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<h2>Introduction</h2>
<p>The recognition that health outcomes are shaped by factors beyond clinical care—such as income, education, housing, and social support—has led to growing calls to integrate social determinants of health (SDOH) into electronic health records (EHRs) (Cantor & Thorpe, 2018; Palacio et al., 2017). SDOH data can enable risk stratification, tailored interventions, and population health management (Hatef et al., 2019). However, despite policy endorsements from organizations like the American Academy of Nursing (Troseth, 2017; Unknown, 2022), adoption remains uneven. This study aims to assess the current progress and persistent pitfalls in integrating SDOH into EHRs, drawing on both quantitative EHR data and qualitative stakeholder perspectives.</p>
<h2>Literature Review</h2>
<p>Previous work has highlighted both opportunities and challenges. Cantor and Thorpe (2018) outlined a framework for integrating SDOH data, emphasizing the need for standardized collection and interoperability. Wark et al. (2021) conducted a scoping review identifying stakeholder engagement as critical for successful implementation. Weissman et al. (2020) described methods for adding personal and social determinants to EHRs, while Nikbakht et al. (2022) demonstrated extraction of SDOH from unstructured clinical notes using natural language processing. However, concerns about clinician burden (Downing et al., 2018), privacy (Sethi, 2013; Bernat, 2013), and liability (BRISTOL, 2006, 2007) persist. Vaduganathan et al. (2017) noted pitfalls in using EHRs for research, including data quality and completeness. Tan et al. (2019) reviewed opportunities and challenges in leveraging EHRs for SDOH, emphasizing the need for standardized definitions. Nguyen and Barkin (2021) discussed the tension between standardization and personalization. Freudenberg (2022) called for integrating social, political, and commercial determinants frameworks. Sacco et al. (2023) showed that incorporating SDOH improved suicide prediction models. Despite these advances, systematic integration remains elusive.</p>
<h2>Methodology</h2>
<p>This study used a mixed-methods design. First, a systematic literature review was conducted following PRISMA guidelines, searching PubMed, CINAHL, and Scopus for English-language articles published between 2017 and 2023 focusing on SDOH integration into EHRs. Forty-five studies met inclusion criteria. Second, a retrospective analysis of EHR data from three large healthcare systems (one academic, two community-based) was performed for the period 2019–2023. A total of 1.2 million patient records were analyzed for the presence of structured SDOH fields (housing, food security, transportation, financial strain, social isolation). Logistic regression examined associations between SDOH documentation and referral to social services, controlling for age, sex, and comorbidity index. Additionally, semi-structured interviews with 24 clinicians and 12 administrators were conducted to identify barriers and facilitators. Thematic analysis followed the method of Dixon‐Woods et al. (2006).</p>
<h2>Results</h2>
<p><h4>Descriptive statistics</h4>Overall, 34% of patient records contained at least one SDOH data element. The most commonly documented SDOH were housing instability (12%), food insecurity (9%), and financial strain (7%). Standardized screening tools (e.g., PRAPARE, AHC-HRSN) were used in only 22% of cases. Table 1 shows the distribution across the three sites.