Predicting Health‐Related Social Needs in Medicaid and Medicare Populations Using Machine Learning
Open accessAbstract
Providers currently rely on universal screening to identify health-related social needs (HRSNs). Predicting HRSNs using EHR and community-level data could be more efficient and less resource intensive. Using machine learning models, we evaluated the predictive performance of HRSN status from EHR and community-level social determinants of health (SDOH) data for Medicare and Medicaid beneficiaries participating in the Accountable Health Communities Model. We hypothesized that Medicaid insurance coverage would predict HRSN status. All models significantly outperformed the baseline Medicaid hypothesis. AUCs ranged from 0.59 to 0.68. The top performance (AUC = 0.68 CI 0.66–0.70) was achieved by the “any HRSNs” outcome, which is the most useful for screening prioritization. Community-level SDOH features had lower predictive performance than EHR features. Machine learning models can be used to prioritize patients for screening. However, screening only patients identified by our current model(s) would miss many patients. Future studies are warranted to optimize prediction of HRSNs.
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The ML models predicting HRSN in Medicaid and Medicare populations had AUROC 0.59-0.68.
“AUROC 0.59–0.68; composite any‐HRSN achieved AUROC 0.68; all models outperformed baseline assumption that Medicaid coverage alone predicted social needs”
All models outperformed the baseline assumption that Medicaid coverage alone predicted social needs.
“AUROC 0.59–0.68; composite any‐HRSN achieved AUROC 0.68; all models outperformed baseline assumption that Medicaid coverage alone predicted social needs”
The composite any-HRSN model achieved AUROC 0.68.
“AUROC 0.59–0.68; composite any‐HRSN achieved AUROC 0.68; all models outperformed baseline assumption that Medicaid coverage alone predicted social needs”
Holcomb et al. [30] reported the lowest AUROC range (0.59-0.68).
“Holcomb et al. [30] reported the lowest AUROC range (0.59–0.68)”
In Holcomb et al. [30], the composite any-HRSN outcome achieved the highest performance within that study.
“with the composite any‐HRSN outcome achieving the highest performance within that study”
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Cites
Advances in Neural Information Processing Systems 29
Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
(2017) · HAL · Cited by 10,512 · see citing works
A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515 · see citing works
Social determinants of health and diabetes: A scientific review
Hill-Briggs, F., Adler, N. E., Berkowitz, S. A., Chin, M. H., Gary-Webb, T. L., Navas-Acien, A., Thornton, P. L., & Haire-Joshu, D. (2021) · Diabetes Care · doi:10.2337/dci20-0053 · Cited by 1,732 · see citing works
Accountable Health Communities — Addressing Social Needs through Medicare and Medicaid
Alley, D. E., Asomugha, C. N., Conway, P. H., & Sanghavi, D. M. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1512532 · Cited by 606 · see citing works
Services, C. F. M. A. M., Billioux, A., Verlander, K., Services, C. F. M. A. M., Anthony, S., Services, C. F. M. A. M., Alley, D., & Services, C. F. M. A. M. (2017) · NAM Perspectives · doi:10.31478/201705b · Cited by 492 · see citing works
Fraze, T. K., Brewster, A. L., Lewis, V. A., Beidler, L. B., Murray, G. F., & Colla, C. H. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.11514 · Cited by 359 · cited 1× · see citing works
“…patients requires healthcare staff to build trust with patients and for healthcare systems to increase healthcare spending to ensure dedicated healthcare staff, electronic health record (EHR) infrastructure, other resources (e.g., funding, staff training, screening materials) and time 29–31 .”
BackgroundAndermann, A. (2018) · Public Health Reviews · doi:10.1186/s40985-018-0094-7 · Cited by 323 · see citing works
Integrating Data On Social Determinants Of Health Into Electronic Health Records
Cantor, M. N., & Thorpe, L. (2018) · Health Affairs · doi:10.1377/hlthaff.2017.1252 · Cited by 308 · see citing works
Lee, J. S., & Frongillo, E. (2001) · The Journals of Gerontology Series B · doi:10.1093/geronb/56.2.s94 · Cited by 297 · see citing works
Ye, C., Fu, T., Hao, S., Zhang, Y., Wang, O., Jin, B., Xia, M., Liu, M., Zhou, X., Wu, Q., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9268 · Cited by 250 · see citing works
Quantification of Neighborhood-Level Social Determinants of Health in the Continental United States
Kolak, M., Bhatt, J., Park, Y. H., Padrón, N. A., & Molefe, A. C. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.19928 · Cited by 235 · see citing works
Cook, J. T., Frank, D. A., Casey, P. H., Jacobs, R. R., Black, M. M., Chilton, M. M., de Cuba, S. E., Appugliese, D. P., Coleman, S. M., Heeren, T., et al. (2008) · PEDIATRICS · doi:10.1542/peds.2008-0286 · Cited by 226 · see citing works
Schickedanz, A. B., Hamity, C., Rogers, A., Sharp, A. L., & Jackson, A. H. (2019) · Medical Care · doi:10.1097/mlr.0000000000001051 · Cited by 225 · see citing works
Food insecurity, healthcare utilization, and high cost: a longitudinal cohort study.
