{"id":7070422,"date":"2026-09-30T12:10:40","date_gmt":"2026-09-30T12:10:40","guid":{"rendered":"https:\/\/som2nypost.com\/?p=7070422"},"modified":"2026-09-30T12:10:40","modified_gmt":"2026-09-30T12:10:40","slug":"can-social-determinants-of-health-predict-your-patients-future","status":"publish","type":"post","link":"https:\/\/som2nypost.com\/?p=7070422","title":{"rendered":"Can Social Determinants of Health Predict Your Patient\u2019s Future?"},"content":{"rendered":"


\n<\/p>\n

\n

The evidence is mixed but suggests
\nthat these overlooked variables have a profound impact on each patient\u2019s
\njourney.\u00a0<\/span><\/h4>\n
\n
<\/a><\/div>\n<\/div>\n

This article was written by Tim
\nSuther, Nicole Hobbs, Jeff McGinn, Matt Turner with Change Healthcare, John
\nHalamka, MD, MS, president of Mayo Clinic Platform, and Paul Cerrato, senior
\nresearch analyst and communications specialist, Mayo Clinic Platform.<\/span><\/span><\/i><\/p>\n

By one estimate, social determinants
\nof health (SDoH) influence up to <\/span>80% of health outcomes<\/span><\/a>. Although reports like this suggest that these
\nsocial factors have a major impact, thought leaders continue to debate whether
\nthey can also enhance the accuracy in predictive models. Resolving that debate
\nis far from simple because the answer depends on the type, source and quality
\nof the data, and the design of the model under consideration.<\/span><\/p>\n

In general, we derive SDoH from subjective
\nand objective sources. Subjective data includes self-reported or clinician-collected
\ndata such as patient reported outcomes, Z codes from ICD-10-CM that report
\nfactors that influence health status and interactions with health service
\nproviders, and other unstructured EHR data. Objective data includes individual-level
\nand community-level data from government, public and private (and consumer
\nbehavior) sources; it\u2019s usually more structured and often derived from national-level
\ndatasets.<\/span><\/p>\n

Unfortunately, the <\/span>research on the value of SDoH<\/span><\/a> in predictive models varies widely. Some
\nstudies report no appreciable differences when SDoH are injected into models,
\nwhile others report significant enhancements to predictive power.
\nUnsurprisingly, these varying study results depend in part on levels of reliance
\non traditional clinical models and, most importantly, on the types and sources
\nof SDoH data employed in the studies.<\/span><\/p>\n

For example, a group from Johns
\nHopkins Bloomberg School of Public Health demonstrated SDoH predictive models
\ncan fail in part due to predictive model design as well as to EHR-level data
\nthat is unstructured and collected inconsistently. \u00a0They also demonstrated that dependence on data
\nfrom EHR-derived population health databases for SDoH can be problematic
\nbecause the data tends to be used as a proxy for individual-level social factors.
\n\u00a0The problem lies in the fact that these
\nproxies are often based on assumptions, not evidence. <\/span>Other research<\/span><\/a> supports the above and showcases the
\nchallenges of using SDoH data from sources that traditionally struggle with the
\ncomprehensive collection and standardization of these data types.<\/span><\/p>\n

On a more positive note, <\/span>several studies<\/span><\/a> <\/span>and<\/span><\/a>
\n<\/span>healthcare articles<\/span><\/a> have reported success by relying on objectively
\ncollected and\/or highly structured and consistent data. For example, <\/span>one study<\/span><\/a> that used EHR-derived SDoH data sources found that the addition of
\nstructured data on median income, unemployment rate, \u00a0and education from trustworthy non-EHR sources
\n\u00a0enhanced their model\u2019s health prediction
\ngranularity for
\nsome of the most vulnerable subgroups of patients.<\/span> <\/span>In another study<\/span><\/a>, collaboration between Stanford, Harvard, and the Imperial
\nCollege London found that adding structured SDoH data from the US Census, along
\nwith using machine learning techniques, improved risk prediction model accuracy
\nfor hospitalization, death, and costs. They also showed that their models based
\non SDoH alone, as well as those based on clinical comorbidities alone, could predict
\nhealth outcomes and costs. Similarly, <\/span>researchers at The Ohio State University
\nCollege<\/span><\/a> of Medicine
\nadded community-level and consumer behavior data not available in standard EHR
\ndata and found it enhanced the study of and impact on obesity prevention. \u00a0<\/span>Juhn et. al. at Mayo Clinic<\/span><\/a> tapped telephone survey data and appended housing and
\nneighborhood characteristic data from local government sources to create a
\nsocioeconomic status index (HOUSES). They first showed that HOUSES correlated
\nwell with outcome measures and <\/span>later showed that HOUSES<\/span><\/a> could even serve as a predictive tool for graft
\nfailure in patients.<\/span><\/p>\n

