Integrating Social And Medical Data To Improve Population Health: Opportunities And Barriers

Revolutionizing urogynecology: Machine learning application with patient-centric technology: Promise, challenges, and future directions

In an epoch where digital innovation is redefining the medical landscape, electronic health records (EHRs) stand out as a pivotal transformative force. Urogynecology, a discipline anchored in intricate patient histories and meticulous follow-ups, is on the brink of profound transformation due to these digital strides. While EHRs have unified patient data, challenges related to data privacy, interoperability, and access persist. In response, we present Pelvic Health Place (PHPlace) - a multilingual, patient-centric application. Purposefully designed to bolster patient engagement, PHPlace provides clinicians with essential pre-consultation insights, streamlines the consent process, vividly delineates surgical pathways, and assures comprehensive long-term monitoring. This platform also establishes a foundation for global data amalgamation, promising to invigorate research and potentially harness artificial intelligence (AI) capabilities. With AI integration, we anticipate a more tailored treatment approach and enriched patient education, signaling a pivotal shift in urogynecology and emphasizing the imperative for ongoing academic inquiry.

Social Determinants of Health Phenotypes and Cardiometabolic Condition Prevalence Among Patients in a Large Academic Health System: Latent Class Analysis

Background

Adverse social determinants of health (SDoH) have been associated with cardiometabolic disease; however, disparities in cardiometabolic outcomes are rarely the result of a single risk factor.

Objective

This study aimed to identify and characterize SDoH phenotypes based on patient-reported and neighborhood-level data from the institutional electronic medical record and evaluate the prevalence of diabetes, obesity, and other cardiometabolic diseases by phenotype status.

Methods

Patient-reported SDoH were collected (January to December 2020) and neighborhood-level social vulnerability, neighborhood socioeconomic status, and rurality were linked via census tract to geocoded patient addresses. Diabetes status was coded in the electronic medical record using International Classification of Diseases codes; obesity was defined using measured BMI ≥30 kg/m2. Latent class analysis was used to identify clusters of SDoH (eg, phenotypes); we then examined differences in the prevalence of cardiometabolic conditions based on phenotype status using prevalence ratios (PRs).

Results

Complete data were available for analysis for 2380 patients (mean age 53, SD 16 years; n=1405, 59% female; n=1198, 50% non-White). Roughly 8% (n=179) reported housing insecurity, 30% (n=710) reported resource needs (food, health care, or utilities), and 49% (n=1158) lived in a high-vulnerability census tract. We identified 3 patient SDoH phenotypes: (1) high social risk, defined largely by self-reported SDoH (n=217, 9%); (2) adverse neighborhood SDoH (n=1353, 56%), defined largely by adverse neighborhood-level measures; and (3) low social risk (n=810, 34%), defined as low individual- and neighborhood-level risks. Patients with an adverse neighborhood SDoH phenotype had higher prevalence of diagnosed type 2 diabetes (PR 1.19, 95% CI 1.06-1.33), hypertension (PR 1.14, 95% CI 1.02-1.27), peripheral vascular disease (PR 1.46, 95% CI 1.09-1.97), and heart failure (PR 1.46, 95% CI 1.20-1.79).

Conclusions

Patients with the adverse neighborhood SDoH phenotype had higher prevalence of poor cardiometabolic conditions compared to phenotypes determined by individual-level characteristics, suggesting that neighborhood environment plays a role, even if individual measures of socioeconomic status are not suboptimal.

Social Needs Screening in Academic Health Systems: A Landscape Assessment

Screening for social needs has gained traction as an approach to addressing social determinants of health, but it faces challenges regarding standardization, resource allocation, and follow-up care. The year-long study, conducted by the Association of American Medical Colleges, integrated data from conferences, surveys, and key informant interviews to examine the integration of social needs screening into health care services within Academic Health Systems (AHS). The authors' analysis unveiled eight key themes, showcasing AHS's active involvement in targeted social needs screening alongside persistent resource allocation obstacles. AHS are dedicated to efficiently identifying high-risk populations, fostering partnerships with community-based organizations, and embracing technology for closed-loop referrals. However, concerns endure about the utilization of reimbursement codes for social needs and regulatory compliance. AHS confront staffing issues, resource allocation intricacies, and the imperative for seamless integration across clinical and nonclinical departments. Notably, opportunities arise in standardized training, alignment of AHS priorities, exploration of social investment models, and engagement with state-level health information exchanges. Aligning clinical care, research pursuits, and community engagement endeavors holds promise for AHS in effectively addressing social needs.

