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

The study, published in BMC Public Health, examined inpatients admitted to the First Affiliated Hospital of Henan Medical University between January 2018 and December 2025. The research team, led by Danxia Ji and Kang Li with corresponding author Jianhua Zhao, identified rare disease cases using ICD-10 diagnostic codes mapped to China’s two national rare disease catalogs, released in 2018 and 2023. Because rare diseases are scattered across virtually every medical specialty and often lack standardized coding, building a reliable case series from routine hospital data is a methodological challenge in itself. By anchoring the search to the official national lists, the investigators aimed to capture a clinically meaningful and policy-relevant cohort rather than an arbitrary assortment of uncommon diagnoses.

What emerged was a dataset of 3,496 rare disease inpatient cases, representing 0.38 percent of all hospitalizations at the center during the study window. That fraction may sound trivial, but the trajectory tells a different story. The annual count of rare disease admissions rose consistently across the eight-year period, reaching a peak of 1,098 cases in 2025, which alone accounted for 31.4 percent of the entire eight-year total. In other words, nearly a third of all rare disease hospitalizations recorded over the study period occurred in its final year. The authors used ARIMA time-series modeling to project the trend forward, and the model predicts a continued upward climb in 2026, suggesting the observed growth is not a statistical fluke but a sustained pattern.

Part of that increase likely reflects genuine growth in case volume, but the authors and the study design also point to the role of improved recognition. China’s publication of the 2018 national rare disease list gave hospitals a shared framework for identifying and coding these conditions, and the 2023 expansion of that list broadened the diagnostic net further. A tertiary referral hospital in a densely populated province is precisely the kind of institution where patients with long-unexplained symptoms eventually arrive, so rising admission counts may partly represent patients who previously circulated through the system without a rare disease label ever being attached to their records.

The demographic profile of the cohort was broad in every sense. Men accounted for 55.6 percent of cases and women 44.4 percent, and the mean age was 49.56 years with a strikingly large standard deviation of 24.52 years. That spread means the cohort ranged from children to the very elderly, underscoring a central difficulty of rare disease medicine: these conditions do not respect age boundaries, and a hospital service designed around common adult or pediatric disease patterns will struggle to accommodate such heterogeneity. Geographically, the cohort was heavily regional, with 98.1 percent of cases coming from just five cities and Xinxiang, the hospital’s home city, predominating. That concentration reflects both the catchment area of a provincial tertiary center and the reality that patients with complex, rare conditions tend to funnel toward the highest tier of care available to them.

By disease category, the burden was dominated by two organ systems. Nervous system diseases made up 29.1 percent of cases and respiratory system diseases 21.4 percent, together accounting for roughly half of all rare disease hospitalizations. Among individual diagnoses, idiopathic pulmonary fibrosis was the single most frequent condition, with 745 cases representing 21.3 percent of the cohort. This chronic, progressively scarring lung disease is a well-known driver of repeated hospitalizations in the later stages of illness, and its prominence here illustrates how a handful of relatively better-recognized rare conditions can shape the overall inpatient picture even when hundreds of other diagnoses contribute smaller numbers.

The economic findings are among the most consequential for policy. The highest mean total hospitalization cost was recorded for perinatal rare diseases, at 35,800 Chinese yuan on average with a standard deviation of 26,037 yuan, reflecting the intensive neonatal and obstetric care these cases demand. Tumor-related rare diseases followed closely, with mean costs of 20,353 yuan and a standard deviation of 19,955 yuan, a figure inflated by oncology workups, targeted therapies, and prolonged stays. The wide standard deviations in both categories reveal how uneven the financial hit can be even within a single diagnostic group: some patients incur modest costs while others generate bills many times the average. For households in a province where a large share of the population relies on the Urban-Rural Resident Basic Medical Insurance scheme, out-of-pocket exposure to costs of this magnitude can be catastrophic, which is precisely why the study’s authors frame their data as an evidence base for healthcare policy rather than a purely clinical exercise.

Readmission patterns added another layer of nuance. Among the top 15 diseases ranked by readmission rate, ANCA-associated vasculitis, an autoimmune condition in which the body’s own antibodies attack small blood vessels, had the highest rate at 83.5 percent, meaning the vast majority of affected patients returned to the hospital after an index admission. At the other extreme, multiple system atrophy, a progressive neurodegenerative disorder, had the lowest readmission rate among the group at 9.7 percent, a figure that likely reflects the relentless, end-stage trajectory of that disease rather than successful outpatient management. Between those poles, the readmission landscape varied enormously, and the authors used wavelet analysis to test whether hospitalizations clustered in predictable monthly cycles. They found no significant periodicity, indicating that rare disease admissions do not follow seasonal rhythms the way respiratory infections do, and therefore cannot be anticipated with calendar-based staffing or bed planning.

