Abstract

An integral part of delivering high-quality healthcare is understanding the social determinants of health (SDOH) of patients and of communities in which healthcare is provided. SDOH are defined by the World Health Organization as the conditions in which people are born, grow, live, work, and age.

SDOH, although experienced by individuals, exist at the community level. Healthcare systems that learn about the communities in which their patients live can adapt their services to meet the communities’ specific needs. This, in turn, can help patients and community members overcome obstacles to achieving and maintaining good health.

Usage

Additional information about SDOH is available on the Agency for Healthcare Research and Quality (AHRQ) SDOH website (https://www.ahrq.gov/sdoh/about.html).

AHRQ's database on Social Determinants of Health (SDOH) was created under a project funded by the Patient Centered Outcomes Research (PCOR) Trust Fund. The purpose of this project is to create easy to use, easily linkable SDOH-focused data to use in PCOR research, inform approaches to address emerging health issues, and ultimately contribute to improved health outcomes.The database was developed to make it easier to find a range of well documented, readily linkable SDOH variables across domains without having to access multiple source files, facilitating SDOH research and analysis.Variables in the files correspond to five key SDOH domains: social context (e.g., age, race/ethnicity, veteran status), economic context (e.g., income, unemployment rate), education, physical infrastructure (e.g, housing, crime, transportation), and healthcare context (e.g., health insurance). The files can be linked to other data by geography (county, ZIP Code, and census tract). The database includes data files and codebooks by year at three levels of geography, as well as a documentation file.The data contained in the SDOH database are drawn from multiple sources and variables may have differing availability, patterns of missing, and methodological considerations across sources, geographies, and years. Users should refer to the data source documentation and codebooks, as well as the original data sources, to help identify these patterns

This archived SDOH Database (beta version) is available for reference. The most recent version of the SDOH Database replaces the beta version and is available on the main page. To ensure consistency in variable names and construction, analyses should not combine data from the beta version and the updated database.Download DataThe SDOH Data Source Documentation (PDF, 1.5 MB) file contains information for researchers about the structure and contents of the database and descriptions of each data source used to populate the database.The Variable Codebook (XLSX, 494 KB) Excel file provides descriptive statistics for each SDOH variable by year.

Social Determinants of Health Market Outlook

According to our latest research, the global Social Determinants of Health (SDOH) market size reached USD 7.2 billion in 2024, reflecting robust momentum driven by the integration of advanced analytics and digital health solutions across healthcare ecosystems. The market is anticipated to expand at a CAGR of 22.8% from 2025 to 2033, with the total market size expected to reach USD 59.6 billion by 2033. This accelerated growth is primarily fueled by the increasing recognition of the critical impact that social, economic, and environmental factors have on health outcomes, as well as the growing adoption of value-based care models globally. As per our latest research, the demand for holistic patient care and the need to address health disparities are the main catalysts propelling the SDOH market forward.

The surge in the Social Determinants of Health market is fundamentally driven by the global shift towards preventive healthcare and population health management. Healthcare organizations are increasingly recognizing that clinical care alone accounts for only a fraction of overall health outcomes, with social determinants such as housing, education, employment, and food security playing a pivotal role. This realization is prompting investments in SDOH data collection, analytics, and intervention programs that enable healthcare providers and payers to identify at-risk populations, design targeted interventions, and ultimately improve health equity. The proliferation of electronic health records (EHRs) and interoperable data platforms is further facilitating the integration of SDOH insights into clinical workflows, enhancing the ability to deliver personalized and effective care.

Another major growth driver for the SDOH market is the transition to value-based care and risk-based reimbursement models. Governments and private payers worldwide are incentivizing healthcare organizations to focus on outcomes rather than volume, which necessitates a comprehensive understanding of the social and environmental factors influencing patient health. As a result, there is a growing demand for advanced analytics, machine learning, and artificial intelligence solutions that can process and interpret large volumes of SDOH data. These technologies are enabling stakeholders to stratify risk, predict adverse health events, and allocate resources more efficiently, thereby reducing costs and improving quality of care. The increasing availability of real-time data from wearable devices, mobile applications, and community sources is also expanding the scope and effectiveness of SDOH initiatives.

