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Abstract

Background:

Addressing social determinants of health in patient care helps hospitals better understand the non-medical factors influencing patients’ health outcomes.

Objectives:

The objective of this study was to evaluate the correlation between hospital characteristics, county determinants, and the systematic recording of health-related social needs among general and surgical acute care hospitals in the United States. It focused on the hospital’s routine collection of data on patients’ health-related social needs, such as transportation, housing, and food insecurity.

Design:

A cross-sectional retrospective study design was utilized.

Methods:

All hospitals that completed the American Hospital Association Annual survey (n = 2254) were included in the study. A series of multinomial logistic analyses were conducted.

Results:

The relative risk of hospitals routinely collecting health-related social needs data is 67% lower in for-profit hospitals and 90% higher in not-for-profit hospitals compared to government hospitals. Hospitals that are part of a system are 1.5 times more likely to routinely collect data on social needs. In addition, counties with higher household income have a statistically significant higher relative risk of hospitals collecting data on social needs, though the magnitude of the difference is small. The relative risk of hospitals collecting social needs data, but not routinely, is 2 times higher in teaching hospitals and 3 times higher among system hospitals.

Conclusion:

Our research strongly indicates that understanding and addressing these inherent hospital-related factors are essential for effectively integrating social determinants of health into routine healthcare data collection practices. Establishing more robust guidelines and standardization in these practices may enhance hospitals’ ability to document and utilize health-related social needs information, ultimately driving improved patient outcomes and supporting more equitable care.

Keywords

Introduction

The social determinants of health (SDOH) encompass multiple non-medical factors that may influence both individual and population health outcomes. Examples of these factors include where people are born, grow, work, live, and age, as well as the systems that shape the conditions of daily life.¹ Social determinants of health can be grouped into 2: upstream and downstream factors.^2,3 Downstream determinants are more easily observed and studied, as they directly precede health outcomes. Examples include individual behaviors such as physical activity, smoking, and access to medical care. Downstream determinants are profoundly influenced by upstream determinants, which operate on a more systematic level and shape the environments in which individuals live, work, and grow. Upstream determinants include factors such as income inequality, educational opportunities, and the availability of social support networks. Addressing these upstream determinants presents a greater challenge due to their complexity, long-term nature, and the intricate pathways through which they impact health outcomes.

Previous literature suggests that SDOH as a whole may contribute between 30% and 55% of health outcomes.^2,4-6 Understanding the distribution and impact of these determinants across different groups and communities is important for shaping targeted interventions and policy changes that promote health equity.^7,8 Policies related to SDOH reporting aim to standardize the collection and utilization of SDOH data to improve patient care and health outcomes. For example, the Centers for Medicare & Medicaid Services (CMS), the federal agency tasked with overseeing health insurance programs for older adults and low-income individuals in the United States, has taken significant steps to integrate social determinants of health (SDOH) into electronic health records (EHRs). Through its Promoting Interoperability Programs, CMS has encouraged healthcare providers to utilize EHR systems for sharing comprehensive patient data, including SDOH-related information. This initiative aims to improve care coordination and foster a more holistic approach to patient health by addressing both medical and social factors.⁹

SDOH reporting involves the systematic collection, analysis, and sharing of data related to the social and environmental conditions that influence patient health. This can include information on housing stability, food security, education, employment, access to healthcare, and social support networks, among others. The goal of SDOH reporting is to provide healthcare providers, policymakers, and community organizations with actionable insights that can shape targeted interventions to address health disparities and improve health outcomes.¹⁰ Addressing SDOH in primary care settings has been recognized as an important consideration in providing patient-centered care and care coordination among clinical care, public health, behavioral health, and community services through integrating SDOH data in patients’ clinical records.¹¹ Most primary care settings have collected data on income, employment, education, financial resource strain, social connections, etc. A recent study found that financial issues, food insecurity, transportation, housing insecurity, and employment were the most frequent patient-reported factors impacting their health outcomes.¹² Several studies showed that providing services reflecting patients’ social needs resulted in positive patient health outcomes. Prescheduled, free rides to primary care appointments and home delivery of medically and non-medically tailored meals were associated with improvement in primary care show rate and a reduction in ED visits, and inpatient admissions.^13,14 However, there is limited information on addressing SDOH in hospital settings, so it is necessary to explore the importance of integration of SDOH into EHR at the hospital level.

