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• W4300773113 abstract "<sec> <title>BACKGROUND</title> Social determinants of health (SDoH), such as geographic neighborhoods, access to healthcare, education, and social structure are important factors affecting people’s health and health outcomes. SDoH of patients are scarcely documented in a discrete format in electronic health records (EHRs) but are often available in free-text clinical narratives such as physician notes. Innovative methods like natural language processing (NLP) are being developed to identify and extract SDoH from EHRs, but it is imperative that the input of key stakeholders is included as NLP systems are designed. </sec> <sec> <title>OBJECTIVE</title> Understand the feasibility, challenges, and benefits of developing an NLP system to uncover SDoH from clinical narratives by conducting interviews with key stakeholders: 1) clinicians, 2) data analysts, 3) citizen scientists and 4) patient navigators. </sec> <sec> <title>METHODS</title> Individuals who frequently work with SDoH data were invited to participate in in-depth, semi-structured interviews. All interviews were recorded and subsequently transcribed. After coding transcripts and developing a codebook, the constant comparative method was used to generate themes. </sec> <sec> <title>RESULTS</title> A total of 16 participants were interviewed (five data analysts, four patient navigators, four physicians, and three citizen scientists). Two themes emerged related to collecting SDoH: 1) the importance of SDoH data and 2) SDoH arises during patient-clinician communication. The challenges of collecting SDoH data included: 1) informal communication and 2) the need for expertise and knowledge about SDoH. Ways of improving how SDoH data can be incorporated into health services research and patient care were to 1) empower patients and 2) make the data actionable. </sec> <sec> <title>CONCLUSIONS</title> Extracting SDoH from EHRs was considered valuable and necessary, but obstacles such as narrative data format can make the process difficult. NLP can be a potential solution, but as the technology is developed, it is important to consider how key stakeholders document SDoH, apply the NLP systems, and use the extracted SDoH in health outcome studies.natural language processing, qualitative, social determinants of health, electronic health records </sec>" @default.
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• W4300773113 date "2022-09-28" @default.
• W4300773113 modified "2023-10-14" @default.
• W4300773113 title "Barriers and facilitators of obtaining SDoH of patients with cancer through the EHR using natural language processing technology: A qualitative study (Preprint)" @default.
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• W4300773113 doi "https://doi.org/10.2196/preprints.43059" @default.
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