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Cited by 9Electronic Health Records SystemsArtificial Intelligence in Healthcare and EducationMedical Coding and Health Information

Development of a social and environmental determinants of health informatics maturity model

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Espinoza, Juan C., Sehgal, Shruti, Phuong, Jimmy, Bahroos, Neil, Starren, Justin, Wilcox, Adam, Meeker, Daniella·2023·Journal of Clinical and Translational Science
DOI 10.1017/cts.2023.691PMC PMC10877515
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Abstract

Introduction: Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps. Methods: We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration’s Informatics Enterprise Committee, and a publicly available online self-assessment tool. Results: We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels. Conclusion: The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.

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  • The Social and Environmental Determinants of Health Informatics Maturity Model guides organizations in assessing and improving their readiness to integrate SDOH into care delivery.

    “Innovative frameworks like the Social and Environmental Determinants of Health Informatics Maturity Model guide organizations in assessing and improving their readiness to integrate SDOH into care delivery [69].”

    From Then, Now, Next: Unpacking the Shifting Trajectory of Social Determinants of Health (2025) · cites this paper as [69]

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  1. [2]Stringhini S, Carmeli C, Jokela M, et al. Socioeconomic status and the 25 × 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1·7 million men and women. Lancet. 2017; 389(10075): 1229– 1237. doi: 10.1016/S0140-6736(16)32380-7. 28159391 PMC5368415 · DOI · Paper page
  2. [3]Hood CM, Gennuso KP, Swain GR, Catlin BB. County health rankings: relationships between determinant factors and health outcomes. Am J Prev Med. 2016; 50(2): 129– 135. doi: 10.1016/j.amepre.2015.08.024. 26526164 · DOI · Paper page
  3. [5]Peretz PJ, Islam N, Matiz LA. Community health workers and covid-19 - addressing social determinants of health in times of crisis and beyond. N Engl J Med. 2020; 383(19): e108. doi: 10.1056/NEJMp2022641. 32966715 · DOI · Paper page
  4. [6]Rollston R, Galea S. COVID-19 and the social determinants of health. Am J Health Promot. 2020; 34(6): 687– 689. doi: 10.1177/0890117120930536b. 32551932 · DOI
  5. [7]Shah GH, Shankar P, Schwind JS, Sittaramane V. The detrimental impact of the COVID-19 crisis on health equity and social determinants of health. J Public Health Manag Pract. 2020; 26(4): 317– 319. doi: 10.1097/PHH.0000000000001200. 32433385 · DOI · Paper page
  6. [8]Ramírez IJ, Lee J. COVID-19 emergence and social and health determinants in Colorado: a rapid spatial analysis. Int J Environ Res Public Health. 2020; 17(11): 3856. doi: 10.3390/ijerph17113856 Published 2020 May 29. 32485854 PMC7312929 · DOI
  7. [9]Phuong J, Zampino E, Dobbins N, et al. Extracting patient-level social determinants of health into the OMOP common data model. AMIA Annu Symp Proc. 2022; 2021: 1989– 1998. PMC8861735 35308947
  8. [10]Kingsbury P, Abajian H, Abajian M, et al. SEnDAE: a resource for expanding research into social and environmental determinants of health. Comput Methods Programs Biomed. 2023; 238: 107542. doi: 10.1016/j.cmpb.2023.107542. 37224727 PMC12579821 · DOI
  9. [11]Chen M, Tan X, Padman R. Social determinants of health in electronic health records and their impact on analysis and risk prediction: a systematic review. J Am Med Inform Assoc. 2020; 27(11): 1764– 1773. doi: 10.1093/jamia/ocaa143. 33202021 PMC7671639 · DOI · Paper page
  10. [12]Carvalho JV, Rocha Á., Abreu A. Maturity models of healthcare information systems and technologies: a literature review. J Med Syst. 2016; 40(6): 131. doi: 10.1007/s10916-016-0486-5. 27083575 · DOI
  11. [16]Knosp BM, Barnett WK, Anderson NR, Embi PJ. Research IT maturity models for academic health centers: early development and initial evaluation. J Clin Transl Sci. 2018; 3(5): 289– 294. doi: 10.1017/cts.2018.339. PMC6390403 30828469 · DOI
  12. [17]Knosp BM, Dorr DA, Campion TR. Maturity in enterprise data warehouses for research operations: analysis of a pilot study. J Clin Transl Sci. 2023; 7(1): e70. doi: 10.1017/cts.2023.23. 37008621 PMC10052397 · DOI
  13. [18]Champieux R, Solomonides A, Conte M, et al. Ten simple rules for organizations to support research data sharing. PLoS Comput Biol. 2023; 19(6): e1011136. doi: 10.1371/journal.pcbi.1011136. 37319166 PMC10270328 · DOI
  14. [19]van de Wetering R, Batenburg R. A PACS maturity model: a systematic meta-analytic review on maturation and evolvability of PACS in the hospital enterprise. Int J Med Inform. 2009; 78(2): 127– 140. doi: 10.1016/j.ijmedinf.2008.06.010. 18752988 · DOI
  15. [22]Phuong J, Hong S, Palchuk MB, et al. Advancing interoperability of patient-level social determinants of health data to support COVID-19 research. AMIA Jt Summits Transl Sci Proc. 2022; 2022: 396– 405. 35854720 PMC9285174
  16. [23]Madlock-Brown C, Wilkens K, Weiskopf N, et al. Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses. BMC Public Health. 2022; 22(1): 747. doi: 10.1186/s12889-022-13168-y. 35421958 PMC9008430 · DOI
  17. [24]Phuong J, Riches NO, Madlock-Brown C, et al. Social determinants of health factors for gene-environment COVID-19 research: challenges and opportunities. Adv Genet (Hoboken). 2022; 3(2): 2100056. doi: 10.1002/ggn2.202100056. 35574521 PMC9087427 · DOI