Furthermore, regulatory mandates and policy initiatives are playing a crucial role in accelerating the adoption of SDOH solutions. In the United States, for instance, the Centers for Medicare & Medicaid Services (CMS) and other agencies have introduced guidelines and incentive programs that require healthcare organizations to screen for and address social determinants as part of routine care. Similar efforts are being observed in Europe and Asia Pacific, where governments are prioritizing health equity and social inclusion in their public health agendas. These policies are not only driving demand for SDOH data analytics and intervention platforms but are also fostering collaboration between healthcare providers, payers, community organizations, and technology vendors, thereby creating a vibrant and dynamic market landscape.

From a regional perspective, North America continues to dominate the SDOH market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront due to its advanced healthcare infrastructure, strong regulatory support, and early adoption of health IT solutions. However, Asia Pacific is expected to witness the fastest growth over the forecast period, driven by rapid urbanization, rising healthcare expenditures, and increasing awareness of social health disparities. Europe also presents significant opportunities, especially with the implementation of digital health strategies and cross-sector collaborations aimed at addressing the root causes of health inequities. Latin America and the Middle East & Africa are gradually catching up, supported by government-led health reforms and international investments in healthcare infrastructure.

This dataset includes materials for an undergraduate learning activity focused on exploring the Social Determinants of Health (SDOH) through applied data analysis and mapping from Coastal Carolina University - Department of Nursing and Health Sciences. The download contains two files:a written assignment with step-by-step instructions, andan Excel file containing county-level health and SDOH data for South Carolina. The data were compiled from three sources (CDC PLACES, US Census Bureau's American Community Survey, Feeding America's Map the Meal Gap)Students use these materials to create maps, correlation matrices, and scatterplots in Microsoft Excel, enabling them to examine relationships between health outcomes and social factors such as poverty, education, and food access.

These datasets contain summarizing clusters and dimensions of place-based social determinant of health measures for the United States from AHRQ's Social Determinants of Health Database (https://www.ahrq.gov/sdoh/data-analytics/sdoh-data.html), along with the underlying SDOH data. Summary clusters and dimensions are available for both counties and Zip codes. The measures are taken from the 2019 and 2018 AHRQ SDOH datasets. Underlying SDOH measures are in the domains of social context, economic context, education, physical infrastructure, and healthcare context. The summary dimensions and cluster memberships for counties and Zip codes were generated using principal components analysis and hierarchical cluster analysis techniques to provide simple high-level representations of the SDOH context for counties and Zip codes.

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US Population Health Management (PHM) Market Size 2025-2029

The us population health management (phm) market size is forecast to increase by USD 6.04 billion at a CAGR of 7.4% between 2024 and 2029.

The Population Health Management (PHM) market in the US is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and analytics. These technologies enable healthcare providers to collect, analyze, and act on patient data to improve health outcomes and reduce costs. However, the high perceived costs associated with PHM solutions pose a challenge for some organizations, limiting their ability to fully implement and optimize these technologies. Despite this obstacle, the potential benefits of PHM, including improved patient care and population health, make it a strategic priority for many healthcare organizations. To capitalize on this opportunity, companies must focus on cost-effective solutions and innovative approaches to addressing the challenges of PHM implementation and optimization. By leveraging advanced analytics, cloud technologies, and strategic partnerships, organizations can overcome cost barriers and deliver better care to their patient populations.