Screening for social determinants of health among populations at risk for MASLD: a scoping review

Background

Social determinants of health (SDoH) have been associated with disparate outcomes among those with metabolic dysfunction-associated steatotic liver disease (MASLD) and its risk factors. To address SDoH among this population, real-time SDoH screening in clinical settings is required, yet optimal screening methods are unclear. We performed a scoping review to describe the current literature on SDoH screening conducted in the clinical setting among individuals with MASLD and MASLD risk factors.

Methods

Through a systematic literature search of MEDLINE, Embase, and CINAHL Complete databases through 7/2023, we identified studies with clinic-based SDoH screening among individuals with or at risk for MASLD that reported pertinent clinical outcomes including change in MASLD risk factors like diabetes and hypertension.

Results

Ten studies (8 manuscripts, 2 abstracts) met inclusion criteria involving 148,151 patients: 89,408 with diabetes and 25,539 with hypertension. Screening was primarily completed in primary care clinics, and a variety of screening tools were used. The most commonly collected SDoH were financial stability, healthcare access, food insecurity and transportation. Associations between clinical outcomes and SDoH varied; overall, higher SDoH burden was associated with poorer outcomes including elevated blood pressure and hemoglobin A1c.

Conclusion

Despite numerous epidemiologic studies showing associations between clinical outcomes and SDoH, and guidelines recommending SDoH screening, few studies describe in-clinic SDoH screening among individuals with MASLD risk factors and none among patients with MASLD. Future research should prioritize real-time, comprehensive assessments of SDoH, particularly among patients at risk for and with MASLD, to mitigate disease progression and reduce MASLD health disparities.

Technology, data, people, and partnerships in addressing unmet social needs within Medicaid Managed Care

BACKGROUND

Individuals with unmet social needs experience adverse health outcomes and are subject to greater inequities in health and social outcomes. Given the high prevalence of unmet needs among Medicaid enrollees, many Medicaid managed care organizations (MCOs) are now screening enrollees for unmet social needs and connecting them to community-based organizations (CBOs) with knowledge and resources to address identified needs. The use of screening and referral technology and data sharing are often considered key components in programs integrating health and social services. Despite this emphasis on technology and data collection, research suggests substantial barriers exist in operationalizing effective systems.

METHODS

We used qualitative methods to examine cross-sector perspectives on the use of data and technology to facilitate MCO and CBO partnerships in Kentucky, a state with high Medicaid enrollment, to address enrollee social needs. We recruited participants through targeted sampling, and conducted 46 in-depth interviews with 26 representatives from all six Kentucky MCOs and 20 CBO leaders. Qualitative descriptive analysis, an inductive approach, was used to identify salient themes.

RESULTS

We found that MCOs and CBOs have differing levels of need for data, varying incentives for collecting and sharing data, and differing valuations of what data can or should do. Four themes emerged from interviewees' descriptions of how they use data, including 1) to screen for patient needs, 2) to case manage, 3) to evaluate the effectiveness of programs, and 4) to partner with each other. Underlying these data use themes were areas of alignment between MCOs/CBOs, areas of incongruence, and areas of tension (both practical and ideological). The inability to interface with community partners for data privacy and ownership concerns contributes to division. Our findings suggest a disconnect between MCOs and CBOs regarding terms of their technology interfacing despite their shared mission of meeting the unmet social needs of enrollees.