When hospitals consider SDOH in their patient care protocols, they can better understand the context of each patient’s life and the non-medical factors that may influence their health outcomes. This comprehensive approach can lead to more personalized and effective treatment plans, improved patient satisfaction, and overall better health outcomes.¹⁵ By addressing SDOH, hospitals can tailor interventions that not only treat the medical condition but also the underlying social or environmental causes.¹⁶ This holistic approach not only enhances patient quality of care but also has the potential to improve healthcare access and reduce costs. For example, a patient with asthma may benefit from medical treatment, but if they return to a home with mold and poor air quality, their condition may not improve. By considering the patient’s living conditions, healthcare providers can offer more comprehensive care, and cultivating partnerships within the community in which it operates leads to better short-term and long-term health outcomes.¹⁷ In addition, this can improve patient engagement, adherence to treatment plans, and overall satisfaction with the healthcare system. Amidst the COVID-19 pandemic, many patients with both medical and social concerns turned to telemedicine, leading to a rise in patient satisfaction.¹⁸ Our study focuses on how hospitals report social needs, specifically through the integration SDOH into their EHR systems. This integration enables healthcare providers to identify and address patients’ individual social needs in real time.

While ample research has explored the impact of SDOH on health outcomes, there remains a gap in understanding current trends in hospital’s recording of health-related social needs. This study aims to assess the relationship between hospital characteristics, contextual county-level determinants, and the systematic recording of health-related social needs among general and surgical acute care hospitals in the U.S. Specifically, this study focuses on the routine collection of data on patients’ health-related social needs, including transportation, housing, food insecurity, and other pertinent determinants. Our research wishes to assess how hospital characteristics and county-level determinants are related to the systematic collection and recording of health-related social needs among hospitals in the U.S.

Methods

Data Sources

In this study, a cross-sectional retrospective study design was utilized. The primary data sources utilized were the most recent Annual Survey and Health Information supplement from the American Hospital Association (AHA) for the year 2022. These datasets, serving as a comprehensive repository of hospital-related information, were instrumental in understanding and analyzing various hospital characteristics, structures, processes, and outcomes. The merging process was executed with a hospital’s Medicare certification number. To augment the hospital-specific data, the study also incorporated the 2022 Area Health Resource Files, which provided contextual county-level information. Integration of these files was facilitated by employing a county’s Federal Information Processing System (FIPS). The linkage between hospital data and county-level contextual factors was crucial for exploring the intricate relationship between hospital characteristics, contextual determinants at the county level, and the systematic documentation of health-related social needs. The final sample consisted of a total of 2254 general and surgical acute care hospitals across all 50 states and the District of Columbia.

Measures

To investigate the relationship between hospital characteristics, contextual county determinants, and the methodical documentation of health-related social needs, the study employed hospitals’ categorical responses to the question: “Does your hospital routinely collect data on individual patients' health-related social needs (often referred to as social determinants of health) such as transportation, housing, food insecurity or other?” The outcome variable was based on hospitals choosing from 3 response options: “Yes, routinely,” “Yes, but not routinely,” or “No.” This nuanced exploration aimed to unravel the depth of hospitals’ engagement in systematically recording critical information pertaining to patients’ SDOH.

