processing to classify social work interventions
    (Managed Care & Healthcare Communications, 2021-01-01) Bako, Abdulaziz Tijjani; Taylor, Heather L.; Wiley, Kevin, Jr.; Zheng, Jiaping; Walter-McCabe, Heather; Kasthurirathne, Suranga N.; Vest, Joshua R.; Health Policy and Management, School of Public Health
    Objectives: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients' social needs using natural language processing (NLP) and machine learning (ML) algorithms. Study design: Secondary data analysis of a longitudinal cohort. Methods: We extracted 815 SW encounter notes from the EHR system of a federally qualified health center. We reviewed the literature to derive a 10-category classification scheme for SW interventions. We applied NLP and ML algorithms to categorize the documented SW interventions in EHR notes according to the 10-category classification scheme. Results: Most of the SW notes (n = 598; 73.4%) contained at least 1 SW intervention. The most frequent interventions offered by social workers included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%). High-performing classification algorithms included the kernelized support vector machine (SVM) (accuracy, 0.97), logistic regression (accuracy, 0.96), linear SVM (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92). Conclusions: NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients' social needs.