  18. [26]Feldstein AC, Glasgow RE. A practical, robust implementation and sustainability model (PRISM) for integrating research findings into practice. Jt Comm J Qual Patient Saf. 2008; 34(4): 228– 243. doi: 10.1016/s1553-7250(08)34030-6. 18468362 · DOI · Paper page
  19. [29]Fuchs VR. Social determinants of health: caveats and nuances. JAMA. 2017; 317(1): 25– 26. doi: 10.1001/jama.2016.17335. 28030707 · DOI
  20. [30]Siegel B, Nolan L. Leveling the field--ensuring equity through national health care reform. N Engl J Med. 2009; 361(25): 2401– 2403. doi: 10.1056/NEJMp0909323. 19955518 · DOI
  21. [31]Bazemore AW, Cottrell EK, Gold R, et al. Community vital signs’: incorporating geocoded social determinants into electronic records to promote patient and population health. J Am Med Inform Assoc. 2016; 23(2): 407– 412. doi: 10.1093/jamia/ocv088. 26174867 PMC11740537 · DOI
  22. [32]Alley DE, Asomugha CN, Conway PH, Sanghavi DM. Accountable health communities—addressing social needs through medicare and medicaid. N Engl J Med. 2016; 374(1): 8– 11. 26731305
  23. [35]Singh GK, Siahpush M, Kogan MD. Neighborhood socioeconomic conditions, built environments, and childhood obesity. Health Aff (Millwood). 2010; 29(3): 503– 512. doi: 10.1377/hlthaff.2009.0730. 20194993 · DOI
  24. [37]Singh GK, Daus GP, Allender M, et al. Social determinants of health in the united states: addressing major health inequality trends for the nation, 1935-2016. Int J MCH AIDS. 2017; 6(2): 139– 164. doi: 10.21106/ijma.236. 29367890 PMC5777389 · DOI
  25. [38]Blizinsky KD, Bonham VL. Leveraging the learning health care model to improve equity in the age of genomic medicine. Learn Health Syst. 2018; 2(1): e10046. doi: 10.1002/lrh2.10046. 29457138 PMC5813818 · DOI
  26. [39]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 Behavioral Domains and Measures in Electronic Health Records: Phase 2. Washington (DC): National Academies Press (US); 2015, https://www.ncbi.nlm.nih.gov/books/NBK268995/ doi: 10.17226/18951. 25590118 · DOI
  27. [40]Hager K, De Kesel Lofthus A, Balan B, Cutts D. Electronic medical record-based referrals to community nutritional assistance for food-insecure patients. Ann Fam Med. 2020; 18(3): 278– 278. doi: 10.1370/afm.2530. 32393567 PMC7213993 · DOI
  28. [41]Woods L, Dendere R, Eden R, et al. Perceived impact of digital health maturity on patient experience, population health, health care costs, and provider experience: mixed methods case study. J Med Internet Res. 2023; 25: e45868. doi: 10.2196/45868. 37463008 PMC10394505 · DOI
  29. [42]Garg A, Boynton-Jarrett R, Dworkin PH. Avoiding the unintended consequences of screening for social determinants of health. JAMA. 2016; 316(8): 813– 814. doi: 10.1001/jama.2016.9282. 27367226 · DOI
  30. [43]Camacho-Rivera Marlene, Jessica Y, et al. Social determinants of health during the COVID-19 pandemic in the US: precision through context. In: Hsueh PYS, Wetter T, Zhu X, eds. Personal Health Informatics. Cognitive Informatics in Biomedicine and Healthcare. Cham: Springer; 2022: 397– 425.
  31. [44]Dorr D, Bejan CA, Pizzimenti C, Singh S, Storer M, Quinones A. Identifying patients with significant problems related to social determinants of health with natural language processing. Stud Health Technol Inform. 2019; 264: 1456– 1457. doi: 10.3233/SHTI190482. 31438179 · DOI
  32. [45]Hatef E, Rouhizadeh M, Tia I, et al. Assessing the availability of data on social and behavioral determinants in structured and unstructured electronic health records: a retrospective analysis of a multilevel health care system. JMIR Med Inform. 2019; 7(3): e13802. doi: 10.2196/13802. 31376277 PMC6696855 · DOI · Paper page
  33. [46]Patra BG, Sharma MM, Vekaria V, et al. Extracting social determinants of health from electronic health records using natural language processing: a systematic review. J Am Med Inform Assoc. 2021; 28(12): 2716– 2727. doi: 10.1093/jamia/ocab170. 34613399 PMC8633615 · DOI · Paper page
  34. [47]Lybarger K, Ostendorf M, Yetisgen M. Annotating social determinants of health using active learning, and characterizing determinants using neural event extraction. J Biomed Inform. 2021; 113: 103631. doi: 10.1016/j.jbi.2020.103631. 33290878 PMC7856628 · DOI
  35. [48]Chapman AB, Jones A, Kelley AT, et al. ReHouSED: a novel measurement of veteran housing stability using natural language processing. J Biomed Inform. 2021; 122: 103903. doi: 10.1016/j.jbi.2021.103903. 34474188 PMC8608249 · DOI
  36. [49]Lybarger K, Yetisgen M, Uzuner Ö. The 2022 n2c2/UW shared task on extracting social determinants of health. J Am Med Inform Assoc. 2023; 30(8): 1367– 1378. doi: 10.1093/jamia/ocad012. 36795066 PMC10354764 · DOI · Paper page
  37. [50]Han S, Zhang RF, Shi L, et al. Classifying social determinants of health from unstructured electronic health records using deep learning-based natural language processing. J Biomed Inform. 2022; 127: 103984. doi: 10.1016/j.jbi.2021.103984. 35007754 · DOI · Paper page
  38. [51]Uzuner O, Goldstein I, Luo Y, Kohane I. Identifying patient smoking status from medical discharge records. J Am Med Inform Assoc. 2008; 15(1): 14– 24. doi: 10.1197/jamia.M2408. 17947624 PMC2274873 · DOI
  39. [52]Ulmer C, McFadden B, Nerenz DR. Institute of medicine (US) subcommittee on standardized collection of race/ethnicity data for healthcare quality improvement. Race, Ethnicity, and Language Data: Standardization for Health Care Quality Improvement. Washington (DC): National Academies Press (US); 2009. 25032349
  40. [53]Vega Perez RD, Hayden L, Mesa J, et al. Improving patient race and ethnicity data capture to address health disparities: a case study from a large urban health system. Cureus. 2022; 14(1): e20973. doi: 10.7759/cureus.20973. 35154951 PMC8815799 · DOI