CONCLUSIONS

While data and technology can be used to identify enrollee needs and determine the most critical need, it is not sufficient in resolving challenges. People and relationships across sectors are vital in connecting enrollees with the community resources to resolve unmet needs.

Diagnosing social ills: Theorising social determinants of health as a diagnostic category

Medicine, as an institution and discipline, has embraced social determinants of health as a key influence on clinical practice and care. Beyond simply acknowledging their importance, most recent versions of the International Classification of Diseases explicitly codify social determinants as a viable diagnostic category. This diagnostic shift is noteworthy in the United States, where 'Z-codes' were introduced to facilitate the documentation of illiteracy, unemployment, poverty and other social factors impacting health. Z-codes hold promise in addressing patients' social needs, but there are likely consequences to medicalising social determinants. In turn, this article provides a critical appraisal of Z-codes, focussing on the role of diagnoses as both constructive and counterproductive sources of legitimacy, knowledge and responsibility in our collective understanding of health. Diagnosis codes for social determinants are powerful bureaucratic tools for framing and responding to psychosocial risks commensurate with biophysiological symptoms; however, they potentially reinforce beliefs about the centrality of individuals for addressing poor health at the population level. I contend that Z-codes demonstrate the limited capacity of diagnoses to capture the complex individual and social aetiology of health, and that sociology benefits from looking further 'upstream' to identify the structural forces constraining the scope and utility of diagnoses.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

Analyzing Multiple Social Determinants of Health Using Different Clustering Methods

Social determinants of health (SDoH) have become an increasingly important area to acknowledge and address in healthcare; however, dealing with these measures in outcomes research can be challenging due to the inherent collinearity of these factors. Here we discuss our experience utilizing three statistical methods-exploratory factor analysis (FA), hierarchical clustering, and latent class analysis (LCA)-to analyze data collected using an electronic medical record social risk screener called Protocol for Responding to and Assessing Patient Assets, Risks, and Experience (PRAPARE). The PRAPARE tool is a standardized instrument designed to collect patient-reported data on SDoH factors, such as income, education, housing, and access to care. A total of 2380 patients had complete PRAPARE and neighborhood-level data for analysis. We identified a total of three composite SDoH clusters using FA, along with four clusters identified through hierarchical clustering, and four latent classes of patients using LCA. Our results highlight how different approaches can be used to handle SDoH, as well as how to select a method based on the intended outcome of the researcher. Additionally, our study shows the usefulness of employing multiple statistical methods to analyze complex SDoH gathered using social risk screeners such as the PRAPARE tool.

Social workers are key to addressing social determinants of health in integrated care settings

Social workers play an important role in assessing social determinants of health (SDH) and providing behavioral health services in integrated care settings. Evidence suggests that integrated care interventions improve quality of life and other patient outcomes. However, the ambiguous role of social workers on the interdisciplinary team, the lack of protocol in SDH screening and intervention, and restrictions due to healthcare reimbursement limit social workers' ability to intervene. Future directions include standardizing integrated care models, evaluating integrated care's efficacy to address SDH, incorporating SDH into interprofessional training including role clarification and reimbursing for SDH assessment and intervention.

Leveraging Student Volunteers to Connect Patients with Social Risk to Resources On a Coordinated Care Platform: A Case Study with Two Endocrinology Clinics

Introduction

Although unmet social needs can impact health outcomes, health systems often lack the capacity to fully address these needs. Our study describes a model that organized student volunteers as a community-based organisation (CBO) to serve as a social referral hub on a coordinated social care platform, NCCARE360.

Description

Patients at two endocrinology clinics were systematically screened for social needs. Patients who screened positive and agreed to receive help were referred via NCCARE360 to student 'Help Desk' volunteers, who organised as a CBO. Trained student volunteers called patients to place referrals to resources and document them on the platform. The platform includes documentation at several levels, acting as a shared information source between healthcare providers, volunteer student patient navigators, and community resources. Navigators followed up with patients to problem-solve barriers and track referral outcomes on the platform, visible to all parties working with the patient.