What will be the size of the US Population Health Management (PHM) Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The Population Health Management (PHM) market in the US is experiencing significant advancements, integrating various elements to improve patient outcomes and reduce healthcare costs. Public health surveillance and data governance ensure accurate population health data, enabling healthcare leaders to identify health disparities and target interventions. Quality measures and health literacy initiatives promote transparency and patient activation, while data visualization and business intelligence facilitate data-driven decision-making. Behavioral health integration, substance abuse treatment, and mental health services address the growing need for holistic care, and outcome-based contracts incentivize providers to focus on patient outcomes. Health communication, community health workers, and patient portals enhance patient engagement, while wearable devices and mHealth technologies provide real-time data for personalized care plans. Precision medicine and predictive modeling leverage advanced analytics to tailor treatment approaches, and social service integration addresses the social determinants of health. Health data management, data storytelling, and healthcare innovation continue to drive market growth, transforming the industry and improving overall population health.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareServicesDeploymentCloudOn-premisesEnd-userHealthcare providersHealthcare payersEmployers and government bodiesGeographyNorth AmericaUS

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

Population Health Management (PHM) software in the US gathers patient data from healthcare systems and utilizes advanced analytics tools, including data visualization and business intelligence, to predict health conditions and improve patient care. PHM software aims to enhance healthcare efficiency, reduce costs, and ensure quality patient care. By analyzing accurate patient data, PHM software enables the identification of community health risks, leading to proactive interventions and better health outcomes. The adoption of PHM software is on the rise in the US due to the growing emphasis on value-based care and the increasing prevalence of chronic diseases. Machine learning, artificial intelligence, and predictive analytics are integral components of PHM software, enabling healthcare payers to develop personalized care plans and improve care coordination. Data integration and interoperability facilitate seamless data sharing among various healthcare stakeholders, while data visualization tools help in making informed decisions. Public health agencies and healthcare providers leverage PHM software for population health research, disease management programs, and quality improvement initiatives. Cloud computing and data warehousing provide the necessary infrastructure for storing and managing large volumes of population health data. Healthcare regulations mandate the adoption of PHM software to ensure compliance with data privacy and security standards. PHM software also supports care management services, patient engagement platforms, and remote patient monitoring, empowering patients

This data shows healthcare utilization for asthma by Allegheny County residents 18 years of age and younger. It counts asthma-related visits to the Emergency Department (ED), hospitalizations, urgent care visits, and asthma controller medication dispensing events.

The asthma data was compiled as part of the Allegheny County Health Department’s Asthma Task Force, which was established in 2018. The Task Force was formed to identify strategies to decrease asthma inpatient and emergency utilization among children (ages 0-18), with special focus on children receiving services funded by Medicaid. Data is being used to improve the understanding of asthma in Allegheny County, and inform the recommended actions of the task force. Data will also be used to evaluate progress toward the goal of reducing asthma-related hospitalization and ED visits.

Regarding this data, asthma is defined using the International Classification of Diseases, Tenth Revision (IDC-10) classification system code J45.xxx. The ICD-10 system is used to classify diagnoses, symptoms, and procedures in the U.S. healthcare system.

Children seeking care for an asthma-related claim in 2017 are represented in the data. Data is compiled by the Health Department from medical claims submitted to three health plans (UPMC, Gateway Health, and Highmark). Claims may also come from people enrolled in Medicaid plans managed by these insurers. The Health Department estimates that 74% of the County’s population aged 0-18 is represented in the data.

Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time. Missing from the data are the uninsured, members in participating plans enrolled for less than 90 continuous days in 2017, children with an asthma-related condition that did not file a claim in 2017, and children participating in plans managed by insurers that did not share data with the Health Department.

Data users should also be aware that diagnoses may also be subject to misclassification, and that children with an asthmatic condition may not be diagnosed. It is also possible that some children may be counted more than once in the data if they are enrolled in a plan by more than one participating insurer and file a claim on each policy in the same calendar year.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Global Social Determinants of Health Market size is growing with CAGR of 18.3% in the prediction period & crosses USD 25.29 Bn by 2032 from USD 7.8 Bn in 2025.

