Modeling Patients' Progression through Health-Related Social Needs

Abstract

Objective

This study sought to characterize how a population experienced health-related social needs (HRSNs) over time.

Methods

We employed hidden Markov modeling using data extracted from a natural language processing state machine from 2018 to 2020 to examine whether a patient experienced any food, legal, transportation, employment, financial, or housing needs. Characteristics of patients transitioning into low/high-risk states were compared. We also identified the frequency at which patients transitioned according to their risk state.

Results

Our results identified that five hidden states best represented how patients are experiencing HRSNs longitudinally. Of 48,055 patients, 80% were categorized in states 1 and 2, labeled as low risk. Nine percent, 8%, and 3% of the study population were labeled as medium, high, and very high risk, respectively. Results also showed that low and high-risk patients (states 1, 2, and 5) only transition states once every year and a half, while patients in medium and high-risk states transition approximately once per year.

Conclusion

Low and very high-risk patients tend to remain in the same state over time, suggesting that low-risk patients may have the means to maintain a healthy state while very high-risk patients have a difficult time resolving multiple HRSNs. Early screening and immediate interventions may be beneficial in mitigating the persistent harm of unaddressed HRSNs.

Keywords

social factors - health-related social needs - social determinants of health - hidden Markov model - natural language processing - electronic health records

Protection of Human and Animal Subjects

No human or animals subjects were involved in this study. The Indiana University Institutional Review Board approved this study.

Authors' Contributions

H.K.: Software, validation, analysis, methodology, writing—original draft, visualization.

O.B-A.: Conceptualization, software, methodology, analysis, writing—review/editing, visualization, supervision.

J.V.: Conceptualization, methodology, writing—review/editing, supervision, data curation, funding acquisition.