Berkowitz, S. A., Seligman, H. K., Meigs, J. B., & Basu, S. (2018) · PubMed · Cited by 224 · see citing works
Silverman, J. B., Krieger, J. W., Kiefer, M. M., Hebert, P. L., Robinson, J., & Nelson, K. M. (2015) · Journal of General Internal Medicine · doi:10.1007/s11606-015-3351-1 · Cited by 208 · see citing works
LaForge, K., Gold, R. B., Cottrell, E., Bunce, A., Proser, M., Hollombe, C., Dambrun, K., Cohen, D. J., & Clark, K. D. (2017) · Journal of Ambulatory Care Management · doi:10.1097/jac.0000000000000221 · Cited by 181 · see citing works
Financial health as a measurable social determinant of health
Weida, E. B., Phojanakong, P., Patel, F., & Chilton, M. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0233359 · Cited by 168 · see citing works
I tried a bunch of things: The dangers of unexpected overfitting in classification of brain data
Hosseini, M., Powell, M., Collins, J., Callahan-Flintoft, C., Jones, W., Bowman, H., & Wyble, B. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.09.036 · Cited by 165 · see citing works
Gurewich, D. A., Garg, A., & Kressin, N. R. (2020) · Journal of General Internal Medicine · doi:10.1007/s11606-020-05720-6 · Cited by 162 · see citing works
Weir, R. C., Proser, M., Jester, M., Li, V., Hood-Ronick, C. M., & Gurewich, D. A. (2020) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2020.0075 · Cited by 152 · see citing works
Meddings, J. A., Reichert, H., Smith, S. N., Iwashyna, T. J., Langa, K. M., Hofer, T. P., & McMahon, L. F. (2016) · Journal of General Internal Medicine · doi:10.1007/s11606-016-3869-x · Cited by 136 · see citing works
Chronic disease burden predicts food insecurity among older adults
Jih, J., Stijacic‐Cenzer, I., Seligman, H. K., Boscardin, W. J., Nguyen, T. T., & Ritchie, C. S. (2018) · Public Health Nutrition · doi:10.1017/s1368980017004062 · Cited by 136 · see citing works
Schickedanz, A. B., Sharp, A. L., Hu, Y. R., Shah, N. R., Adams, J., Francis, D., & Rogers, A. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05123-2 · Cited by 133 · see citing works
Nagata, J. M., Palar, K., Gooding, H. C., Garber, A. K., Bibbins-Domingo, K. B., & Weiser, S. D. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05317-8 · Cited by 132 · see citing works
Cottrell, E. K., Hendricks, M., Dambrun, K., Cowburn, S., Pantell, M., Gold, R., & Gottlieb, L. M. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.16852 · Cited by 126 · cited 1× · see citing works
“However, the U.S. currently lacks standards and guidelines related to the collection of social needs screening data, particularly in healthcare settings 25–28 .”
BackgroundCole, M. B., & Nguyen, K. H. (2020) · Health Services Research · doi:10.1111/1475-6773.13555 · Cited by 118 · see citing works
A Practical Approach to Screening for Social Determinants of Health.