It is well-established that social determinants of health contribute to health and well-being. Among the social determinants of health, health-related social needs (HRSNs) are unmet needs that can be identified by the health care system and addressed through referral to community services. Despite the importance of identifying patients with HRSNs, none of the few screening tools for HRSNs available internationally have received a comprehensive psychometric validation. This study aims to conduct a qualitative validation of the Social Determinants of Health Screening Tool (SDoHST). This study took place at Lyell McEwin Hospital, a major tertiary hospital located in Adelaide, South Australia. Patient (n = 5) and stakeholder (n = 9) focus groups were conducted face to face, audio recorded, and transcribed verbatim. Inductive content analysis of focus group transcripts was performed to inform tool modifications (e.g. item rewording). The patient focus group recommended the addition of an explanatory paragraph to improve face validity, and highlighted the importance of reliable transport and internet access. The stakeholder focus group recommended using language that carries less stigma to this particular community and incorporating questions surrounding cultural, linguistic, and spiritual needs. The final version of the SDoHST included 12 items (four original items were removed and seven new items were added during the validation process). The SDoHST is the first validated tool to measure social determinants of health (and specifically HRSNs) in Australia, receiving a comprehensive qualitative validation. The instrument is readily available and future studies will further investigate its psychometric properties with quantitative methods. A brief guide to screening tools for social determinants of health and their validation The importance of social context in contributing to overall health is well-established. Social determinants of health (SDoH) are social and environmental factors such as employment, housing security, financial stability, social isolation, and personal safety, which contribute up to 60% of overall health. In recent years, there has been a paradigm shift in how healthcare systems view health and wellbeing. There is a growing call to intervene in adverse SDoH from within the healthcare system. One such intervention involves screening patients for unmet needs, such as housing or food insecurity, and providing appropriate connections to organizations in the community to assist with their needs. The screening tools implemented in this context are multiple and diverse. Some target only one or two factors while others assess multiple SDoH. However, little is reported about the development of these tools. If a screening tool is not thoroughly developed and validated, it is impossible to know whether the data collected with the tool are appropriate or relevant. This study details the development and validation of a screening tool for unmet social needs by community members and healthcare providers at a major metropolitan hospital in South Australia.

Real‑time Health Data Analytics Market Outlook

According to our latest research, the global real-time health data analytics market size reached USD 16.2 billion in 2024, and is projected to grow at a robust CAGR of 18.4% from 2025 to 2033, reaching an estimated value of USD 80.2 billion by 2033. This substantial growth is primarily driven by the increasing adoption of digital health solutions, the proliferation of connected medical devices, and the rising demand for instant, actionable healthcare insights to improve patient outcomes and operational efficiency worldwide.

One of the primary growth factors fueling the real-time health data analytics market is the rapid digitization of healthcare systems. Hospitals, clinics, and other healthcare providers are increasingly deploying electronic health records (EHRs), wearable devices, and remote monitoring solutions that generate vast volumes of real-time patient data. These technologies enable continuous tracking of vital signs, medication adherence, and other health metrics, allowing clinicians to make timely decisions and intervene early in case of anomalies. The integration of artificial intelligence (AI) and machine learning (ML) algorithms with real-time analytics platforms further enhances the ability to detect patterns, predict adverse events, and personalize treatment plans. As healthcare organizations strive to transition from reactive to proactive care models, the demand for sophisticated real-time analytics solutions is expected to surge.

Another significant driver for the real-time health data analytics market is the increasing emphasis on value-based care and population health management. Governments and payers across the globe are incentivizing healthcare providers to improve quality while reducing costs, which necessitates the use of advanced analytics for tracking patient outcomes, identifying high-risk populations, and optimizing resource allocation. Real-time analytics platforms empower healthcare professionals to aggregate and analyze data from multiple sources, including EHRs, claims, and social determinants of health, providing a holistic view of patient populations. By enabling early identification of trends and gaps in care, these solutions facilitate targeted interventions, reduce hospital readmissions, and support evidence-based decision-making, thereby aligning with the objectives of value-based healthcare delivery.