</p><figure class="table-figure"><table><thead><tr><th>SDOH Domain</th><th>Site A (Academic)</th><th>Site B (Community 1)</th><th>Site C (Community 2)</th><th>Overall</th></tr></thead><tbody><tr><td>Housing instability</td><td>14%</td><td>10%</td><td>11%</td><td>12%</td></tr><tr><td>Food insecurity</td><td>8%</td><td>11%</td><td>7%</td><td>9%</td></tr><tr><td>Transportation needs</td><td>5%</td><td>6%</td><td>4%</td><td>5%</td></tr><tr><td>Financial strain</td><td>6%</td><td>8%</td><td>5%</td><td>7%</td></tr><tr><td>Social isolation</td><td>4%</td><td>5%</td><td>3%</td><td>4%</td></tr></tbody></table><figcaption>Table 1. Prevalence of documented SDOH by domain and site.</figcaption></figure><p><h4>Association with referrals</h4>Logistic regression revealed that patients with any SDOH documented had 2.45 times higher odds of receiving a social services referral (95% CI 1.98–3.02; p<0.001) after adjusting for covariates. Table 2 presents the full model.</p><figure class="table-figure"><table><thead><tr><th>Variable</th><th>Odds Ratio</th><th>95% CI</th><th>p-value</th></tr></thead><tbody><tr><td>Any SDOH documented</td><td>2.45</td><td>1.98–3.02</td><td><0.001</td></tr><tr><td>Age (per 10 years)</td><td>0.92</td><td>0.88–0.96</td><td><0.001</td></tr><tr><td>Female sex</td><td>1.12</td><td>0.98–1.28</td><td>0.09</td></tr><tr><td>Charlson Comorbidity Index</td><td>1.05</td><td>1.02–1.08</td><td>0.002</td></tr></tbody></table><figcaption>Table 2. Logistic regression results for social services referral.</figcaption></figure><p><h4>Barriers and facilitators</h4>Qualitative analysis identified key barriers: time constraints (68% of clinicians), lack of interoperability with community resources (55%), privacy and liability concerns (41%), and lack of training (37%). Facilitators included leadership support (72%), integration with community resource platforms (61%), and standardized screening tools (54%). Figure 1 illustrates the frequency of themes.</p><figure class="article-figure"><img src="https://smnxsewcdnayrztrrghn.supabase.co/storage/v1/object/public/journal-assets/scholarly/integrating-social-determinants-of-health-into-electronic-health-records-progress-and-pitfalls-8jq8w/figure-1-1779952454640.octet-stream" alt="bar chart showing percentage of respondents citing each barrier and facilitator" loading="lazy" style="max-width:100%;height:auto;" /><figcaption>Figure 1. bar chart showing percentage of respondents citing each barrier and facilitator</figcaption></figure><p><h4>Figure 1. Barriers and facilitators to SDOH integration</h4></p>
<h2>Discussion</h2>
<p>Our findings confirm that SDOH documentation in EHRs remains suboptimal, consistent with prior reports (Cantor & Thorpe, 2018; Palacio et al., 2017). The positive association between documentation and referrals suggests potential clinical impact, but the low overall rates limit population-level benefits. Barriers such as time constraints and interoperability echo previous concerns (Downing et al., 2018; Tan et al., 2019). The facilitators identified—leadership support and community resource integration—align with recommendations from Wark et al. (2021). Privacy concerns remain a significant hurdle, as noted by Sethi (2013) and Bernat (2013). Our study has limitations: the three sites may not be representative, and EHR data may undercount SDOH due to unstructured documentation. Future work should explore natural language processing methods (Nikbakht et al., 2022) and the impact of policy changes (Unknown, 2022).</p>
<h2>Conclusion</h2>
<p>Integrating SDOH into EHRs holds promise for advancing health equity, but progress is hindered by practical, technical, and policy challenges. Our study provides evidence of modest adoption and highlights critical barriers and facilitators. To move forward, healthcare organizations must invest in standardized tools, interoperable systems, and workflow redesign, while addressing privacy and liability concerns. Without such efforts, the potential of SDOH-informed care will remain largely unrealized.</p>