Hospital characteristics included in this analysis were: hospital ownership, teaching status, system membership, Medicare and Medicaid patient percentages, and total number of staffed beds. Hospital ownership was categorized as government (including both federal entities such and non-federal entities), for-profit (investor-owned facilities operating as taxable business entities), or not-for-profit (private hospitals with IRS 501(c)(3) tax-exempt status that reinvest financial surplus into operations and provide community benefits).¹⁹ Teaching status was classified as either teaching (facilities with approved residency programs, Council of Teaching Hospitals designation, or reported medical school affiliation) or non-teaching (facilities without formal medical education programs or affiliations).²⁰ System membership was defined as either system-affiliated (hospitals belonging to multi-hospital healthcare systems with shared governance, management, or resources) or independent (standalone facilities with autonomous governance and operations). Medicare and Medicaid patient percentages were calculated by dividing the number of Medicare or Medicaid inpatient days,^21,22 respectively, by total facility inpatient days and multiplied by 100, representing the proportion of care provided to these beneficiary populations.²³ Total number of staffed beds was measured as the count of hospital beds regularly maintained for inpatient use across all units, reflecting the facility’s size and inpatient capacity.²⁴

County characteristics explored in this analysis were market competition, median household income, population density per square mile, number of persons in poverty,²⁵ and percent urban population. To understand market concentration and competition, market competition was operationalized by using the Herfindahl-Hirschman Index (HHI), whereby an HHI close to 0 indicates a purely competitive market.²⁶ Ethics approval was not required for this study, as it utilized publicly available secondary data sets and did not involve research on human subjects.

Statistical Approach

The study focused exclusively on general and surgical acute care hospitals to ensure comparability across facilities. Inclusion was limited to hospitals classified as general medical and surgical facilities according to AHA designation, with active Medicare certification numbers for proper identification and linkage, and with complete data on key study variables in the AHA survey. The sample was restricted to counties with at least 1 hospital to enable meaningful contextual comparisons. Exclusion criteria were applied to remove facilities that did not align with the study focus, including specialty hospitals (eg, psychiatric, rehabilitation, children’s, orthopedic, or cardiac hospitals), long-term care hospitals, and critical access hospitals. Additionally, hospitals within US territories were excluded from analysis. These criteria were established to create a homogeneous sample of comparable acute care facilities while maximizing sample size and representativeness across diverse geographic regions.

A power calculation was conducted to ensure sufficient sample size in the mixed effect multinomial logistic regression analysis to determine the difference in social need responses across the different hospital and county characteristics. For a two-tailed test with alpha = 0.05, the sample of 2254 hospitals (representing 37.6% of the total hospitals) provided statistical power exceeding 99.9% for detecting small effects (Cohen’s d = 0.2) in 2-sample comparisons. Similarly, for correlation analyses, the sample size yielded power greater than 99.9% for detecting even small correlations (r = .1). For analyses involving group comparisons (ANOVA), the power to detect small effect sizes (f = 0.1) also exceeded 99.9%.

Means and standard deviations were used to summarize continuous merged data, while frequencies and precents were used to summarize categorical data. To check for multicollinearity, variance inflation factors and variance-covariance matrix collection assessment were completed and yielded low values, indicating no multicollinearity. Three Mixed effects multinomial logistic regression model was developed to calculate the relative risk ratio (RRR) for the nominal health-related social needs variable to account for hospital nesting within counties (hospital characteristics model, county characteristics model, and a full model). This approach allowed for the assessment of the probability of more than 2 categorical outcomes that lack inherent rank or order, ensuring a robust statistical analysis. The sample of 2254 was determined to be appropriate based on power analysis calculations. With this sample size, the margin of error at the 95% confidence level is ±1.63%, which exceeds conventional research standards (typically ±5%). This level of precision ensures that population parameters can be estimated with high confidence. Statistical significance was assessed at a P-value < .05. All data were analyzed Stata, version 17 MP.

