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Cited by 13Electronic Health Records SystemsMedical Coding and Health InformationMachine Learning in Healthcare

Characterizing the relative frequency of clinician engagement with structured social determinants of health data

Open access
Iott, Bradley E, Adler-Milstein, Julia, Gottlieb, Laura M, Pantell, Matthew S·2023·Journal of the American Medical Informatics Association
DOI 10.1093/jamia/ocac251PMC PMC9933071
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Abstract

Objective: Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians’ engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations. Materials and Methods: We created measures of data engagement capturing instances of data documentation (data added/updated) or review (review of data that were previously documented) during a hospitalization. We applied these measures to four domains of EHR data, (medical, family, behavioral, and SDH) and explored associations between data engagement and hospital readmission risk. Results: SDH data engagement was associated with lower readmission risk. Yet, there were lower levels of SDH data engagement (8.37% of hospitalizations) than medical (12.48%), behavioral (17.77%), and family (14.42%) history data engagement. In hospitalizations where data were available from prior hospitalizations/outpatient encounters, a larger proportion of hospitalizations had SDH data engagement than other domains (72.60%). Discussion: The goal of SDH data collection is to drive interventions to reduce social risk. Data on when and how clinical teams engage with SDH data should be used to inform informatics initiatives to address health and healthcare disparities. Conclusion: Overall levels of SDH data engagement were lower than those of common medical, behavioral, and family history data, suggesting opportunities to enhance clinician SDH data engagement to support social services referrals and quality measurement efforts.

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Research works printed but not linked: 38