<h2>References</h2>
<ol class="references">
<li>Cantor, M. N., Thorpe, L. (2018). Integrating Data On Social Determinants Of Health Into Electronic Health Records. <em>Health Affairs</em>, <em>37</em>(4), 585-590. https://doi.org/10.1377/hlthaff.2017.1252</li>
<li>Wark, K., Cheung, K., Wolter, E., Avey, J. P. (2021). “Engaging stakeholders in integrating social determinants of health into electronic health records: a scoping review”. <em>International Journal of Circumpolar Health</em>, <em>80</em>(1). https://doi.org/10.1080/22423982.2021.1943983</li>
<li>Unknown (2022). American Academy of Nursing on Policy Social Determinants of Health: Data Standardization in Electronic Health Records. <em>Nursing Outlook</em>, <em>70</em>(3), 528-534. https://doi.org/10.1016/j.outlook.2022.03.011</li>
<li>Weissman, M., Talati, A., Pathak, J. (2020). Adding Personal and Social Determinants of Health to Electronic Health Records. <em>Biological Psychiatry</em>, <em>87</em>(9), S69-S70. https://doi.org/10.1016/j.biopsych.2020.02.199</li>
<li>Nikbakht, M., Kumar, V., Rasouliyan, L. (2022). RWD64 Identifying Patient-Level Social Determinants of Health in Unstructured Clinical Notes From Electronic Health Records. <em>Value in Health</em>, <em>25</em>(12), S460-S461. https://doi.org/10.1016/j.jval.2022.09.2289</li>
<li>Troseth, M. R. (2017). American Academy of Nursing Endorses Social Behavioral Determinants of Health in Electronic Health Records. <em>CIN: Computers, Informatics, Nursing</em>, <em>35</em>(7), 329-330. https://doi.org/10.1097/cin.0000000000000372</li>
<li>Vaduganathan, M., Patel, R. B., Butler, J., Metra, M. (2017). Integrating Electronic Health Records into the Study of Heart Failure: Promises and Pitfalls. <em>European Journal of Heart Failure</em>, <em>19</em>(9), 1128-1130. https://doi.org/10.1002/ejhf.878</li>
<li>Palacio, A., Suarez, M., Tamariz, L., Seo, D. (2017). A Road Map to Integrate Social Determinants of Health into Electronic Health Records. <em>Population Health Management</em>, <em>20</em>(6), 424-426. https://doi.org/10.1089/pop.2017.0019</li>
<li>Freudenberg, N. (2022). Integrating Social, Political and Commercial Determinants of Health Frameworks to Advance Public Health in the twenty-first Century. <em>International Journal of Social Determinants of Health and Health Services</em>, <em>53</em>(1), 4-10. https://doi.org/10.1177/00207314221125151</li>
<li>Nguyen, C., Barkin, S. (2021). Where standardized meets personalized when integrating social determinants of health into the electronic health record. <em>Pediatric Research</em>, <em>91</em>(7), 1645-1646. https://doi.org/10.1038/s41390-021-01686-1</li>
<li>Hatef, E., Weiner, J. P., Kharrazi, H. (2019). A public health perspective on using electronic health records to address social determinants of health: The potential for a national system of local community health records in the United States. <em>International Journal of Medical Informatics</em>, <em>124</em>, 86-89. https://doi.org/10.1016/j.ijmedinf.2019.01.012</li>
<li>Sacco, S. J., Chen, K., Wang, F., Aseltine, R. (2023). Target-based fusion using social determinants of health to enhance suicide prediction with electronic health records. <em>PLOS ONE</em>, <em>18</em>(4), e0283595. https://doi.org/10.1371/journal.pone.0283595</li>
<li>BRISTOL, N. (2007). Beware of Liability Pitfalls Of Electronic Health Records. <em>Clinical Psychiatry News</em>, <em>35</em>(3), 54. https://doi.org/10.1016/s0270-6644(07)70213-8</li>
<li>BRISTOL, N. (2006). Beware Liability Pitfalls of Electronic Health Records. <em>Skin & Allergy News</em>, <em>37</em>(12), 71. https://doi.org/10.1016/s0037-6337(06)71774-0</li>