Results

Descriptive Summary

Table 1 provides information on the routine collection of data on individual patients’ health-related social needs in hospitals, categorized by ownership, teaching status, and whether the hospital is part of a system. The data is presented in terms of frequency and percentage for each category. In terms of ownership, not-for-profit hospitals have the highest proportion of hospitals routinely collecting data on patients’ health-related social needs, with 71.22% of them engaging in routine collection. Government-owned hospitals follow, with 52.60% routinely collecting such data, while for-profit hospitals have the lowest proportion at 25.25%. When considering teaching status, 59.45% of non-teaching hospitals routinely collect data on patients’ health-related social needs, compared to 64.38% of teaching hospitals. Additionally, 9.82% of non-teaching hospitals do not collect social needs data at all, while 2.49% of teaching hospitals fall into this category. Regarding hospital system affiliation, 64.65% of hospitals that are part of a system routinely collect data on patients’ health-related social needs, compared to 52.86% of non-affiliated hospitals. Additionally, 3.09% of system-affiliated hospitals do not collect social needs data at all, while 16.17% of non-affiliated hospitals fall into this category.

Table 1.

Descriptive statistics of hospital and community characteristics.

	Does your hospital routinely collect data on individual patients’ health related social needs?
	No		Yes, routinely		Yes, but not routinely
	Frequency	%	Frequency	%	Frequency	%
Ownership
Government	57	14.84	202	52.60	125	32.55
For-Profit	17	5.72	75	25.25	205	69.02
Not-for-Profit	62	3.94	1121	71.22	391	24.84
Teaching status
Non-teaching	107	9.82	648	59.45	335	30.73
Teaching	29	2.49	750	64.38	386	33.13
Part of a system
No	82	16.17	268	52.86	157	30.97
Yes	54	3.09	1130	64.65	564	32.27
	Mean	SD	Mean	SD	Mean	SD
Herfindahl–Hirschman index	0.75	0.03	0.55	0.01	0.58	0.01
Medicare percentage	51.65	2.07	53.85	0.44	53.85	0.58
Medicaid percentage	19.67	1.72	20.34	0.36	18.79	0.47
Number of staffed beds	88.95	10.73	206.66	6.79	188.77	8.01
Median household income	57241.22	1042.42	67113.76	455.53	64968.94	652.57
Population density per square mile	162.37	35.46	1766.51	188.94	715.59	67.97
Number of person in poverty	25025.43	7211.85	103226.90	6542.38	80428.30	6926.98
Percent urban population	40.75	2.81	64.78	0.90	63.20	1.24

The descriptive table also includes mean and standard deviation values for various factors, such as the Herfindahl–Hirschman index, Medicare and Medicaid percentages, number of staffed beds, median household income, population density per square mile, number of persons in poverty, and percent urban population. The Herfindahl–Hirschman index, a measure of market concentration, indicates that hospitals that routinely collect data on patients’ health-related social needs have a lower mean index (0.55) compared to those that do not collect such data routinely (0.75). This suggests a more diverse market among hospitals routinely collecting this data. The Medicare and Medicaid percentages represent the proportion of patients covered by these government programs. Hospitals routinely collecting data on patients’ social needs have slightly higher mean for both Medicare (53.85) and Medicaid (20.34) compared to hospitals not routinely collecting this data (Medicare: 51.65, Medicaid: 19.67). In terms of capacity, hospitals routinely collecting data on social needs have a significantly higher mean number of staffed beds (206.66) compared to those that do not routinely collect such data (88.95). This suggests that larger hospitals may be more inclined to collect comprehensive data on social needs. Socioeconomic indicators also show differences across the groups. Hospitals routinely collecting data on social needs have higher mean values for median household income ($67,113.76), population density per square mile (1766.51), and percent urban population (64.78%) compared to hospitals that do not routinely collect such data (Income: $57,241.22, Density: 162.37, and Urban Population: 40.75%). However, the number of persons in poverty is lower on average in hospitals routinely collecting social needs data (103,226.90) compared to those that do not (25,025.43).