O’Gurek, D. T., & Henke, C. (2019) · PubMed · Cited by 109 · see citing works
Fiori, K. P., Heller, C. G., Rehm, C. D., Parsons, A. S., Flattau, A., Braganza, S. F., Lue, K., Lauria, M. E., & Racine, A. D. (2020) · American Journal of Public Health · doi:10.2105/ajph.2020.305717 · Cited by 91 · see citing works
Bhavsar, N. A., Gao, A., Phelan, M., Pagidipati, N. J., & Goldstein, B. A. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.2716 · Cited by 89 · see citing works
Hatef, E., Ma, X., Rouhizadeh, M., Singh, G., Weiner, J. P., & Kharrazi, H. (2021) · Population Health Management · doi:10.1089/pop.2020.0043 · Cited by 88 · see citing works
Functional limitation and chronic diseases are associated with food insecurity among U.S. adults
Venci, B. J., & Lee, S. Y. (2018) · Annals of Epidemiology · doi:10.1016/j.annepidem.2018.01.005 · Cited by 78 · see citing works
Bensken, W. P., Alberti, P. M., & Koroukian, S. M. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-021-06646-3 · Cited by 74 · see citing works
Kasthurirathne, S. N., Vest, J. R., Menachemi, N., Halverson, P. K., & Grannis, S. J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx130 · Cited by 71 · see citing works
Measuring the Effect of Social Determinants on Patient Outcomes: A Systematic Literature Review
Knighton, A. J., Stephenson, B., & Savitz, L. A. (2018) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2018.0009 · Cited by 70 · see citing works
Screening for Health-Related Social Needs of Emergency Department Patients
Samuels‐Kalow, M. E., Boggs, K. M., Cash, R. E., Herrington, R., Mick, N. W., Rutman, M. S., Venkatesh, A. K., Zabbo, C. P., Sullivan, A. F., Hasegawa, K., et al. (2020) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2020.08.010 · Cited by 68 · see citing works
Nijhawan, A. E., Clark, C., Kaplan, R., Moore, B. J., Halm, E. A., & Amarasingham, R. (2012) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0b013e31826ebc83 · Cited by 62 · see citing works
Collection of social determinants of health in the community clinic setting: a cross-sectional study
Kusnoor, S. V., Koonce, T. Y., Hurley, S., McClellan, K. M., Blasingame, M. N., Frakes, E. T., Huang, L., Epelbaum, M. I., & Giuse, N. B. (2018) · BMC Public Health · doi:10.1186/s12889-018-5453-2 · Cited by 62 · see citing works
Vest, J. R., & Ben-Assuli, O. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.06.013 · Cited by 61 · see citing works
Uses and Misuses of Patient- and Neighborhood-level Social Determinants of Health Data
Gottlieb, L. M., Francis, D., & Beck, A. F. (2018) · The Permanente Journal · doi:10.7812/tpp/18-078 · Cited by 59 · see citing works
Cassarino, M., Robinson, K., Quinn, R., Naddy, B., O’Regan, A., Ryan, D., Boland, F., Ward, M. E., McNamara, R., O’Connor, M., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220709 · Cited by 59 · see citing works
Hao, S., Wang, Y., Jin, B., Shin, A. Y., Zhu, C., Huang, M., Zheng, L., Luo, J. J., Hu, Z., Fu, C., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140271 · Cited by 58 · see citing works
Chen, M. D. B. S. (2020) · The American Journal of Managed Care · doi:10.37765/ajmc.2020.42142 · Cited by 57 · see citing works
Zhang, Y., Zhang, Y., Sholle, E., Abedian, S., Sharko, M., Turchioe, M. R., Wu, Y., & Ancker, J. S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235064 · Cited by 57 · see citing works
Accelerating the Implementation of Social Determinants of Health Interventions in Internal Medicine
Byhoff, E., Freund, K. M., & Garg, A. (2017) · Journal of General Internal Medicine · doi:10.1007/s11606-017-4230-8 · Cited by 44 · see citing works
Provider Perspectives on the Collection of Social Determinants of Health
Palacio, A., Seo, D., Medina, H., Singh, V., Suarez, M., & Tamariz, L. (2018) · Population Health Management · doi:10.1089/pop.2017.0166 · Cited by 41 · see citing works
Addressing Health-Related Social Needs: Value-Based Care or Values-Based Care?
Berkowitz, S. A., Baggett, T. P., & Edwards, S. T. (2019) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05087-3 · Cited by 40 · see citing works
Joffe, E., Byrne, M. J., Reeder, P., Herskovic, J. R., Johnson, C. W., McCoy, A. B., Sittig, D. F., & Bernstam, E. V. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001744 · Cited by 39 · see citing works
Self-citationHow Do Social Needs Cluster Among Low-Income Individuals?