Discussion

Of the 44 patients who screened positive for social needs and were given referrals by Help Desk, 41 (93%) were reached for follow-up. Thirty-six patients (82%) connected to at least one resource. These results speak to the feasibility and utility of organising undergraduate student volunteers into a social referral hub to connect patients to resources on a coordinated care platform.

Conclusion

Organising students as a CBO on a centralized social care platform can help bridge a critical gap between healthcare and social services, addressing health system capacity and ultimately improving patients' connections with resources.

A Systematic Review of Electronic Community Resource Referral Systems

INTRODUCTION

Community Resource Referral Systems delivered electronically through healthcare information technology systems (e.g., electronic medical records) have become more common in efforts to address patients' unmet health-related social needs. Community Resource Referral System connects patients with social supports such as food assistance, utility support, transportation, and housing. This systematic review identifies barriers and facilitators that influence the Community Resource Referral System's implementation in the U.S. by identifying and synthesizing peer-reviewed literature over a 15-year period.

METHODS

This systematic review was conducted following PRISMA guidelines. A search was conducted on five scientific databases to capture the literature published between January 2005 and December 2020. Data analysis was conducted from August 2021 to July 2022.

RESULTS

This review includes 41 articles of the 2,473 initial search results. Included literature revealed that Community Resource Referral Systems functioned to address a variety of health-related social needs and were delivered in different ways. Integrating the Community Resource Referral Systems into clinic workflows, maintenance of community-based organization inventories, and strong partnerships between clinics and community-based organizations facilitated implementation. The sensitivity of health-related social needs, technical challenges, and associated costs presented as barriers. Overall, electronic medical records-integration and automation of the referral process was reported as advantageous for the stakeholders.

DISCUSSION

This review provides information and guidance for healthcare administrators, clinicians, and researchers designing or implementing electronic Community Resource Referral Systems in the U.S. Future studies would benefit from stronger implementation science methodological approaches. Sustainable funding mechanisms for community-based organizations, clear stipulations regarding how healthcare funds can be spent on health-related social needs, and innovative governance structures that facilitate collaboration between clinics and community-based organizations are needed to promote the growth and sustainability of Community Resource Referral Systems in the U.S.

Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system

BACKGROUND

Mental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes.

METHODS

Since diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014-2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions.

RESULTS

Substantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs.

CONCLUSIONS

The current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.

Social Risk Screening and Response Equity: Assessment by Race, Ethnicity, and Language in Community Health Centers

INTRODUCTION

Little has previously been reported about the implementation of social risk screening across racial/ethnic/language groups. To address this knowledge gap, the associations between race/ethnicity/language, social risk screening, and patient-reported social risks were examined among adult patients at community health centers.

METHODS

Patient- and encounter-level data from 2016 to 2020 from 651 community health centers in 21 U.S. states were used; data were extracted from a shared Epic electronic health record and analyzed between December 2020 and February 2022. In adjusted logistic regression analyses stratified by language, robust sandwich variance SE estimators were applied with clustering on patient's primary care facility.

RESULTS

Social risk screening occurred at 30% of health centers; 11% of eligible adult patients were screened. Screening and reported needs varied significantly by race/ethnicity/language. Black Hispanic and Black non-Hispanic patients were approximately twice as likely to be screened, and Hispanic White patients were 28% less likely to be screened than non-Hispanic White patients. Hispanic Black patients were 87% less likely to report social risks than non-Hispanic White patients. Among patients who preferred a language other than English or Spanish, Black Hispanic patients were 90% less likely to report social needs than non-Hispanic White patients.

CONCLUSIONS

Social risk screening documentation and patient reports of social risks differed by race/ethnicity/language in community health centers. Although social care initiatives are intended to promote health equity, inequitable screening practices could inadvertently undermine this goal. Future implementation research should explore strategies for equitable screening and related interventions.

"Addressing the bigger picture": A qualitative study of internal medicine patients' perspectives on social needs data collection and use

BACKGROUND

There is increasing interest in collecting sociodemographic and social needs data in hospital settings to inform patient care and health equity. However, few studies have examined inpatients' views on this data collection and what should be done to address social needs. This study describes internal medicine inpatients' perspectives on the collection and use of sociodemographic and social needs information.