Hospital Characteristics With the Systematic Recording of Health-Related Social Needs

Table 2 shows the characteristics of hospitals recording health-related social needs. Not-for-profit hospitals have higher relative risk ratio (RRR) of routinely collecting social needs data (RRR = 2.02, 95% CI 1.30, 3.13), while for-profit hospitals have lower RRR compared to government hospitals (RRR = 0.37, 95% CI 0.19, 0.74). Teaching hospitals and hospitals affiliated with a healthcare system were more likely than non-teaching and independent hospitals to collect data on health-related social needs, both routinely and non-routinely. Specifically, teaching hospitals had a higher relative risk for routine collection ratio (RRR = 1.76, 95% CI: 1.05, 2.96) and for non-routine collection (RRR = 2.24, 95% CI: 1.32, 3.79). Similarly, hospitals that were part of a system showed an even greater likelihood, with an RRR of 4.42 (95% CI: 2.91, 6.73) for routine collection and 3.41 (95% CI: 2.20, 5.28) for non-routine collection.

Table 2.

Multivariate regression analysis of hospital recording of health-related social needs by hospital characteristics.

	Model 1: Hospital characteristics model
	Yes, routinely		Yes, but not routinely
	RRR	95% CI	RRR	95% CI
For-profit (Ref: Government)	0.37**	[0.19,0.74]	1.97*	[1.01,3.85]
Not-for-profit (ref: Government)	2.02**	[1.30,3.13]	1.31	[0.83,2.08]
Teaching status (ref: non-teaching)	1.76*	[1.05,2.96]	2.24**	[1.32,3.79]
Part of a system (ref: No)	4.42***	[2.91,6.73]	3.41***	[2.20,5.28]
Herfindahl–Hirschman index	0.43**	[0.23,0.79]	0.72	[0.38,1.35]
Medicare percentage	1.01	[1.00,1.03]	1.00	[0.99,1.02]
Medicaid percentage	1.01	[0.99,1.02]	1.00	[0.98,1.01]
Number of staffed beds	1.00	[1.00,1.00]	1.00	[1.00,1.00]

Abbreviations: CI, confident interval; RRR, relative risk ratio.

P < .05. **P < .01. ***P < .001.

County Determinants With the Systematic Recording of Health-Related Social Needs

Table 3 shows the characteristics of counties of hospitals that engage in systematic recording of health-related social needs. Higher median household income and population density per square mile have a higher relative risk of collecting data on social needs both routinely and non-routinely (RRR = 1.00, 95% CI 1.00, 1.00). In addition, a higher percentage of urban population has a higher relative risk of non-routine social needs data collection (RRR = 1.01, 95% CI 1.00, 1.02). However, the difference compared to their counterparts is very small.

Table 3.

Multivariate regression analysis of hospital recording of health-related social needs by county characteristics.

	Model 2: County characteristics model
	Yes, routinely		Yes, but not routinely
	RRR	95% CI	RRR	95% CI
Median household income	1.00**	[1.00,1.00]	1.00*	[1.00,1.00]
Population density per square mile	1.00**	[1.00,1.00]	1.00*	[1.00,1.00]
Number of person in poverty	1.00	[1.00,1.00]	1.00	[1.00,1.00]
Percent urban population	1.01	[1.00,1.01]	1.01**	[1.00,1.02]

Abbreviations: CI, confident interval; RRR, relative risk ratio.

P < .05. **P < .01.

Complete Model With the Systematic Recording of Health-Related Social Needs

Table 4 presents the results of the full model, incorporating the systematic recording of health-related social needs. Hospitals’ likelihood of routinely collecting health-related social needs data varied significantly by ownership type and system affiliation. Compared to government hospitals, for-profit hospitals were significantly less likely to collect social needs data routinely (RRR = 0.33, 95% CI: 0.17, 0.67), while not-for-profit hospitals were significantly more likely to do so (RRR = 1.93, 95% CI: 1.24, 3.00). Hospitals that were part of a healthcare system demonstrated a much higher likelihood of routinely collecting this data, with an RRR of 4.25 (95% CI: 2.78, 6.49). In contrast, teaching hospitals had a higher relative risk ratio (RRR = 1.59, 95% CI: 0.95, 2.69), but the difference was not statistically significant. For hospitals that collect social needs data but not routinely, ownership status did not show a significant association. However, teaching hospitals were significantly more likely than non-teaching hospitals to engage in non-routine data collection (RRR = 2.07, 95% CI: 1.22, 3.52). Similarly, hospitals affiliated with a healthcare system had a significantly higher likelihood of non-routine data collection (RRR = 3.38, 95% CI: 2.17, 5.26).