The study’s technical toolkit deserves attention because it signals a shift in how hospital epidemiology is being done. Beyond standard descriptive statistics, the team applied wavelet analysis, a signal-processing method that decomposes time-series data into different frequency components to detect periodic structure, and ARIMA modeling, a classical autoregressive integrated moving average framework for forecasting counts from their own past values. Neither method is exotic by the standards of time-series research, but applying them to rare disease admission data is relatively novel, and the combination allowed the authors to separate trend from noise and to generate a quantitative forecast for 2026 rather than a vague expectation of continued growth.

The limitations of a single-center retrospective design are real and the authors do not disguise them. The data capture only patients who were hospitalized at one tertiary institution in northern Henan, so the findings cannot be generalized to the whole province or country, and cases managed entirely in outpatient clinics or smaller hospitals are invisible here. Coding practices may also have shifted over the eight-year window, particularly after the national catalogs changed how clinicians label rare diagnoses. Still, the value of the work lies in its granularity. By quantifying who is admitted, with what conditions, at what cost, and how often they come back, the study gives regional health planners in China something they have historically lacked: concrete numbers on which to base decisions about rare disease care capacity, insurance coverage, referral networks, and specialist training. As China’s national rare disease policy framework matures, studies of this kind, replicated across other regions and centers, will be essential to turning a list of rare disease names into a functioning system of care for the millions of patients those names represent.

Subject of Research: Characteristics and hospitalization burden of rare disease inpatients at a tertiary hospital in northern Henan, China, from 2018 to 2025

Article Title: Characteristics and hospitalization burden of rare disease inpatients in Northern Henan, 2018–2025: a single-center retrospective observational study

Article References: Ji, D., Li, K., Wang, N., Chen, Z., Zhai, X., Ren, R., & Zhao, J. (2026). Characteristics and hospitalization burden of rare disease inpatients in Northern Henan, 2018–2025: a single-center retrospective observational study. BMC Public Health. https://doi.org/10.1186/s12889-026-29790-z

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29790-z

Keywords: rare diseases, hospitalization burden, tertiary hospital, Henan, ICD-10 coding, idiopathic pulmonary fibrosis, ANCA-associated vasculitis, readmission rates, ARIMA model, wavelet analysis, healthcare costs, BMC Public Health

The analysis was conducted by the Centre for Behavioural and Implementation Science Interventions (BISI) at the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), working alongside the clinical leadership of NUHS@Home. Rather than simply reporting outcomes, the team applied implementation science methods, a discipline concerned with how evidence-based practices can be embedded reliably into real-world systems and workflows, to understand what it actually takes to move a small pilot into a full-scale service. Professor Nick Sevdalis, Director of BISI, worked with Dr Stephanie Ko, lead of NUHS@Home, to systematically map the considerations and approaches needed for scaling the programme between 2022 and 2025. The findings were presented at the Behavioural and Implementation Sciences in Health, Asia Conference 2026 (BISHAC 2026), Singapore’s first conference dedicated to these fields, as a real-world case study in accelerating healthcare transformation.

The numbers behind the scale-up are striking. NUHS@Home is NUHS’ Mobile Inpatient Care service, delivering acute-level care to patients who would otherwise require ward-based hospitalisation but who are clinically stable enough to be managed in their own homes. Launched in September 2020 as a three-bed proof-of-concept pilot, the service was subsequently pivoted to support COVID-19 care as part of the national pandemic response, a period that gave the team early operational experience in caring for acutely unwell patients outside hospital walls. Since May 2022, the service has grown from an average of 48 patients a month to more than 300 a month by 2025, a roughly seven-fold increase in patient volume. Virtual beds scaled even faster, expanding fifteen-fold from 6 to 90 over the same period.

Perhaps the most telling statistic concerns the service’s share of overall capacity. At full scale, NUHS@Home beds make up 3.3 percent of total bed capacity across NUHS, up from 0.6 percent at the start of the scale-up, a roughly 5.5-fold increase in relative share. That figure captures a structural shift rather than a temporary surge: a growing proportion of hospital-level care within the cluster is now being delivered safely at home instead of in a ward. Over the scale-up period, the programme saved an estimated 21,800 hospital bed days, capacity that was freed up for patients whose conditions genuinely require ward-based treatment. In systems where bed occupancy routinely runs close to operational limits, the ability to create that much headroom without constructing a single new ward carries obvious strategic value.