METHODS

A qualitative interpretive description methodology was used. Semi-structured interviews were conducted with 18 patients admitted to a large academic hospital in Toronto, Canada. Participants were recruited using maximum variation sampling for diverse genders, races, and those with and without social needs. Interviews were coded using a predominantly inductive approach and a thematic analysis was conducted.

RESULTS

Patients expressed that sociodemographic and social needs data collection is important to offer actionable solutions to address their needs. Patients described a gap between their ideal care which would attend to social needs, versus the reality that hospital-based teams are faced with competing priorities and pressures that make it unfeasible to provide such care. They also believed that this data collection could facilitate more holistic, integrated care. Patients conveyed a need to have a trusting and transparent relationship with their provider to alleviate concerns surrounding bias, discrimination, and confidentiality. Lastly, they indicated that sociodemographic and social needs data could be useful to inform care, support research to inspire social change, and assist them with navigating community resources or creating in-hospital programs to address unmet social needs.

CONCLUSIONS

While the collection of sociodemographic and social needs information in hospital settings is generally acceptable, there were varied views on whether hospital staff should intervene, as their priority is medical care. The results can inform the implementation of social data collection and interventions in hospital settings.

Social Determinants of Health-Related Z Codes and Health Care Among Patients With Hypertension

Introduction

Tracking social needs can provide information on barriers to controlling hypertension and the need for wraparound services. No recent studies have examined ICD-10-CM social determinants of health-related Z codes (Z55-Z65) to indicate social needs with a focus on patients with hypertension.

Methods

Three cohorts were identified with a diagnosis of hypertension during 2016-2017 and continuously enrolled in fee-for-service insurance through June 2021: (1) commercial, age 18-64 years (n=1,024,012); (2) private insurance to supplement Medicare (Medicare Supplement), age ≥65 years (n=296,340); and (3) Medicaid, age ≥18 years (n=146,484). Both the proportion of patients and healthcare encounters or visits with social determinants of health-related Z code were summarized annually. Patient and visit characteristics were summarized for 2019.

Results

In 2020, the highest annual documentation of social determinants of health-related Z codes was among Medicaid beneficiaries (3.02%, 0.46% commercial, 0.42% Medicare Supplement); documentation was higher among inpatient than among outpatient visits for all insurance types. Z63 (related to primary support group) was more common among commercial and Medicare Supplement beneficiaries, and Z59 (housing and economic circumstances) was more common among Medicaid beneficiaries. The 2019 total unadjusted medical expenditures were 1.85, 1.78, and 1.61 times higher for those with social determinants of health-related Z code than for those without commercial, Medicare Supplement, and Medicaid, respectively. Patients with social determinants of health-related Z code also had higher proportions of diagnosed chronic conditions. Among Medicaid beneficiaries, differences in the presence of social determinants of health-related Z code by race or ethnicity were observed.

Conclusions

Although currently underreported, social determinants of health-related Z codes provide an opportunity to integrate social and medical data and may help decision makers understand the need for additional services among individuals with hypertension.

Predicting Social Determinants of Health in Patient Navigation: Case Study

BACKGROUND

Patient navigation (PN) programs have demonstrated efficacy in improving health outcomes for marginalized populations across a range of clinical contexts by addressing barriers to health care, including social determinants of health (SDoHs). However, it can be challenging for navigators to identify SDoHs by asking patients directly because of many factors, including patients' reluctance to disclose information, communication barriers, and the variable resources and experience levels of patient navigators. Navigators could benefit from strategies that augment their ability to gather SDoH data. Machine learning can be leveraged as one of these strategies to identify SDoH-related barriers. This could further improve health outcomes, particularly in underserved populations.