Table 4.

Multivariate regression analysis of hospital recording of health-related social needs by hospital and county characteristics.

	Model 3: Complete model
	Yes, routinely		Yes, but not routinely
	RRR	95% CI	RRR	95% CI
For-profit (ref: Government)	0.33**	[0.17,0.67]	1.7	[0.86,3.37]
Not-for-profit (ref: Government)	1.93**	[1.24,3.00]	1.21	[0.76,1.92]
Teaching status (ref: non-teaching)	1.59	[0.95,2.69]	2.07**	[1.22,3.52]
Part of a system (ref: No)	4.25***	[2.78,6.49]	3.38***	[2.17,5.26]
Herfindahl–Hirschman index	0.77	[0.39,1.50]	1.02	[0.51,2.04]
Medicare percentage	1.01	[1.00,1.02]	1.01	[0.99,1.02]
Medicaid percentage	1.01	[0.99,1.02]	1.00	[0.98,1.02]
Number of staffed beds	1.00	[1.00,1.00]	1.00	[1.00,1.00]
Median household income	1.00**	[1.00,1.00]	1.00**	[1.00,1.00]
Population density per square mile	1.00	[1.00,1.00]	1.00	[1.00,1.00]
Number of person in poverty	1.00	[1.00,1.00]	1.00	[1.00,1.00]
Percent urban population	1.00	[0.99,1.01]	1.00	[0.99,1.01]

Abbreviations: CI, confident interval; RRR, relative risk ratio.

**P < .01. ***P < .001. RRR: Relative risk ratio; CI: Confident interval.

Among county-level characteristics, hospitals in counties with higher household income were slightly more likely to collect social needs data, but the effect size was small (RRR = 1.00, 95% CI: 1.00, 1.00). For every one-point increase in HHI, the relative risk of the hospital routinely collecting data on social needs decreases by 23%. Other county determinants don’t significantly impact the likelihood of data collection (routinely and non-routinely) on social needs, with the inclusion of all the hospital characteristics in the multivariate model.

Discussion

Collecting and organizing patient-reported SDOH data at the hospital level plays a crucial role in addressing patients’ needs, promoting actionable community interventions, enhancing patient health, and ultimately contributing to overall population health. Hospitals, as anchor institutions, are well-positioned to leverage health-related social needs data to support patients during care transitions and establish partnerships with social service organizations.²⁷ This study aims to investigate the latest trends in SDOH data collection among hospitals. Specifically, it examines the extent to which hospitals engage in routine social needs data collection. By understanding how hospitals collect and utilize SDOH data, we can enhance patient outcomes, foster collaboration, and contribute to a healthier community.

This study found that hospital characteristics significantly influence the routine and non-routine recording of social needs data, surpassing the impact of county-level determinants. Notably, not-for-profit hospitals, teaching hospitals, and hospitals within a system membership are more likely to report patients’ health-related social needs compared to their counterparts. The marked decrease in the likelihood of for-profit hospitals routinely collecting social needs data, contrasted with the notable increase seen in not-for-profit hospitals, is consistent with the latest literature.²⁸ This highlights the critical role of a hospital’s profit status in shaping its data recording practices. This finding is aligned with the mission and requirements of not-for-profit hospitals to conduct Community Health Needs Assessments, make them publicly accessible, and provide community benefit activities to maintain their tax-exempt status.^29-32 On the other hand, for-profit hospitals had significantly lower involvement in the routine collection of patients’ health-related social needs data. Consistent with the existing literature, these hospitals appear less inclined to report such data, prioritizing its use for clinical decision rather than population health analytics, community needs assessment, or other equity-focused initiatives.^33,34 However, this stands in contrast to prior studies suggesting that for-profit hospitals have increasingly focused on community engagement, population health, and health equity beyond financial profitability, based on self-reported insights from for-profit hospital leaders.³⁵ These disparities point to the pivotal influence of financial structures and incentives in shaping hospitals’ approaches to documenting social needs.

