The RCMI Program in Health Disparities at Meharry Medical College - Supplement

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PROJECT SUMMARY Substance use disorders (SUDs) are a major public health issue that has recently more than doubled in prevalence among Americans in the last few years, from affecting 20 million in 2018 to 46.3 million Americans in 2021. Those affected by SUD disproportionately experience negative social determinants of health (SDoH), including inadequate access to safe housing, transportation, education, employment opportunities, and nutritious foods. These SDoH are connected with low self-esteem, self-efficacy, and failed attempts at SUD abstinence. Collecting and integrating SDoH information as part of patients’ electronic health records (EHR) for clinical modeling could help uncover patient experiences and behaviors related to SUD, but the majority of SDoH are embedded in unstructured free text. Additionally, the SUD diagnosis is under represented in structural EHR. Therefore, an automated and more accurate approach is needed to extract SDoH and identify SUDs. Natural language processing (NLP) can unlock the information conveyed in clinical narratives, thus playing a critical role in real-world studies. Methods and tools are being developed to facilitate such extractions; however, these tools are still under study for cohort-specific samples and unstructured text analytics is complex. To advance health disparity studies and improve understanding of patient characteristics of the SUD patients from the underserved population at Meharry, it is a high priority to explore machine learning tools to extract SDoH factors and identify SUDs diagnosis. In this proposal, we will address the challenge of SDoH extraction and SUDs identification from unstructured clinical notes or patient surveys to generate a consistent framework that can aid in identifying, understanding, treating, and predicting SUDs and associated outcomes (e.g. relapse). We will solve this challenge by developing two NLP pipelines, one for SDoH extraction and one for SUDs identification. We will focus on SUD patients with cocaine, cannabis, and opioid use disorders. The SDoH NLP pipeline will mine and enrich data in five SDoH domains defined by CDC including economic stability, education, health care access, neighborhood environment, and social and community context. Name entity recognition methods will be investigated in the SUDs NLP pipeline. We will develop and test the two pipelines in a cohort of 200-500 patients and compare our pipeline-derived results to manual review outcome to measure the NLP model performance. We hypothesize that a high performance SDoH NLP pipeline will be developed that will fit to our application, and more SUD patients will be identified. The ultimate goal of our studies is to identify patients at risk of SUDs, identify risk factors associated with health outcomes, and improve patient health outcome prediction that will potentially help clinical decision support and healthcare management. In order to accomplish this, we will integrate the SDoH associated factors and SUD diagnosis that we obtained from the two NLP pipelines, along with other phenotype risk factors being extracted from EHR structured data. We hypothesize that the performance of the SUD predictive model will be increased after SDoH are included.