Moreover, the ongoing COVID-19 pandemic has underscored the critical importance of real-time health data analytics in managing public health crises. Governments and healthcare organizations worldwide have leveraged real-time analytics to monitor the spread of the virus, allocate resources, and optimize vaccination campaigns. The pandemic has accelerated the adoption of telemedicine, remote patient monitoring, and cloud-based analytics platforms, further expanding the scope of real-time data utilization. As the world continues to face emerging infectious diseases and chronic health challenges, the ability to rapidly analyze and act upon real-time health data will remain a strategic priority for both public and private sector stakeholders.

From a regional perspective, North America currently dominates the real-time health data analytics market, accounting for the largest revenue share in 2024, driven by advanced healthcare infrastructure, widespread adoption of digital health technologies, and strong regulatory support for interoperability and data sharing. Europe follows closely, with significant investments in health IT modernization and data-driven healthcare initiatives. The Asia Pacific region is poised for the fastest growth during the forecast period, fueled by expanding healthcare access, increasing government spending on digital health, and a burgeoning population of tech-savvy consumers. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by ongoing healthcare reforms and rising awareness regarding the benefits of real-time analytics in improving care delivery.

Component Analysi

BackgroundDespite the incentives and provisions created for hospitals by the US Affordable Care Act related to value-based payment and community health needs assessments, concerns remain regarding the adequacy and distribution of hospital efforts to address SDOH. This scoping review of the peer-reviewed literature identifies the key characteristics of hospital/health system initiatives to address SDOH in the US, to gain insight into the progress and gaps.MethodsPRISMA-ScR criteria were used to inform a scoping review of the literature. The article search was guided by an integrated framework of Healthy People SDOH domains and industry recommended SDOH types for hospitals. Three academic databases were searched for eligible articles from 1 January 2018 to 30 June 2023. Database searches yielded 3,027 articles, of which 70 peer-reviewed articles met the eligibility criteria for the review.ResultsMost articles (73%) were published during or after 2020 and 37% were based in Northeast US. More initiatives were undertaken by academic health centers (34%) compared to safety-net facilities (16%). Most (79%) were research initiatives, including clinical trials (40%). Only 34% of all initiatives used the EHR to collect SDOH data. Most initiatives (73%) addressed two or more types of SDOH, e.g., food and housing. A majority (74%) were downstream initiatives to address individual health-related social needs (HRSNs). Only 9% were upstream efforts to address community-level structural SDOH, e.g., housing investments. Most initiatives (74%) involved hot spotting to target HRSNs of high-risk patients, while 26% relied on screening and referral. Most initiatives (60%) relied on internal capacity vs. community partnerships (4%). Health disparities received limited attention (11%). Challenges included implementation issues and limited evidence on the systemic impact and cost savings from interventions.ConclusionHospital/health system initiatives have predominantly taken the form of downstream initiatives to address HRSNs through hot-spotting or screening-and-referral. The emphasis on clinical trials coupled with lower use of EHR to collect SDOH data, limits transferability to safety-net facilities. Policymakers must create incentives for hospitals to invest in integrating SDOH data into EHR systems and harnessing community partnerships to address SDOH. Future research is needed on the systemic impact of hospital initiatives to address SDOH.

Card data by location. Description of data: Data is broken down by location to show each card that was completed. Each CHC site has two corresponding data sheets. The first sheet contains the raw data including totals which show how many SDH was recorded. The second sheet contains tables with counts for each SDH and how often it was billed and/or diagnosed. (XLSX 235 kb)