OBJECTIVE

In this formative study, we explored novel machine learning-based approaches to predict SDoHs in 2 Chicago area PN studies. In the first approach, we applied machine learning to data that include comments and interaction details between patients and navigators, whereas the second approach augmented patients' demographic information. This paper presents the results of these experiments and provides recommendations for data collection and the application of machine learning techniques more generally to the problem of predicting SDoHs.

METHODS

We conducted 2 experiments to explore the feasibility of using machine learning to predict patients' SDoHs using data collected from PN research. The machine learning algorithms were trained on data collected from 2 Chicago area PN studies. In the first experiment, we compared several machine learning algorithms (logistic regression, random forest, support vector machine, artificial neural network, and Gaussian naive Bayes) to predict SDoHs from both patient demographics and navigator's encounter data over time. In the second experiment, we used multiclass classification with augmented information, such as transportation time to a hospital, to predict multiple SDoHs for each patient.

RESULTS

In the first experiment, the random forest classifier achieved the highest accuracy among the classifiers tested. The overall accuracy to predict SDoHs was 71.3%. In the second experiment, multiclass classification effectively predicted a few patients' SDoHs based purely on demographic and augmented data. The best accuracy of these predictions overall was 73%. However, both experiments yielded high variability in individual SDoH predictions and correlations that become salient among SDoHs.

CONCLUSIONS

To our knowledge, this study is the first approach to applying PN encounter data and multiclass learning algorithms to predict SDoHs. The experiments discussed yielded valuable lessons, including the awareness of model limitations and bias, planning for standardization of data sources and measurement, and the need to identify and anticipate the intersectionality and clustering of SDoHs. Although our focus was on predicting patients' SDoHs, machine learning can have a broad range of applications in the field of PN, from tailoring intervention delivery (eg, supporting PN decision-making) to informing resource allocation for measurement, and PN supervision.

The adoption of social determinants of health documentation in clinical settings

OBJECTIVE

To understand the frequency of social determinants of health (SDOH) diagnosis codes (Z-codes) within the electronic health record (EHR) for patients with prediabetes and diabetes and examine factors influencing the adoption of SDOH documentation in clinical care.

DATA SOURCES

EHR data and qualitative interviews with health care providers and stakeholders.

STUDY DESIGN

An explanatory sequential mixed methods design first examined the use of Z-codes within the EHR and qualitatively examined barriers to documenting SDOH. Data were integrated and interpreted using a joint display. This research was informed by the Framework for Dissemination and Utilization of Research for Health Care Policy and Practice.

DATA COLLECTION/EXTRACTION METHODS

We queried EHR data for patients with a hemoglobin A1c > 5.7 between October 1, 2015 and September 1, 2020 (n = 118,215) to examine the use of Z-codes and demographics and outcomes for patients with and without social needs. Semi-structured interviews were conducted with 23 participants (n = 15 health care providers; n = 7 billing and compliance stakeholders). The interview questions sought to understand how factors at the innovation-, individual-, organizational-, and environmental-level influence SDOH documentation. We used thematic analysis to analyze interview data.

PRINCIPAL FINDINGS

Patients with social needs were disproportionately older, female, Black, uninsured, living in low-income and high unemployment neighborhoods, and had a higher number of hospitalizations, obesity, prediabetes, and type 2 diabetes than those without a Z-code. Z-codes were not frequently used in the EHR (<1% of patients), and there was an overall lack of congruence between quantitative and qualitative results related to the prevalence of social needs. Providers faced barriers at multiple levels (e.g., individual-level: discomfort discussing social needs; organizational-level: limited time, competing priorities) for documenting SDOH and identified strategies to improve documentation.

CONCLUSIONS

Providers recognized the impact of SDOH on patient health and had positive perceptions of screening for and documenting social needs. Implementation strategies are needed to improve systematic documentation.