Cited by

Sorted by citations

Authors
Espinoza, J. C., Sehgal, S., Phuong, J., Bahroos, N., Starren, J., Wilcox, A., & Meeker, D.
Affiliations
  • Northwestern University (US)
  • University of Washington (US)
  • University of Southern California (US)
  • Washington University in St. Louis (US)
  • Yale University (US)
Corresponding author
Espinoza, Juan C.
ORCID
Venue
Journal of Clinical and Translational Science
Publisher
Cambridge University Press (CUP)
Published
2023
Volume
7
Issue
1
Pages
e266-e266
ISSN
2059-8661 (ISSN-L)
DOI
10.1017/cts.2023.691
PMC
PMC10877515
OpenAlex
W4389451556
Cited by
9
References
2
Citations by year
2026: 1 · 2025: 8
FWCI
2.02
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Clinical and Translational Science (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Southern California Clinical and Translational Science Institute (UL1-TR-001855)
  • Institute of Translational Health Sciences (UL1TR002319)
  • Georgia Clinical and Translational Science Alliance (UL1TR002319, UL1TR001422)
  • University of Cambridge
  • National Institutes of Health (U24TR002306, UL1TR001422, UL1TR001855, UL1TR002319)
  • National Center for Advancing Translational Sciences
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
Keywords
health equity, clinical data collection, health data analytics, health information technology, academic medical centers, N3C, self-assessment tool, health disparities

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