In addition, the present study reveals an interesting association between teaching hospitals and their greater likelihood of collecting social needs data, though not necessarily on a routine basis. One plausible explanation is that most teaching hospitals are not-for-profit hospitals, they may have shared characteristics and motives, that aligns with their mission to address broader societal issues. The recent study found that teaching hospitals tend to use SDOH data primarily for referrals to social service organizations rather than for direct clinical decision making. This pattern of usage aligns with trends observed in not-for-profit hospitals, suggesting a shared emphasis on leveraging SDOH data to connect patients with essential support services.³⁶ For instance, Henry Ford Health System provides healthcare services in Detroit, Michigan, which has the highest percentage of poverty in any major U.S. city. A cornerstone of their strategy is to address disparities in the healthcare system by utilizing SDOH data to drive initiatives that can address specific issues, such as partnerships with other institutions to improve local neighborhoods and safer housing in the area.³⁷

This study also highlights the impact of healthcare system membership on the routine or non-routine collection of health-related social needs data. Hospitals that belong to larger systems could have more resources, including staff or teams that are solely able to collect data internally and access to expertise to support their data collection efforts, such as social service organizations and health information exchange organizations.^38,39 In addition, hospitals integrated into larger systems may appear more predisposed to systematic data recording, emphasizing the importance of the organizational context.

The strong association between hospital types and elevated relative risk of collecting social needs data underscores the pivotal role of inherent hospital characteristics in shaping data recording practices. These patterns remain consistent across various hospital types, reinforcing the notion that factors intrinsic to the hospital significantly influence data collection efforts.

Limitations

While this study provides valuable insights, several limitations must be acknowledged. First, the cross-sectional design restricts the ability to establish causality between hospital and community characteristics and the recording of health-related social needs. Future longitudinal studies could offer a more comprehensive understanding of these relationships. Second, there could be omitted factors influencing hospital’s collection of patients’ health-related social needs. Future research could benefit from examining more confounding variables, such as participation in value-based care, designation as a critical access hospital, other population-level socioeconomic factors, including unemployment rates. Third, the survey data on recording health-related social needs is based on self-reported responses. These responses reflect individual perspectives from within hospitals rather than collective input from hospital administrators, which limits the generalizability of findings to hospitals across the United States. Future research could enhance reliability by triangulating self-reports with objective measures. Moreover, this study operationalized the main outcome variables by identifying the presence of social needs data but did not assess its quality, accuracy, or collection methods. Future investigations should delve into these aspects to deepen our understanding and improve data utility. Further research should also evaluate the impact of health-related social needs data on patient health outcomes and community well-being, which is critical for informed decision-making. Finally, it is important to recognize that the 2022 data collection occurred during a period of ongoing adaptation within the healthcare system due to the COVID-19 pandemic. Although the acute crisis phase had largely subsided, the pandemic likely continued to influence healthcare priorities, resource allocation, and attention to social determinants of health. While the heightened visibility of health disparities may have increased awareness of social needs documentation among certain hospital types, pandemic-related financial constraints and staffing shortages may have hindered other facilities’ capacity to implement or sustain robust social needs data collection systems.