Publication History

Received: 16 December 2024

Accepted: 06 May 2025

Article published online:
19 September 2025

• References

• 1 Schiavoni KH, Helscel K, Vogeli C. et al. Prevalence of social risk factors and social needs in a Medicaid Accountable Care Organization (ACO). BMC Health Serv Res 2022; 22 (01) 1375
• 2 Engelberg Anderson JK, Jain P, Wade AJ, Morris AM, Slaboda JC, Norman GJ. Indicators of potential health-related social needs and the association with perceived health and well-being outcomes among community-dwelling Medicare beneficiaries. Qual Life Res 2020; 29 (06) 1685-1696
• 3 Kreuter MW, Thompson T, McQueen A, Garg R. Addressing social needs in health care settings: Evidence, challenges, and opportunities for public health. Annu Rev Public Health 2021; 42: 329-344
• 4 Canterberry M, Figueroa JF, Long CL. et al. Association between self-reported health-related social needs and acute care utilization among older adults enrolled in Medicare advantage. JAMA Health Forum 2022; 3 (07) e221874
• 5 National Academies of Sciences, Medicine, and Engineering. Integrating Social Care into the Delivery of Health Care. Moving Upstream to Improve the Nation's Health. The National Academies Press; 2019. Doi: 10.17226/25467
• 6 Pruitt Z, Emechebe N, Quast T, Taylor P, Bryant K. Expenditure reductions associated with a social service referral program. Popul Health Manag 2018; 21 (06) 469-476
• 7 Kim EJ, Abrahams S, Uwemedimo O, Conigliaro J. Prevalence of social determinants of health and associations of social needs among United States adults, 2011-2014. J Gen Intern Med 2020; 35 (05) 1608-1609
• 8 Molina MF, Li CN, Manchanda EC. et al. Prevalence of emergency department social risk and social needs. West J Emerg Med 2020; 21 (06) 152-161
• 9 Jih J, Nguyen A, Cenzer I, Morrish J. Burden of unmet health-related social needs in an academic adult primary care practice in San Francisco, California. BMC Prim Care 2023; 24 (01) 166
• 10 Haff N, Choudhry NK, Bhatkhande G. et al. Frequency of quarterly self-reported health-related social needs among older adults, 2020. JAMA Netw Open 2022; 5 (06) e2219645
• 11 Domestic Policy Council, Office of Science and Technology Policy. The U.S. playbook to address social determinants of health. November 2023
• 12 CMS. CMS Measures Inventory Tool.
• 13 HHS, CMS. 2023 Collection Type: MIPS Clinical Quality Measures (CQMS) MEASURE TYPE: Process-High Priority Quality ID #487: Screening for Social Drivers of Health. 2023
• 14 Reynolds A. . Social Need: New HEDIS Measure Uses Electronic Data to Look at Screening, Intervention. NCQA; 2022.
• 15 The Joint Commission. R3 Report. 2022. Accessed August 22, 2024 at: https://www.jointcommission.org/-/media/tjc/documents/standards/r3-reports/r3_disparities_july2022-6-20-2022.pdf
• 16 Cartier Y, Gottlieb L. The prevalence of social care in US health care settings depends on how and whom you ask. BMC Health Serv Res 2020; 20 (01) 481
• 17 Boch S, Keedy H, Chavez L, Dolce M, Chisolm D. An integrative review of social determinants of health screenings used in primary care settings. J Health Care Poor Underserved 2020; 31 (02) 603-622
• 18 Shaheen A, Squire MA, Gay H, Rana SY, Gwynne M, Gupta SK. System approaches to social determinants of health screening and intervention. NEJM Catal 2023; 4 (04)
• 19 Trochez RJ, Sharma S, Stolldorf DP. et al. Screening health-related social needs in hospitals: A systematic review of health care professional and patient perspectives. Popul Health Manag 2023; 26 (03) 157-167
• 20 Morrissey TW, Cha Y, Wolf S, Khan M. Household economic instability: Constructs, measurement, and implications. Child Youth Serv Rev 2020; 118: 105502
• 21 Feller DJ, Zucker J, Walk OBD, Yin MT, Gordon P, Elhadad N. Longitudinal analysis of social and behavioral determinants of health in the EHR: exploring the impact of patient trajectories and documentation practices. AMIA Annu Symp Proc 2020; 2019: 399-407
• 22 Xu S, Goodrich GK, Moore KR. et al. Identifying relative changes in social risk factors: An analytic approach. Med Care 2021; 59 (02) e9-e15
• 23 Kang S. Severe and persistent housing instability: examining low-income households' residential mobility trajectories in the United States. Housing Stud 2023; 38 (09) 1615-1641
• 24 Martinez R, Alper J. . Front Matter. Investing in Interventions That Address Non-Medical, Health-Related Social Needs. National Academies Press; 2019
• 25 Gultekin L, Brush BL. In their own words: Exploring family pathways to housing instability. J Fam Nurs 2017; 23 (01) 90-115
• 26 Holcomb J, Highfield L, Ferguson GM, Morgan RO. Association of social needs and healthcare utilization among Medicare and Medicaid beneficiaries in the accountable health communities model. J Gen Intern Med 2022; 37 (14) 3692-3699
• 27 Kaplan D. Latent Class Models. In: The Sage Handbook of Quantitative Methodology for the Social Sciences. SAGE Publications; 2004
