Hospitals Screen Patients for Social Needs, but Data Quality Varies Widely

When the Centers for Medicare and Medicaid Services began requiring hospitals to screen inpatients for health-related social needs in January 2024, it set off one of the largest data collection experiments in modern American medicine. The idea is deceptively simple: ask every admitted patient about food insecurity, housing instability, transportation barriers, utility difficulties, and interpersonal safety, then use the answers to connect people with resources that could improve their health. But a new observational study published in the Journal of General Internal Medicine offers the first large-scale look at what actually happens when a real health system rolls out this screening across an entire hospital network, and the results reveal both the promise and the practical friction of turning social care into a routine clinical metric.

Researchers led by Bradley Rowland, MD, of Wake Forest University School of Medicine and Atrium Health Wake Forest Baptist Medical Center, analyzed electronic health record data from May to December 2024 covering 197,305 eligible adult inpatient encounters across 18 hospitals in the Atrium Health system, an integrated network spanning three states in the Southeastern United States. The network includes two quaternary care centers and numerous regional hospitals, providing significant demographic, geographic, and socioeconomic diversity. The system deployed the Accountable Health Communities Health-Related Social Needs Screening Tool, the instrument available through CMS, with minor modifications adding two response options per domain: patient declined to answer and patient unable to answer. The patient’s primary admission nurse completes the screen as part of the admission intake forms in the electronic health record, and nursing staff received an online training module before implementation and a second module ten months later.

The study’s first major finding concerns data quality, evaluated using two complementary frameworks. Under the harmonized data quality framework, which assesses conformance, completeness, and plausibility against internal standards, the screening responses satisfied all three domains, meeting minimum standards for real-world electronic health record data analysis. Validation against an external gold standard was not possible because other institutions use heterogeneous screening tools and inpatient screening evidence remains limited. A secondary semantic framework interrogating whether data collected for operational purposes is fit for research use revealed a notable weakness: the Housing Instability domain of the AHC-HRSN tool allows 2,048 possible unique response combinations, far more than a care team member could reasonably navigate during an inpatient encounter, and the answer choices were not sufficiently distinct to warrant their use. The researchers suggest modifying that domain’s implementation to achieve higher impact with lower response burden.

Screening performance was impressively high overall. Of the 197,305 eligible encounters, 172,519, or 87.4 percent, had at least one social needs element assessed, with consistent rates across all months of the study period. But the headline number conceals substantial variation between sites. Hospitals in Market B, comprising one quaternary center and twelve regional hospitals, completed screens in 92.1 percent of eligible encounters, while Market A, with one quaternary academic center and four regional hospitals, reached only 73.4 percent. The gap between the two largest hospitals in the study was maintained over time and was not significantly affected by the post-implementation training module. Despite a unified tool and standardized training, screening rates varied widely across sites, and the authors note that qualitative work is needed to understand these site-level differences.

Among patients who were screened, 12.7 percent, or 21,900 encounters, registered positive for at least one social need, while 80.1 percent were negative and 7.2 percent were inconclusive due to incomplete responses, refusals, or inability to answer. At the patient level, 12.4 percent screened positive for at least one category. Breaking down the positivity rates by domain, 7.4 percent of patients registered positive on transportation questions, 5.5 percent on food insecurity, 4.1 percent on utilities, 3.9 percent on housing insecurity, and 1.4 percent on partner violence. Refusals and inability-to-answer responses were infrequent, at less than 1 percent and less than 3 percent respectively, suggesting that patients are generally willing to discuss social needs with their nurses.

The correlation structure of the screening data provides important technical validation. Using Cramer’s V, a measure of association between categorical variables ranging from 0 to 1, the researchers found moderate to strong correlations between questions within the same domain, such as food insecurity questions correlating at 0.87. But correlations between different domains were weaker, such as 0.44 between food and housing insecurity. This pattern is clinically meaningful: it confirms that questions within a domain hang together coherently, while also demonstrating that a positive answer in one domain does not reliably predict positive answers in others. The practical implication is that screening all five domains is essential, because patients with transportation needs are not necessarily the same patients facing food insecurity or housing instability.

The clinical characteristics of patients screening positive aligned well with established knowledge about social vulnerability. Positive screens were more likely among younger patients, males, Black or African American individuals, Medicaid recipients, and those with higher social vulnerability as measured by the CDC’s Social Vulnerability Index, a composite census-based measure incorporating socioeconomic, demographic, and housing-related factors. Patients screening positive had a median Social Vulnerability Index of 0.74 compared with 0.67 for negative screens, and were more likely to have Medicaid coverage at 23.1 percent versus 13.7 percent. Patients with active online portals were less likely to screen positive, as were patients without recent hospitalizations. These patterns strengthen the validity of the tool, because the patient populations registering positive screens are precisely those the literature identifies as facing limited social resources.

On clinical outcomes, the picture is more nuanced. Among the 160,117 encounters with a positive or negative result, the overall 30-day readmission rate was 11.4 percent. Patients screening positive had higher raw readmission rates, 14.2 percent versus 11.0 percent for negative screens, and also showed higher annual hospitalization rates. However, after adjusting for age, sex, race, patient portal access, insurance class, Social Vulnerability Index, and prior year admissions using a mixed-effects logistic regression model with patient and hospital site as random intercepts, the significant difference in readmission rates disappeared. This suggests that the social needs captured by the screen overlap substantially with other clinical and socioeconomic risk factors already known to predict readmission, and that the incremental predictive value of the screening data for this particular outcome remains to be demonstrated.

The authors frame their findings as a functional first step in understanding the value of social needs screening across healthcare systems, particularly in the Southeastern United States. The diverse patient population and variety of care environments, from rural to suburban settings, lend hope that even smaller systems may benefit from these investigations. From a data quality standpoint, the researchers conclude that the AHC-HRSN tool and their implementation approach can be considered a ready option for other health systems contemplating deployment, with the caveat that the Housing Instability domain warrants modification. The implementation process appears sustainable, and the clinical correlations validate the screening instrument at significant scale.

Yet the study also highlights the gap between collecting social needs data and acting on it meaningfully. When patients screen positive, an automated alert is sent to the care coordination team, which then refers patients to FindHelp.Org, a zip code-based online resource platform. Whether such referrals translate into improved health outcomes remains an open question, and the authors acknowledge that insights attendant to this work remain preliminary. The CMS mandate presents an opportunity for health systems to provide more holistic and personalized care, and may offer improved risk stratification for important clinical outcomes. But as the researchers caution, for this potential to be realized, health systems must verify and validate their screening data and leverage it in a meaningful way to benefit patients, otherwise the requirement risks becoming yet another metric for metric’s sake. The 87.4 percent screening rate achieved across 18 hospitals demonstrates that large-scale implementation is feasible; the remaining challenge is ensuring that the data collected actually changes what happens to patients after they leave the hospital.

Subject of Research: Health systems research on social determinants of health screening implementation

Article Title: Evaluating the Implementation of Social Determinants of Health Screening Across 18 Hospitals

Article References: Rowland, B., Gee, J., Obermiller, C., Thorpe, A., Hernandez, J., Osiecki, L., Silber, R., & Houston, T. (2026). Evaluating the Implementation of Social Determinants of Health Screening Across 18 Hospitals. Journal of General Internal Medicine. https://doi.org/10.1007/s11606-026-10662-6

Image Credits: AI Generated

DOI: 10.1007/s11606-026-10662-6

Keywords: social determinants of health, health-related social needs, hospital screening, CMS mandate, electronic health records, data quality, health equity, readmission rates, Atrium Health, Journal of General Internal Medicine, Evaluating, Implementation