Kreuter, M. W., Garg, R., Li, L., McNulty, L., Thompson, T., McQueen, A., & Luke, A. A. (2020) · Population Health Management · doi:10.1089/pop.2020.0107 · Cited by 38 · see citing works
A Theoretical Framework for Clinical Implementation of Social Determinants of Health
Hammond, G., & Maddox, K. E. J. (2019) · JAMA Cardiology · doi:10.1001/jamacardio.2019.3805 · Cited by 35 · see citing works
Toward Understanding Social Needs Among Primary Care Patients With Uncontrolled Diabetes
Chambers, E. C., McAuliff, K. E., Heller, C. G., Fiori, K. P., & Hollingsworth, N. (2021) · Journal of Primary Care & Community Health · doi:10.1177/2150132720985044 · Cited by 33 · see citing works
Social Needs, Chronic Conditions, and Health Care Utilization among Medicaid Beneficiaries
McQueen, A., Li, L., Herrick, C., Verdecias, N., Brown, D. S., Broussard, D. J., Smith, R., & Kreuter, M. W. (2021) · Population Health Management · doi:10.1089/pop.2021.0065 · Cited by 33 · see citing works
Jin, B., Zhao, Y., Hao, S., Shin, A. Y., Wang, Y., Zhu, C., Hu, Z., Fu, C., Ji, J., Wang, Y., et al. (2016) · BMC Emergency Medicine · doi:10.1186/s12873-016-0074-5 · Cited by 32 · see citing works
Comparing the performance of two social risk screening tools in a vulnerable subpopulation
Lewis, C. C., Wellman, R., Jones, S. W., Walsh‐Bailey, C., Thompson, E., Derus, A. J., Paolino, A. R., Steiner, J. F., Marchis, E. H. D., Gottlieb, L. M., et al. (2020) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_650_20 · Cited by 30 · see citing works
Roberts, E. T., Mellor, J. M., McInerney, M., & Sabik, L. M. (2019) · Health Services Research · doi:10.1111/1475-6773.13205 · Cited by 30 · see citing works
The association of social determinants of health with health outcomes
Krause, T. M., Schaefer, C., & Highfield, L. D. (2021) · The American Journal of Managed Care · doi:10.37765/ajmc.2021.88603 · Cited by 27 · see citing works
Self-citationMiller, R. L., Boyer, C. B., Chiaramonte, D. M., Lindeman, P. T., Chutuape, K. S., Walker, B. C., Kapogiannis, B. G., Wilson, C. M., & Fortenberry, J. D. (2017) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2017.0105 · Cited by 20 · see citing works
The potential value of social determinants of health in predicting health outcomes
Ancker, J. S., Kim, M. H., Zhang, Y., Zhang, Y., & Pathak, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy061 · Cited by 14 · see citing works
Dual eligible patients are not the same
Alberti, P. M., & Baker, M. C. (2020) · Medicine · doi:10.1097/md.0000000000022245 · Cited by 11 · see citing works
A Conceptual Framework for Addressing Social Needs Through the Accountable Health Communities Model
Highfield, L. D. (2020) · The American Journal of Accountable Care · doi:10.37765/ajac.2020.88681 · Cited by 7 · see citing works
Self-citationPayne, R., Esse, T. W., Qian, J., Mohan, A., Vadhariya, A., Becho-Dominguez, G., Serna, O., Villarreal, K., & Abughosh, S. (2021) · Journal of Managed Care & Specialty Pharmacy · doi:10.18553/jmcp.2021.27.5.544 · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 62 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- [2]Green, K. & Zook, M. When Talking About Social Determinants, Precision Matters | Health Affairs Forefront. 10.1377/forefront.20191025.776011/full/. · DOI
- [17]National Academies of Sciences Engineering, and Medicine Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health. Integrating Social Care into the Delivery of Health Care: Moving Upstream to Improve the Nation’s Health 2019 National Academies Press (US) 31940159
- [69]Why More Evidence is Needed on the Effectiveness Of Screening For Social Needs Among High-Use Patients In Acute Care Settings | Health Affairs Forefront. 10.1377/forefront.20190520.243444/full/. · DOI
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Sedani, A. E., Gomez, S. L., Lawrence, W. R., Moore, J. X., Brandt, H. M., & Rogers, C. R. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.49556 · Cited by 38 · see citing works
Telzak, A., Levano, S. R., Haughton, J., Chambers, E. C., & Fiori, K. P. (2024) · Journal of Clinical and Translational Science · doi:10.1017/cts.2024.519 · Cited by 16 · see citing works
Craven, C. K., Highfield, L. D., Basit, M. A., Bernstam, E. V., Choi, B. Y., Ferrer, R. L., Gelfond, J., Pruitt, S. L., Kannan, V., Shireman, P. K., et al. (2024) · Journal of Clinical and Translational Science · doi:10.1017/cts.2024.2 · Cited by 13 · see citing works