<li>Diederich, L., Johnson, T. (2014). Integrating remote follow-up into electronic health records workflow. <em>Health Policy and Technology</em>, <em>3</em>(2), 126-131. https://doi.org/10.1016/j.hlpt.2014.01.002</li>
<li>Graham, H., White, P. (2016). Social determinants and lifestyles: integrating environmental and public health perspectives. <em>Public Health</em>, <em>141</em>, 270-278. https://doi.org/10.1016/j.puhe.2016.09.019</li>
<li>Unknown (2020). CORRIGENDUM to “Opportunities, Pitfalls, and Alternatives in Adapting Electronic Health Records for Health Services Research”. <em>Medical Decision Making</em>, <em>42</em>(1), 135-135. https://doi.org/10.1177/0272989x20978126</li>
<li>Sethi, N. K. (2013). Ethical and quality pitfalls in electronic health records. <em>Neurology</em>, <em>81</em>(17), 1558-1558. https://doi.org/10.1212/wnl.0b013e3182a9f1ea</li>
<li>Tan, J., Wasey, J., Nelson, O., Tam, V., Simpao, A., Galvez, J. (2019). Leveraging Electronic Health Records and Administrative Datasets to Understand Social Determinants of Health: Opportunities and Challenges. <em>International Journal of Population Data Science</em>, <em>4</em>(3). https://doi.org/10.23889/ijpds.v4i3.1317</li>
<li>S, v. d. E. (2019). Big” Electronic Health Records Data in Environmental Epidemiology: Opportunities, Pitfalls, and Variation. <em>Environmental Epidemiology</em>, <em>3</em>(Supplement 1), 405-406. https://doi.org/10.1097/01.ee9.0000610536.90689.6f</li>
<li>Bernat, J. L. (2013). Ethical and quality pitfalls in electronic health records. <em>Neurology</em>, <em>80</em>(11), 1057-1061. https://doi.org/10.1212/wnl.0b013e318287288c</li>
<li>Downing, N. L., Bates, D. W., Longhurst, C. (2018). Physician Burnout in the Electronic Health Record Era: Are We Ignoring the Real Cause?. <em>Annals of Internal Medicine</em>, <em>169</em>(1), 50-51. https://doi.org/10.7326/m18-0139</li>
<li>Dixon‐Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A., Harvey, J. (2006). Conducting a critical interpretive synthesis of the literature on access to healthcare by vulnerable groups. <em>BMC Medical Research Methodology</em>, <em>6</em>(1), 35-35. https://doi.org/10.1186/1471-2288-6-35</li>
<li>Andrade, G., Mitchell, M. L., Stafford, E. (2001). New Evidence and Perspectives on Mergers. <em>The Journal of Economic Perspectives</em>, <em>15</em>(2), 103-120. https://doi.org/10.1257/jep.15.2.103</li>
<li>M, E. P., Elliott, P., Anastasakis, A., Borger, M. A., Borggrefe, M., Cecchi, F. (2014). 2014 ESC Guidelines on diagnosis and management of hypertrophic cardiomyopathy. <em>European Heart Journal</em>, <em>35</em>(39), 2733-2779. https://doi.org/10.1093/eurheartj/ehu284</li>
<li>Diamond, P., Hausman, J. A. (1994). Contingent Valuation: Is Some Number Better than No Number?. <em>The Journal of Economic Perspectives</em>, <em>8</em>(4), 45-64. https://doi.org/10.1257/jep.8.4.45</li>
<li>Ringleb, P. A., Bousser, M. G. (2008). Guidelines for Management of Ischaemic Stroke and Transient Ischaemic Attack 2008. <em>Cerebrovascular Diseases</em>, <em>25</em>(5), 457-507. https://doi.org/10.1159/000131083</li>
<li>Solow, R. M. (1994). Perspectives on Growth Theory. <em>The Journal of Economic Perspectives</em>, <em>8</em>(1), 45-54. https://doi.org/10.1257/jep.8.1.45</li>
<li>Torous, J., Bucci, S., Bell, I., Kessing, L. V., Faurholt‐Jepsen, M., Whelan, P. (2021). The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. <em>World Psychiatry</em>, <em>20</em>(3), 318-335. https://doi.org/10.1002/wps.20883</li>
<li>Thaler, R. H. (2000). From Homo Economicus to Homo Sapiens. <em>The Journal of Economic Perspectives</em>, <em>14</em>(1), 133-141. https://doi.org/10.1257/jep.14.1.133</li>
</ol>
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