  1. Muhlestein D, Saunders RS, Richards R, McClellan MB. Recent progress in the value journey: growth of ACOs and value-based payment models in 2018. Health Affairs Blog 2018. https://www.healthaffairs.org/do/10. 1377/hblog20180810.481968/full/. Accessed November 28, 2021. · DOI
  2. Gottlieb L, Fichtenberg C, Alderwick H, Adler N. Social determinants of health: what's a healthcare system to do? J Healthc Manag 2019; 64 (4): 243-57.
  3. VanLare JM, Blum JD, Conway PH. Linking performance with payment: implementing the physician value-based payment modifier. JAMA 2012; 308 (20): 2089-90.
  4. Meddings J, Reichert H, Smith SN, et al. The impact of disability and social determinants of health on condition-specific readmissions beyond Medicare risk adjustments: a cohort study. J Gen Intern Med 2017; 32 (1): 71-80.
  5. Ash AS, Mick EO, Ellis RP, Kiefe CI, Allison JJ, Clark MA. Social deter- minants of health in managed care payment formulas. JAMA Intern Med 2017; 177 (10): 1424-30.
  6. Senteio C, Veinot T, Adler-Milstein J, Richardson C. Physicians' percep- tions of the impact of the EHR on the collection and retrieval of psychoso- cial information in outpatient diabetes care. Int J Med Inform 2018; 113: 9-16.
  7. National Academies of Sciences, Medicine. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation's Health. Washington, DC: The National Academies Press; 2020.
  8. Alderwick H, Gottlieb LM. Meanings and misunderstandings: a social determinants of health Lexicon for health care systems. Milbank Q 2019; 97: 407.
  9. Gottlieb LM, Tirozzi KJ, Manchanda R, Burns AR, Sandel MT. Moving electronic medical records upstream: incorporating social determinants of health. Am J Prev Med 2015; 48 (2): 215-8.
  10. Adler NE, Stead WW. Patients in context -EHR capture of social and behavioral determinants of health. N Engl J Med 2015; 372 (8): 698-701.
  11. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records; Board on Population Health and Public Health Practice; Institute of Medicine. Capturing Social and Behav- ioral Domains in Electronic Health Records: Phase 1. Washington, DC: National Academies Press; 2014. http://www.nationalacademies.org/ hmd/Reports/2014/Capturing-Social-and-Behavioral-Domains-in-Electronic- Health-Records-Phase-1.aspx. Accessed July 25, 2018.
  12. Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records, Board on Population Health and Public Health Practice, Institute of Medicine. Capturing Social and Behav- ioral Domains and Measures in Electronic Health Records: Phase 2. Washington, DC: National Academies Press; 2015. http://www.ncbi.nlm. nih.gov/books/NBK268995/. Accessed July 25, 2018.
  13. Hripcsak G, Forrest CB, Brennan PF, Stead WW. Informatics to support the IOM social and behavioral domains and measures. J Am Med Inform Assoc 2015; 22 (4): 921-4.
  14. Ahnquist J, Wamala SP, Lindstrom M. Social determinants of health -a question of social or economic capital? Interaction effects of socioeco- nomic factors on health outcomes. Soc Sci Med 2012; 74 (6): 930-9.
  15. Bernstein CN, Walld R, Marrie RA. Social determinants of outcomes in inflammatory bowel disease. Am J Gastroenterol 2020; 115 (12): 2036-46.
  16. Luong M-LN, Cleveland RJ, Nyrop KA, Callahan LF. Social determinants and osteoarthritis outcomes. Aging Health 2012; 8 (4): 413-37.
  17. Evans WN, Kroeger S, Munnich EL, Ortuzar G, Wagner Kl. Reducing readmissions by addressing the social determinants of health. Am J Health Econ 2021; 7 (1): 1-40.
  18. Zhang Y, Zhang Y, Sholle E, et al. Assessing the impact of social determi- nants of health on predictive models for potentially avoidable 30-day readmission or death. PLoS One 2020; 15 (6): e0235064.
  19. Lax Y, Martinez M, Brown NM. Social determinants of health and hospi- tal readmission. Pediatrics 2017; 140 (5): e20171427.
  20. Baker MC, Alberti PM, Tsao T-Y, Fluegge K, Howland RE, Haberman M. Social determinants matter for hospital readmission policy: insights from New York City. Health Aff 2021; 40 (4): 645-54.
  21. Friedman B, Basu J. The rate and cost of hospital readmissions for pre- ventable conditions. Med Care Res Rev 2004; 61 (2): 225-40.
  22. Burke RE, Coleman EA. Interventions to decrease hospital readmissions: keys for cost-effectiveness. JAMA Intern Med 2013; 173 (8): 695-8.
  23. Wang M, Pantell MS, Gottlieb LM, Adler-Milstein J. Documentation and review of social determinants of health data in the EHR: measures and associated insights. J Am Med Inform Assoc 2021; 28 (12): 2608-16.
  24. Veinot TC, Mitchell H, Ancker JS. Good intentions are not enough: how informatics interventions can worsen inequality. J Am Med Inform Assoc 2018; 25 (8): 1080-8.
  25. Garg A, Boynton-Jarrett R, Dworkin PH. Avoiding the unintended conse- quences of screening for social determinants of health. JAMA 2016; 316 (8): 813-4.
  26. Axelson DJ, Stull MJ, Coates WC. Social determinants of health: a missing link in emergency medicine training. AEM Educ Train 2018; 2 (1): 66-8.
  27. Gruß I, Bunce A, Davis J, et al. Initiating and implementing social determi- nants of health data collection in community health centers. Popul Health Manag 2021; 24 (1): 52-8.
  28. Herrera C-N, Brochier A, Pellicer M, Garg A, Drainoni M. Implementing social determinants of health screening at community health centers: clini- cian and staff perspectives. J Prim Care Community Health 2019; 10: 2150132719887260.
  29. Pooler JA, Hartline-Grafton H, DeBor M, Sudore RL, Seligman HK. Food insecurity: a key social determinant of health for older adults. J Am Ger- iatr Soc 2019; 67 (3): 421-4.
  30. Barr DA. Health Disparities in the United States: Social Class, Race, Eth- nicity, and the Social Determinants of Health. Baltimore, MD: JHU Press; 2019.
  31. Paro A, Hyer JM, Diaz A, et al. Profiles in social vulnerability: the associa- tion of social determinants of health with postoperative surgical out- comes. Surgery 2021; 170 (6): 1777-84.
  32. Yap ZL, Summers SJ, Grant AR, Moseley GL, Karran EL. The role of the social determinants of health in outcomes of surgery for low back pain: a systematic review and narrative synthesis. Spine J 2022; 22 (5): 793-809.
  33. Bonner SN, Wakeam E. The volume-outcome relationship in lung cancer surgery: the impact of the social determinants of health care delivery. J Thorac Cardiovasc Surg 2022; 163 (6): 1933-7.
  34. Fox M. Social determinants of health and surgery: an overview. Bull Am Coll Surg 2021; 106: 12-21. https://bulletin.facs.org/2021/05/social- determinants-of-health-and-surgery-an-overview/. Accessed July 25, 2022.
  35. Bradywood A, Leming-Lee TS, Watters R, Blackmore C. Implementing screening for social determinants of health using the Core 5 screening tool. BMJ Open Qual 2021; 10 (3): e001362.
  36. Cartier Y, Fichtenberg C, Gottlieb LM. Implementing community resource referral technology: facilitators and barriers described by early adopters. Health Aff 2020; 39 (4): 662-9.
  37. Nuzum R, Lewis C, Chang DI. Measuring what matters: social drivers of health. Commonw Fund 2021; doi:10.26099/q0pa-xf79 · DOI · Paper page
  38. Navathe AS, Zhong F, Lei VJ, et al. Hospital readmission and social risk factors identified from physician notes. Health Serv Res 2018; 53 (2): 1110-36.

Cited by

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Authors
Iott, B. E., Adler-Milstein, J., Gottlieb, L. M., & Pantell, M. S.
Affiliations
  • University of California, San Francisco (US)
  • Center for Clinical Informatics and Improvement Research, University of California, San Francisco (UCSF) , San Francisco, California, USA
Corresponding author
Iott, Bradley E
ORCID
Venue
Journal of the American Medical Informatics Association
Publisher
Oxford University Press (OUP)
Published
2022-12-21
Volume
30
Issue
3
Pages
503-510
ISSN
  • 1067-5027 (ISSN-L)
  • 1527-974X
DOI
10.1093/jamia/ocac251
PMC
PMC9933071
OpenAlex
W4312094473
Cited by
13
References
2
Citations by year
2026: 3 · 2025: 4 · 2024: 4 · 2023: 2
FWCI
1.87
Type
journal article
Language
en
Open access
Licence
academic.oup.com/pages/standard-publication-reuse-rights (vor)
Locations
  • Journal of the American Medical Informatics Association (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • Agency for Healthcare Research and Quality (K08HS028473)
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (L40 HD106442)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Keywords
social determinants of health, clinician engagement, electronic health records, data documentation, hospital readmission, hospitalization, social risk, health disparities, social services referral, quality measurement
MeSH terms
Humans, Motivation, Patient Acceptance of Health Care, Patient Readmission, Electronic Health Records, Social Determinants of Health

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