Patient Level SDoH + Clinical Data =
\nPredictive Power<\/span><\/span><\/b><\/p>\n

Incorporating social factors into the healthcare equation can
\nfill gaps needed at the point of care, but it also generates better healthcare predictions,
\nbut only when these determinants are patient level and linked to robust clinical
\ndata. Change Healthcare, for example, has curated such an integrated
\nnational-level dataset, linking billions of historical de-identified distinct
\nmedical claims with patient-level social, physical and behavioral determinants
\nof health. One of this dataset\u2019s most important uses is to understand the
\nrelative weight of specific patient SDOH factors, in comparison to clinical
\nfactors alone, for various therapeutic conditions, including COVID-19. For
\nexample, across Change Healthcare\u2019s research, economic stability is repeatedly
\nranked as the highest or among the highest predictors of the healthcare
\nexperience. Despite this realization, most end users, including providers and
\npayers, lack such visibility (or rely on geographic averages that are unhelpful
\nin making accurate predictive models).<\/span><\/p>\n

Incorporating SDoH data into
\npredictive models holds much promise. Given the relative newness of SDoH data
\nin predictive analytics, along with a lack of data standardization and scale, it\u2019s
\nnot surprising to find varying degrees of success in using it to improve predictive
\nhealth models. But as researchers learn more about the best types and sources of
\nSDoH data to use, along with developing better-suited models for these types of
\ndata, we\u2019re likely to see significant advances in healthcare predictive models.
\nBy combining the right data with the right models, SDoH are a powerful asset in
\npredictive models of health, outcomes, and potential health disparities.<\/span><\/span><\/p>\n

If you’re still with us . . .<\/b><\/p>\n

Please consider supporting\u00a0Dr. Steve Parodi<\/a>, Reed Abelson<\/a> and I<\/span>\u00a0by “voting up” on our panel at the upcoming South by Southwest conference<\/a> in March of 2022. Our proposed panel, “Extending the Stethoscope Into the Home,” will\u00a0dive into a discussion about acute health
\ncare for patients in their home and the infrastructures needed to support it. If you are so inclined to vote, please do so\u00a0here<\/a>.<\/span><\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"

The evidence is mixed but suggests that these overlooked variables have a profound impact on each patient\u2019s journey.\u00a0 This article was written by Tim Suther, Nicole Hobbs, Jeff McGinn, Matt Turner with Change Healthcare, John Halamka, MD, MS, president of Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform. […]<\/p>\n","protected":false},"author":1,"featured_media":7070423,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[81356,2481,597,4405,11681,10974],"dealstore":[],"offerexpiration":[],"class_list":["post-7070422","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-determinants","tag-future","tag-health","tag-patients","tag-predict","tag-social"],"yoast_head":"\nCan Social Determinants of Health Predict Your Patient\u2019s Future? - Som2ny Network<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/som2nypost.com\/?p=7070422\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can Social Determinants of Health Predict Your Patient\u2019s Future? - Som2ny Network\" \/>\n<meta property=\"og:description\" content=\"The evidence is mixed but suggests that these overlooked variables have a profound impact on each patient\u2019s journey.\u00a0 This article was written by Tim Suther, Nicole Hobbs, Jeff McGinn, Matt Turner with Change Healthcare, John Halamka, MD, MS, president of Mayo Clinic Platform, and Paul Cerrato, senior research analyst and communications specialist, Mayo Clinic Platform. 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