Crucially, the analysis found that rapid growth did not come at the cost of quality. As the service expanded, 30-day readmission rates and home visit frequency showed no statistically significant difference across the scale-up period. Readmission rates are one of the most closely scrutinised safety indicators for hospital-at-home models, because a deterioration at home that ends in an unplanned return to hospital is the key failure mode the model must guard against. Home visit frequency, meanwhile, is a proxy for care intensity, since in-person assessment by clinical teams is what distinguishes true hospital-at-home care from remote monitoring alone. Holding both steady while monthly patient volumes grew seven-fold suggests that the expansion was achieved by adding capacity and broadening eligibility rather than by thinning the care delivered to each patient.

The implementation science analysis identified three factors associated with successful scale-up. The first was growing the healthcare workforce trained to deliver hospital care at home, which is a non-trivial challenge: clinicians accustomed to ward-based practice need to learn to work autonomously in patients’ homes, coordinate remotely, and make decisions without the immediate backup of a physical hospital environment. The second was expanding the range of clinical conditions that can be managed at home, including patients with cancer and those recovering after surgery, groups that push the model well beyond the simpler infectious-disease and general-medicine cases with which many hospital-at-home programmes begin. The third was strengthening quality and safety processes, ensuring that governance, escalation pathways and monitoring kept pace as the service multiplied in size and complexity.

For the clinical leadership, the human dimension of those statistics is the point of the exercise. “Behind every number in this study is a patient who got to recover in their own bed, surrounded by family, instead of in a hospital ward. Working with BISI helped us understand how to scale that option safely, so more patients now have the choice of receiving hospital-level care where they’re most comfortable,” said Dr Stephanie Ko, Lead of NUHS@Home. The sentiment reflects a broader body of patient-experience research suggesting that familiar surroundings, better sleep and reduced exposure to hospital-acquired infection are among the advantages patients cite when hospital-level care can be delivered at home.

The work also signals an institutional commitment to making this kind of translation routine rather than exceptional. To accelerate the movement of research into frontline care across NUHS, a dedicated BISI team will support the scale-up of effective projects under the auspices of the newly established NUHS Health Services and Population Health (HSPH) Unit, which combines behavioural and implementation sciences with data science and health economics. Established on 1 August 2026, the HSPH Unit is positioned to drive health services innovation and population health improvement across the cluster. “Good ideas don’t scale by themselves. With the right method, a proven innovation can be grown quickly, safely and at lower cost. By applying implementation science methods, we helped NUHS@Home identify how to best grow and get the right care to more patients faster. With the new NUHS Health Services and Population Health Unit, our goal is not only to help scale more effective programmes in NUHS, but to help build the cluster’s capability to translate evidence into better care, routinely,” said Professor Nick Sevdalis, Director of BISI and the HSPH Unit.

The NUHS@Home findings were presented at BISHAC 2026, held from 3 to 5 September 2026 and convened by BISI, one of the few centres in the world to combine behavioural science and implementation science expertise within a single organisation. The distinction between the two fields matters: behavioural science explains how people make decisions, while implementation science explains how evidence-based practices can be embedded into systems and workflows. BISHAC 2026 marks the first time the two fields have been brought together in a dedicated Singapore conference, with a deliberate focus on Asian health systems, where ageing populations, rising chronic disease burden and constrained infrastructure make scalable alternatives to conventional ward care especially urgent.

The conference also served as the launchpad for a pan-Asian research collaboration aimed at closing the persistent gap between what health research shows and what patients actually receive. The Hub for Implementation, Voices and Engagement (HIVE) initiative, jointly led by BISI and the School of Public Health of Zhejiang University, brought together implementation science researchers from across the region to set shared priorities for the next five to seven years. In total, 67 senior scientists across 15 Asian countries contributed expert views and identified 27 priority areas for accelerating the translation of evidence into practice. Among the top priorities were advocacy for implementation work with research funders, close working with communities, partnerships between researchers and policy makers, regional mentorship and training programmes, and sustainable financing for scaling successful health interventions. For the NUHS@Home team, the message of both the service data and the conference is the same: the evidence that hospital-level care can be delivered at home is no longer in doubt, and the frontier now lies in the disciplined, measurable work of helping proven models grow quickly, safely and affordably across diverse health systems.

Subject of Research: Implementation science-guided scale-up of a hospital-at-home service in Singapore

Article Title: NUHS@Home scales bed capacity 15-fold, saving 21,800 hospital bed-days

Article References: NUHS@Home scales bed capacity 15-fold, saving 21,800 hospital bed-days. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: hospital-at-home, NUHS@Home, implementation science, virtual beds, bed capacity, patient safety, readmission rates, NUS Medicine, BISHAC 2026, healthcare scaling, Mobile Inpatient Care, population health