Patient and Care Team Perspectives on Social Determinants of Health Screening in Primary Care

Rudisill, A. Caroline; Eicken, Meredith G.A.; Gupta, Deeksha; Macauda, Mark; Self, Stella; Kennedy, Ann Blair; Thomas, Darin; Kao, Elise; Jeanty, Mia; Hartley, Jackson

doi:10.1001/jamanetworkopen.2023.45444

Cited by 4 publications

(1 citation statement)

References 25 publications

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“…33 Moreover, a recent study demonstrated that only 3.7% of the patients in a large health care system in South Carolina had answered all 11 questions on the SDoH screening forms. 34 Therefore, for better assessment of SBDH burden in a population, information must be aggregated from a variety of sources in the EHR, including the clinical notes.…”

Section: Discussionmentioning

confidence: 99%

Large-scale identification of social and behavioral determinants of health from clinical notes: Comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

Roy,

Morrell,

Zhao

et al. 2024

Preprint

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Background: Social and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR. Methods: Latent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using gpt-3.5-turbo-1106 and gpt-4-1106-preview models. Prediction of SBDH categories were performed using logistic regression model that included age, gender race and SBDH ICD-9 codes with a natural cubic spline of 2 degrees of freedom for age. Results: LSI retrieved patients according to 15 SBDH domains, with an overall average PPV ≥ 83%. Using manually curated gold standard (GS) sets for nine SBDH categories, the macro-F1 score of LSI (0.74) was better than ICD-9 (0.71) and GPT-3.5 (0.54), but lower than GPT-4 (0.80). Due to document size limitations, only a subset of the GS cases could be processed by GPT-3.5 (55.8%) and GPT-4 (94.2%), compared to LSI (100%). Using common GS subsets for nine different SBDH categories, the macro-F1 of ICD-9 combined with either LSI (mean 0.88, 95% CI 0.82-0.93), GPT-3.5 (0.86, 0.82-0.91) or GPT-4 (0.88, 0.83-0.94) was not significantly different. After including age, gender, race and ICD-9 in a logistic regression model, the AUC for prediction of six out of the nine SBDH categories was higher for LSI compared to GPT-4.0. Conclusions: These results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems.

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Section: Discussionmentioning

confidence: 99%

Large-scale identification of social and behavioral determinants of health from clinical notes: Comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

Roy,

Morrell,

Zhao

et al. 2024

Preprint

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Application of the Delphi method to the development of common data elements for social drivers of health: A systematic scoping review

Levites Strekalova,

Nelson,

Weber

et al. 2024

Translational Behavioral Medicine

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Collaborative data science requires standardized, harmonized, interoperable, and ethically sourced data. Developing an agreed-upon set of elements requires capturing different perspectives on the importance and feasibility of the data elements through a consensus development approach. This study reports on the systematic scoping review of literature that examined the inclusion of diverse stakeholder groups and sources of social drivers of health variables in consensus-based common data element (CDE) sets. This systematic scoping review included sources from PubMed, Embase, CINAHL, WoS MEDLINE, and PsycINFO databases. Extracted data included the stakeholder groups engaged in the Delphi process, sources of CDE sets, and inclusion of social drivers data across 11 individual and 6 social domains. Of the 384 studies matching the search string, 22 were included in the final review. All studies involved experts with healthcare expertise directly relevant to the developed CDE set, and only six (27%) studies engaged health consumers. Literature reviews and expert input were the most frequent sources of CDE sets. Seven studies (32%) did not report the inclusion of any demographic variables in the CDE sets, and each demographic SDoH domain was included in at least one study with age and sex assigned at birth included in all studies, and social driver domains included only in four studies (18%). The Delphi technique engages diverse expert groups around the development of SDoH data elements. Future studies can benefit by involving health consumers as experts.

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Information Technology in Screening and Identifying Unmet Social Needs: A Scoping Review

Sezgin,

Jackson,

Boch

et al. 2024

Preprint

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Background: Social determinants of health (SDOH) affect health outcomes, with unmet social needs like food insecurity, housing instability, and lack of services contributing to poor outcomes, health disparities, and higher healthcare costs. Identifying these needs is vital for improving health and achieving equity. Information technology has emerged as a tool for screening and identifying unmet social needs. Objectives: This scoping review examines how information technology is used to screen and identify unmet social needs among vulnerable populations and its impact on the assessment process. Methods: Following PRISMA-ScR guidelines, we searched databases including MEDLINE, Embase, Scopus, ACM Digital Library, and Web of Science for studies published from 2010 to 2023. Eligible studies used technology to screen for and identify unmet social needs in populations with health and socioeconomic challenges. Data extraction focused on the types of technology, screening processes, and social needs identified. Results: Our findings highlight a limited yet evolving landscape of technological applications. We identified 13 studies using tools like self-assessment surveys, tablet-based systems, and electronic portals. These tools were applied across diverse groups, such as refugees and patients in emergency departments. Innovative approaches, such as chatbots and multi-dimensional risk appraisal systems for older adults, showed potential. However, challenges included single-site studies, small samples, and integration issues with medical records. Effectiveness varied. The effectiveness of these tools in screening for unmet social needs shows mixed outcomes. Conclusions: Information technology plays a pivotal role in improving the identification of unmet social needs. The findings underscore the need for broader, more integrated research to fully understand the impact of technology-based assessments and screening processes for social needs. Future efforts should focus on facilitated screening using technology both within and outside of the visit, ensuring the linkage to appropriate resources and care.

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Patient and Care Team Perspectives on Social Determinants of Health Screening in Primary Care

Cited by 4 publications

References 25 publications

Large-scale identification of social and behavioral determinants of health from clinical notes: Comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

Large-scale identification of social and behavioral determinants of health from clinical notes: Comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

Application of the Delphi method to the development of common data elements for social drivers of health: A systematic scoping review

Information Technology in Screening and Identifying Unmet Social Needs: A Scoping Review

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