Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content Analysis
Introduction: Social determinants of health (SDoH) are known to impact the health and well-being of patients. However, information regarding them is not always collected in healthcare interactions, and healthcare professionals are not always well-trained or equipped to address them. Emergency medica...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eScholarship Publishing, University of California
2023-08-01
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| Series: | Western Journal of Emergency Medicine |
| Online Access: | https://escholarship.org/uc/item/10n524nx |
| _version_ | 1797660240959766528 |
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| author | Susan J. Burnett Rachel Stemerman Johanna C. Innes Maria C. Kaisler Remle P. Crowe Brian M. Clemency |
| author_facet | Susan J. Burnett Rachel Stemerman Johanna C. Innes Maria C. Kaisler Remle P. Crowe Brian M. Clemency |
| author_sort | Susan J. Burnett |
| collection | DOAJ |
| description | Introduction: Social determinants of health (SDoH) are known to impact the health and well-being of patients. However, information regarding them is not always collected in healthcare interactions, and healthcare professionals are not always well-trained or equipped to address them. Emergency medical services (EMS) professionals are uniquely positioned to observe and attend to SDoH because of their presence in patients’ environments; however, the transmission of that information may be lost during transitions of care. Documentation of SDoH in EMS records may be helpful in identifying and addressing patients’ insecurities and improving their health outcomes. Our objective in this study was to determine the presence of SDoH information in adult EMS records and understand how such information is referenced, appraised, and linked to other determinants by EMS personnel. Methods: Using EMS records for adult patients in the 2019 ESO Data Collaborative public-use research dataset using a natural language processing (NLP) algorithm, we identified free-text narratives containing documentation of at least one SDoH from categories associated with food, housing, employment, insurance, financial, and social support insecurities. From the NLP corpus, we randomly selected 100 records from each of the SDoH categories for qualitative content analysis using grounded theory. Results: Of the 5,665,229 records analyzed by the NLP algorithm, 175,378 (3.1%) were identified as containing at least one reference to SDoH. References to those SDoH were centered around the social topics of accessibility, mental health, physical health, and substance use. There were infrequent explicit references to other SDoH in the EMS records, but some relationships between categories could be inferred from contexts. Appraisals of patients’ employment, food, and housing insecurities were mostly negative. Narratives including social support and financial insecurities were less negatively appraised, while those regarding insurance insecurities were mostly neutral and related to EMS operations and procedures. Conclusion: The social determinants of health are infrequently documented in EMS records. When they are included, they are infrequently explicitly linked to other SDoH categories and are often negatively appraised by EMS professionals. Given their unique position to observe and share patients’ SDoH information, EMS professionals should be trained to understand, document, and address SDoH in their practice. |
| first_indexed | 2024-03-11T18:26:56Z |
| format | Article |
| id | doaj.art-684f7fcc3bd0487a93447e6c2710103d |
| institution | Directory Open Access Journal |
| issn | 1936-900X 1936-9018 |
| language | English |
| last_indexed | 2024-03-11T18:26:56Z |
| publishDate | 2023-08-01 |
| publisher | eScholarship Publishing, University of California |
| record_format | Article |
| series | Western Journal of Emergency Medicine |
| spelling | doaj.art-684f7fcc3bd0487a93447e6c2710103d2023-10-13T16:02:12ZengeScholarship Publishing, University of CaliforniaWestern Journal of Emergency Medicine1936-900X1936-90182023-08-0124587888710.5811/westjem.5907059070Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content AnalysisSusan J. Burnett0Rachel Stemerman1Johanna C. Innes2Maria C. Kaisler3Remle P. Crowe4Brian M. Clemency5Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Department of Emergency Medicine, Buffalo, New YorkESO, Austin, TexasJacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Department of Emergency Medicine, Buffalo, New YorkJacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Department of Emergency Medicine, Buffalo, New YorkESO, Austin, TexasJacobs School of Medicine and Biomedical Sciences, University at Buffalo, The State University of New York, Department of Emergency Medicine, Buffalo, New YorkIntroduction: Social determinants of health (SDoH) are known to impact the health and well-being of patients. However, information regarding them is not always collected in healthcare interactions, and healthcare professionals are not always well-trained or equipped to address them. Emergency medical services (EMS) professionals are uniquely positioned to observe and attend to SDoH because of their presence in patients’ environments; however, the transmission of that information may be lost during transitions of care. Documentation of SDoH in EMS records may be helpful in identifying and addressing patients’ insecurities and improving their health outcomes. Our objective in this study was to determine the presence of SDoH information in adult EMS records and understand how such information is referenced, appraised, and linked to other determinants by EMS personnel. Methods: Using EMS records for adult patients in the 2019 ESO Data Collaborative public-use research dataset using a natural language processing (NLP) algorithm, we identified free-text narratives containing documentation of at least one SDoH from categories associated with food, housing, employment, insurance, financial, and social support insecurities. From the NLP corpus, we randomly selected 100 records from each of the SDoH categories for qualitative content analysis using grounded theory. Results: Of the 5,665,229 records analyzed by the NLP algorithm, 175,378 (3.1%) were identified as containing at least one reference to SDoH. References to those SDoH were centered around the social topics of accessibility, mental health, physical health, and substance use. There were infrequent explicit references to other SDoH in the EMS records, but some relationships between categories could be inferred from contexts. Appraisals of patients’ employment, food, and housing insecurities were mostly negative. Narratives including social support and financial insecurities were less negatively appraised, while those regarding insurance insecurities were mostly neutral and related to EMS operations and procedures. Conclusion: The social determinants of health are infrequently documented in EMS records. When they are included, they are infrequently explicitly linked to other SDoH categories and are often negatively appraised by EMS professionals. Given their unique position to observe and share patients’ SDoH information, EMS professionals should be trained to understand, document, and address SDoH in their practice.https://escholarship.org/uc/item/10n524nx |
| spellingShingle | Susan J. Burnett Rachel Stemerman Johanna C. Innes Maria C. Kaisler Remle P. Crowe Brian M. Clemency Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content Analysis Western Journal of Emergency Medicine |
| title | Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content Analysis |
| title_full | Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content Analysis |
| title_fullStr | Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content Analysis |
| title_full_unstemmed | Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content Analysis |
| title_short | Social Determinants of Health in EMS Records: A Mixed-methods Analysis Using Natural Language Processing and Qualitative Content Analysis |
| title_sort | social determinants of health in ems records a mixed methods analysis using natural language processing and qualitative content analysis |
| url | https://escholarship.org/uc/item/10n524nx |
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