IntroductionSocial determinants of health significantly shape community discharge (CD) rates in post-acute rehabilitation settings. Additionally, healthcare disparities between urban and rural regions in the United States can affect these discharge rates. These disparities underscore the critical need to understand how social, economic, educational, and healthcare-related factors influence community discharge outcomes to guide equitable healthcare strategies.MethodsThis observational, cross-sectional study analyzed 40,476 ZIP code tabulation area (ZCTA)-level data points linked to rehabilitation service areas and Agency for Healthcare Research and Quality datasets. Exploratory and confirmatory factor analyses identified five social determinants of health domains—social, economic, educational, physical infrastructure, and healthcare—which were assessed using structural equation modeling to evaluate their direct and mediated effects on community discharge rates.ResultsSignificant disparities in community discharge rates were observed across urban and rural areas. Urban areas exhibited lower community discharge rates, influenced by higher social and economic deprivation and limited English proficiency. Conversely, rural areas demonstrated higher rates, attributed to areal social, economic, and education characteristics. Key factors affecting community discharge outcomes included economic inequities, limited healthcare access, and transportation barriers.ConclusionTargeted interventions addressing economic inequities, healthcare access, and transportation challenges are essential to improving community discharge outcomes. These findings inform policy and healthcare practices aimed at fostering equitable rehabilitation services and optimizing community reintegration.

Recent developments include: March 2022:Azure Health Data Services aims to simplify the management and analysis of PHI, allowing healthcare organizations to gain insights and make informed decisions while maintaining data privacy and security. It provides tools such as Azure API for FHIR, Azure Cognitive Search, and Azure Machine Learning to help healthcare providers, researchers, and other stakeholders in the industry., November 2020:Change Healthcare's SDoH Analytics is a platform that leverages big data analytics to provide a deeper understanding of the impact of social determinants on health outcomes. This information can be used by healthcare organizations to improve patient care and reduce costs by addressing non-medical factors that contribute to negative health outcomes, such as poverty, lack of access to healthy food, and limited transportation options.. Notable trends are: Demand for Population Health Analytics to Boost Market Growth.

Supplementary data for journal article "Is life expectancy higher in countries and territories with publicly funded healthcare?: Global analysis of healthcare access and the social determinants of health" at Journal of Global Health. Data on life expectancy, access to publicly funded healthcare and 10 social determinants for 196 countries and 4 major territories across the world.

The data on health care facilities includes the name and location of all the hospitals and primary care facilities in Allegheny County. The current listing of hospitals and primary care facilities is managed by the Allegheny County Health Department and is used in internal reporting and shared for public use.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

The global Healthcare Data Analytics Solutions market, valued at $335 million in 2025, is projected to experience robust growth, driven by a compound annual growth rate (CAGR) of 4.2% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of electronic health records (EHRs) and the rising volume of healthcare data generate a significant need for advanced analytics to improve operational efficiency, enhance patient care, and reduce healthcare costs. Furthermore, the growing prevalence of chronic diseases and the aging population necessitate more sophisticated data-driven insights for disease prevention, personalized medicine, and improved treatment outcomes. Government initiatives promoting interoperability and data sharing across healthcare systems further contribute to market growth. The market is segmented by application (Home Healthcare and Commercial Healthcare) and type (Software and Service), with software solutions likely holding a larger market share due to their scalability and flexibility. Competitive landscape analysis reveals a diverse range of players, including established technology giants like IBM and AWS, along with specialized healthcare data analytics companies like Health Catalyst and Datavant, and numerous regional providers. The North American market currently dominates, benefiting from advanced technological infrastructure and high healthcare expenditure. However, other regions, particularly Asia Pacific, are anticipated to witness significant growth, driven by increasing healthcare investments and technological advancements. The market's growth trajectory is influenced by several dynamic trends. The integration of artificial intelligence (AI) and machine learning (ML) into healthcare data analytics is transforming the industry, enabling predictive analytics, improved diagnostics, and more effective treatment strategies. The rise of cloud-based solutions is enhancing accessibility, scalability, and cost-effectiveness for healthcare providers of all sizes. However, challenges such as data security and privacy concerns, the complexity of data integration, and the need for skilled professionals to manage and interpret data analytics insights present potential restraints on market expansion. Overcoming these challenges through robust cybersecurity measures, standardized data formats, and investments in workforce training will be crucial for realizing the full potential of this market. The forecast period (2025-2033) presents significant opportunities for companies to innovate and capitalize on the increasing demand for advanced healthcare data analytics solutions.