Self-citationAbegaz, T. M., Ahmed, M. B., Ali, A. A., & Bhagavathula, A. S. (2025) · Bioengineering · doi:10.3390/bioengineering12020166 · Cited by 12 · see citing works
Jung, D., Pollack, H. A., & Konetzka, R. T. (2022) · Journal of Applied Gerontology · doi:10.1177/07334648221129548 · Cited by 12 · see citing works
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Rahman, Y., & Dua, P. (2025) · Healthcare Analytics · doi:10.1016/j.health.2025.100411 · Cited by 11 · see citing works
Hatef, E., Chang, H.-Y., Richards, T. M., Kitchen, C., Budaraju, J., Foroughmand, I., Lasser, E., & Weiner, J. P. (2024) · JMIR Formative Research · doi:10.2196/54732 · Cited by 8 · see citing works
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Vest, J. R., Wu, W., Gregory, M. E., Kasthurirathne, S. N., Mendonça, E. A., Bian, J., Magoč, T., Grannis, S. J., McNamee, C., & Harle, C. A. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.27426 · Cited by 7 · see citing works
Phuong, J., Riches, N. O., Calzoni, L., Datta, G., Duran, D. G., Lin, Y., Singh, R. P., Solomonides, A. E., Whysel, N., & Kavuluru, R. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac162 · Cited by 7 · see citing works
Mazurenko, O., Hirsh, A. T., Harle, C. A., Shen, J., McNamee, C., & Vest, J. R. (2024) · PLoS ONE · doi:10.1371/journal.pone.0312193 · Cited by 3 · see citing works
Mazurenko, O., Harle, C. A., Musey, P. I., Schleyer, T., Sanner, L. M., & Vest, J. R. (2025) · JAMIA Open · doi:10.1093/jamiaopen/ooaf060 · Cited by 3 · see citing works
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Cossio, M. (2023) · doi:10.33774/coe-2023-xvh49 · Cited by 3 · see citing works
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Hatef, E., Kitchen, C., Gray, G. M., Zirikly, A., Richards, T. M., Ahumada, L. M., & Weiner, J. P. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae117 · Cited by 2 · see citing works
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Friedman, H., Li, M., Harvey, K. L. L., Griesemer, I., Mohr, D. C., Linsky, A. M., & Gurewich, D. A. (2024) · Journal of General Internal Medicine · doi:10.1007/s11606-024-08862-z · Cited by 2 · see citing works
Leng, L., Suárez, P. X. A., Momodu, M. A., Srirangapatanam, S., & Bayne, D. B. (2025) · Urology · doi:10.1016/j.urology.2025.03.005 · Cited by 2 · see citing works
Tsai, M., Pollard, E. M., Coughlin, S. S., Vernon, M., & Rao, S. S. C. (2024) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2024.102825 · Cited by 1 · see citing works
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Wagner, J. A., Bermúdez‐Millán, A., & Feinn, R. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.113077 · Cited by 2 · see citing works
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Gour, S., & Randa, R. (2024) · Algorithms for intelligent systems · doi:10.1007/978-981-97-4533-3_22 · Cited by 1 · see citing works
Razzano, A. V. (2026) · Health Science Reports · doi:10.1002/hsr2.73083
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- Authors
- Holcomb, J., Oliveira, L. C., Highfield, L., Hwang, K. O., Giancardo, L., & Bernstam, E. V.
- Affiliations
- Sinai Urban Health Institute (US)
- Houston Methodist (US)
- The University of Texas Health Science Center at Houston (US)
- Memorial Hermann (US)
- Corresponding author
- Bernstam, Elmer Victor
- Venue
- Scientific Reports
- Published
- 2022-03-16
- Volume
- 12
- Issue
- 1
- Pages
- 4554-4554
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC8927567
- OpenAlex
- W4220891881
- Cited by
- 27
- References
- 62
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 11 · 2023: 1 · 2022: 3
- FWCI
- 1.89
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Cullen Trust for Health Care,United States
- National Center for Advancing Translational Sciences (UL1TR000371)
- Cancer Prevention and Research Institute of Texas (RP170668)
- Reynolds and Reynolds Professorship in Clinical Informatics
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- health-related social needs, social determinants of health, Medicaid, Medicare, universal screening, predictive modeling, electronic health records, community-level data, health equity
- MeSH terms
- Machine Learning, Aged, Medicare, Humans, Mass Screening, Medicaid, United States, Social Determinants of Health
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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