• 28 McDonald CJ, Overhage JM, Barnes M. et al.; INPC Management Committee. The Indiana network for patient care: a working local health information infrastructure. An example of a working infrastructure collaboration that links data from five health systems and hundreds of millions of entries. Health Aff (Millwood) 2005; 24 (05) 1214-1220
• 29 U.S. Census Bureau. U.S. Department of Commerce. Published online 2020. Accessed May 5, 2025 at: data.census.gov
• 30 Consumer Financial Protection Bureau. CFPB Financial Well-Being Scale Scale Development Technical Report. 2017.
• 31 USDA. Food security status of US households in 2019. 2019
• 32 Burgard SA, Seefeldt KS, Zelner S. Housing instability and health: findings from the Michigan Recession and Recovery Study. Soc Sci Med 2012; 75 (12) 2215-2224
• 33 Vest JR, Hinrichs RJ, Hosler H. How legal problems are conceptualized and measured in healthcare settings: a systematic review. Health Justice 2023; 11 (01) 48
• 34 Dooley D. Unemployment, underemployment, and mental health: conceptualizing employment status as a continuum. Am J Community Psychol 2003; 32 (1-2): 9-20
• 35 Syed ST, Gerber BS, Sharp LK. Traveling towards disease: transportation barriers to health care access. J Community Health 2013; 38 (05) 976-993
• 36 Allen KS, Hood DR, Cummins J, Kasturi S, Mendonca EA, Vest JR. Natural language processing-driven state machines to extract social factors from unstructured clinical documentation. JAMIA Open 2023; 6 (02) ooad024
• 37 Magoc T, Allen KS, McDonnell C. et al. Generalizability and portability of natural language processing system to extract individual social risk factors. Int J Med Inform 2023; 177: 105115
• 38 Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med Care 1998; 36 (01) 8-27
• 39 Degirmenci A. Introduction to Hidden Markov Models. 2014
• 40 Akaike H. A new look at the statistical model identification. IEEE Trans Automat Contr 1974; 19 (06) 716-723
• 41 Schwarz G. Estimating the dimension of a model. Ann Stat 1978; 6 (02) 461-464
• 42 Visser I. Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series. J Math Psychol 2011; 55 (06) 403-415
• 43 Zucchini W. An introduction to model selection. J Math Psychol 2000; 44 (01) 41-61
• 44 Giudici P, Rydén T, Vandekerkhove P. Likelihood-ratio tests for hidden Markov models. Biometrics 2000; 56 (03) 742-747
• 45 Visser I, Raijmakers MEJ, Molenaar PCM. Fitting hidden Markov models to psychological data. Sci Program 2002; 10 (03) 185-199
• 46 Ben-Assuli O, Jacobi A, Goldman O. et al. Stratifying individuals into non-alcoholic fatty liver disease risk levels using time series machine learning models. J Biomed Inform 2022; 126: 103986
• 47 Ben-Assuli O, Heart T, Vest JR, Ramon-Gonen R, Shlomo N, Klempfner R. Profiling readmissions using hidden Markov model - the case of congestive heart failure. Inf Syst Manage 2021; 38 (03) 237-249
• 48 Fisher R, Yates F. Statistical Methods for Research Workers. 1925.
• 49 Visser I, Speekenbrink M. DepmixS4: An R Package for Hidden Markov Models. 2010;Vol. 36. Accessed May 5, 2025 at: http://www.jstatsoft.org/
• 50 WHO. A Conceptual Framework for Action on the Social Determinants of Health. 2010
• 51 CMS. Accountable Health Communities (AHC) Model. Evaluation of 2018–2023. 2023
• 52 Burgermaster M, Rodriguez VA. Psychosocial-behavioral phenotyping: A novel precision health approach to modeling behavioral, psychological, and social determinants of health using machine learning. Ann Behav Med 2022; 56 (12) 1258-1271
• 53 Hewner S, Smith E, Sullivan SS. Identifying high-need primary care patients using nursing knowledge and machine learning methods. Appl Clin Inform 2023; 14 (03) 408-417
• 54 Jiang Y, Huang YL, Watral A, Blocker RC, Rushlow DR. Predicting provider workload using predicted patient risk score and social determinants of health in primary care setting. Appl Clin Inform 2024; 15 (03) 511-527
• 55 Mazurenko O, Hirsh AT, Harle CA, Shen J, McNamee C, Vest JR. Comparing the performance of screening surveys versus predictive models in identifying patients in need of health-related social need services in the emergency department. PLoS ONE 2024; 19 (11) e0312193
• 56 Sahoo SS, Plasek JM, Xu H. et al. Large language models for biomedicine: foundations, opportunities, challenges, and best practices. J Am Med Inform Assoc 2024; 31 (09) 2114-2124
• 57 Sun S, Zack T, Williams CYK, Sushil M, Butte AJ. Topic modeling on clinical social work notes for exploring social determinants of health factors. JAMIA Open 2024; 7 (01) ooad112
• 58 Wu C, Lin W, Zhang X, Zhang Y, Xie W, Wang Y. PMC-LLaMA: Toward building open-source language models for medicine. J Am Med Inform Assoc 2024; 31 (09) 1833-1843
• 59 Yu Z, Peng C, Yang X. et al. Identifying social determinants of health from clinical narratives: A study of performance, documentation ratio, and potential bias. J Biomed Inform 2024; 153: 104642
• 60 Feller DJ, Bear Don't Walk Iv OJ, Zucker J, Yin MT, Gordon P, Elhadad N. Detecting social and behavioral determinants of health with structured and free-text clinical data. Appl Clin Inform 2020; 11 (01) 172-181