Policy Implications

The importance of collecting and assessing social needs continues to grow. However, as highlighted by this study, significant gaps exist in how data are collected and by which organizations. In turn, this creates a large amount of variance in actionable data collected and limits our collective understanding of the impact of social needs on patient outcomes.^40,41 Specifically, the lack of a systemically approach to data collection hampers our ability to define associations between social needs and quality metrics and prevents large-scale evaluation of actions that can be taken to improve care or proactively intervene to prevent adverse outcomes.^42-44 This sentiment has been identified previously in the literature and across multiple medical and surgical specialties.^45-47 These associations support more specific policy intervention focused at the collection and utilization of SDOH for patient care activities.

Additionally, previous research underscores the impact of SDOH at the county level on hospital-level outcomes, which could lead to an over or under-evaluation of quality, such as those associated with value-based purchasing or hospital-acquired conditions.^48-50 When considering the results of the current work as well as the findings of previous studies, the inclusion of SDOH in the evaluation of healthcare quality has the potential to improve outcomes and promote health equity through the provision of more tailored care delivery.^2,4-8,15 As such there is an opportunity for policy development focused at more systematic and consistent SDOH recording and use.⁵¹ Notably, there has been some influence on SDOH collection based on community benefit policies and organizational community focus as identified by more routine collection in not-for-profit and government (non-federal) facilities.⁴⁷ Leveraging existing policies to incorporate SDOH and greater standardization of the SDOH measures would allow for better comparison of potential differences between organizational populations served, care provided, and outcomes achieved.⁴⁴ Specifically, the expansion of policies to encourage SDOH collection within for-profit systems and the inclusion of SDOH in quality metric adjustment is warranted and supported by other works. A recent policy shift from voluntary participation to mandatory hospital engagement in patient health-related social needs screening marks an important step forward.⁵²

Conclusion

There is a growing body of evidence in support of the significant relationship between addressing SDOH in healthcare settings and improved patient health outcomes. Particularly, the systematic collection of SDOH is posed to play increasingly pivotal role within the pay-for-performance healthcare reimbursement model. As the healthcare landscape transitions away from traditional fee-for-service models, it has become imperative to consider the potential social determinants that may adversely impact the vulnerable populations. Our study has identified key hospital and county characteristics that significantly influence the hospital reporting of patient social needs data, highlighting the need for standardized guidance and robust framework to support hospitals in effectively documenting and leveraging health-related social needs information. By doing so, healthcare providers can enhance care delivery, address disparities, and contribute to improved health outcomes. Strengthening policies and standardizing SDOH collection will ultimately empower healthcare systems to integrate social context into care.

Footnotes

Acknowledgements

The authors gratefully acknowledge the support provided by the University of North Florida Faculty Publishing Grant.

ORCID iD

Sinyoung Park

Author Contributions

S.Park assisted in conceiving and designing the work, conducting the literature search, writing part of the manuscript, and critically revising the manuscript for important intellectual content. H.Hamadi assisted in conceiving and designing the work, analyzing and interpreting the data, writing part of the manuscript, and critically revising the manuscript. S.Abdul assisted in assisted in drafting part of the manuscript and literature search. A.Spaulding assisted in writing part of the manuscript and critical revision of manuscript for important intellectual content. J.Xu assisted in writing part of the manuscript and critical revision of manuscript for important intellectual content. M. Zhao assisted in critical revision of manuscript for important intellectual content.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

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Exploring the Routine Recording of Health-Related Social Needs in U.S. Acute Care Hospitals

Abstract

Background:

Objectives:

Design:

Methods:

Results:

Conclusion:

Keywords

Introduction

Methods

Data Sources

Measures

Statistical Approach

Results

Descriptive Summary

Hospital Characteristics With the Systematic Recording of Health-Related Social Needs

County Determinants With the Systematic Recording of Health-Related Social Needs

Complete Model With the Systematic Recording of Health-Related Social Needs

Discussion

Limitations

Policy Implications

Conclusion

Footnotes

Acknowledgements

ORCID iD

Author Contributions

Funding

Declaration of Conflicting Interests

References