Project Overview This project is designed to bridge the gap in medical education by integrating the SNAP Challenge, an experiential learning exercise where participants live on the average Supplemental Nutrition Assistance Program (SNAP) budget—about $4–5 per day—to understand the realities of food insecurity. Most medical students come from affluent backgrounds, limiting their first-hand experience with poverty. Traditional curricula rely on lectures, but experiential learning fosters deeper understanding and motivation for change. The Transformative Care Continuum (TCC) program at Ohio University’s Heritage College of Osteopathic Medicine (OUHCOM) immerses first-year medical students in this structured experience, incorporating journaling, clinical case logs, and quality improvement initiatives to connect learning to real-world patient care. Unlike voluntary initiatives, this program embeds the challenge into training, enhancing empathy, clinical decision-making, and awareness of social determinants of health to better prepare future family medicine physicians. Data and Data Collection Overview This study collected data in August 2024 from forty medical students who had participated in the SNAP exercise as first-year students (between 2018 and 2023) through reflective journals, patient-centered observation forms, clinical case logs, and follow-up surveys to assess the impact of the SNAP Challenge on medical students' understanding of food insecurity. Students documented their daily experiences on a limited SNAP budget through reflective journals, capturing challenges and personal insights. Faculty assessed students’ ability to apply motivational interviewing and empathy using patient-centered observation forms during clinical encounters. Clinical case logs tracked students’ recognition of food insecurity in patient interactions and documentation in electronic health records. A long-term follow-up survey, conducted 9 months to 5 years post- participation, evaluated knowledge retention, continued engagement with food insecurity issues, and application of skills in clinical practice, with a 50% response rate (21/40). Results showed that students increased their awareness of food insecurity, improved patient screening, and initiated community-based interventions to address food insecurity in healthcare settings. Shared Data Organization The data file shared here contains the responses (including free-text entries) to the follow-up survey. The documentation files include the recruitment email, informed consent form and survey questionnaire used for the study, as well as this Data Narrative and an administrative README file.

The global medical-social work services market is experiencing robust growth, driven by several key factors. An aging global population, increasing prevalence of chronic diseases requiring ongoing care, and a rising demand for integrated healthcare models are significantly boosting market expansion. The integration of social work into healthcare settings improves patient outcomes, reduces hospital readmissions, and enhances overall patient satisfaction, leading to increased investment in these services. Technological advancements, such as telehealth platforms and electronic health records, are also streamlining workflows and expanding access to medical-social work services. Based on industry reports and observed growth trends in related sectors, we estimate the 2025 market size to be approximately $150 billion, with a Compound Annual Growth Rate (CAGR) of 7% projected from 2025 to 2033. This growth is further fueled by the increasing awareness of mental health issues and the expanding role of social workers in addressing social determinants of health, such as poverty, housing insecurity, and lack of access to resources. This contributes to a holistic approach to patient care, moving beyond purely clinical interventions. The market segmentation, encompassing services like patient intake screening, counseling, and education, across various settings including hospitals, nursing homes, and residential treatment centers, reflects the diverse applications and growing demand for this crucial healthcare support. While the market exhibits significant growth potential, some challenges remain. These include workforce shortages within the social work profession, particularly in underserved areas, reimbursement complexities, and the need for effective data integration across different healthcare systems. Despite these hurdles, the positive impact on patient care, increasing investment in healthcare infrastructure, and the growing emphasis on value-based care models will sustain and propel the market’s expansion throughout the forecast period. Competition among providers is intensifying, with established healthcare systems and specialized social work agencies vying for market share, driving innovation and service improvements. The market’s growth trajectory points to a continuously expanding role for medical-social work services in the future of healthcare.