Trends and Patterns of Social History Data Collection Within an Electronic Health Record

There is increased acceptance that social and behavioral determinants of health (SBDH) impact health outcomes, but electronic health records (EHRs) are not always set up to capture the full range of SBDH variables in a systematic manner. The purpose of this study was to explore rates and trends of social history (SH) data collection-1 element of SBDH-in a structured portion of an EHR within a large academic integrated delivery system. EHR data for individuals with at least 1 visit in 2017 were included in this study. Completeness rates were calculated for how often SBDH variable was assessed and documented. Logistic regressions identified factors associated with assessment rates for each variable. A total of 44,166 study patients had at least 1 SH variable present. Tobacco use and alcohol use were the most frequently captured SH variables. Black individuals were more likely to have their alcohol use assessed (odds ratio [OR] 1.21) compared with White individuals, whereas White individuals were more likely to have their "smokeless tobacco use" assessed (OR 0.92). There were also differences between insurance types. Drug use was more likely to be assessed in the Medicaid population for individuals who were single (OR 0.95) compared with the commercial population (OR 1.05). SH variable assessment is inconsistent, which makes use of EHR data difficult to gain better understanding of the impact of SBDH on health outcomes. Standards and guidelines on how and why to collect SBDH information within the EHR are needed.

Perspective: Acknowledging a Hierarchy of Social Needs in Diabetes Clinical Care and Prevention

The evidence of suboptimal social determinants of health (SDoH) on poor health outcomes has resulted in widespread calls for research to identify ways to measure and address social needs to improve health outcomes and reduce disparities. While assessing SDoH has become increasingly important in diabetes care and prevention research, little guidance has been offered on how to address suboptimal determinants in diabetes-related clinical care, prevention efforts, medical education and research. Not surprisingly, many patients experience multiple social needs - some that are more urgent (housing) than others (transportation/resources), therefore the order in which these needs are addressed needs to be considered in the context of diabetes care/outcomes. Here we discuss how conceptualizing diabetes related health through the lens of Maslow's hierarchy of needs has potential to help prioritize individual social needs that should be addressed to improve outcomes in the context of population-level determinants in the communities where people live.

Social Risk Factor Documentation in Emergency Departments

STUDY OBJECTIVE

Social Z codes are International Classification of Diseases, Tenth Revision, Clinical Modification codes that provide one way of documenting social risk factors in electronic health records. Despite the utility and availability of these codes, no study has examined social Z code documentation prevalence in emergency department (ED) settings.

METHODS

In this descriptive, cross-sectional study of all ED visits included in the 2018 Nationwide Emergency Department Sample, we estimated the prevalence of social Z code documentation and used logistic regression to examine the association between documentation and patient and hospital characteristics.

RESULTS

Of more than 35.8 million adult and pediatric ED visits, there was a 1.21% weighted prevalence (n=452,499) of at least 1 documented social Z code. Social Z codes were significantly more likely to be documented in ED visits among patients aged 35 to 64 compared to patients aged 18 to 34 (18.6/1000 [16.9 to 20.4] versus 12.7/1000 [11.5 to 14.0], odds ratio (OR) 1.47 [1.42 to 1.53]), male patients (16.6/1000 [15.1 to 18.2] versus female 8.5/1000 [7.8 to 9.2], OR 1.97 [1.89 to 2.06]), patients with Medicaid compared to patients with private insurance (15.9/1000 [14.4 to 17.6] versus (6.6/1000 [6.0 to 7.2], OR 2.45 [1.30 to 1.63]), and patients who had a Charlson Comorbidity Index≥1 compared to those with a Charlson Comorbidity Index of 0 (ranges 15.0 to 16.6/1000 versus 10.6/1000 [9.6 to 11.7], ORs ranging 1.43 to 1.58). ED visits with a primary diagnosis of mental, behavioral, and neurodevelopmental illness had the strongest positive association with social Z code documentation (85.6/1000 [78.4 to 93.4], OR 10.75 [9.88 to 11.70]) compared to ED visits without this primary diagnosis.

CONCLUSION

We found a very low prevalence of social Z code documentation in ED visits nationwide. More systematic social Z code documentation could support targeted social interventions, social risk payment adjustments, and future policy reforms.

Real-world integration of the protocol for responding to and assessing patients' assets, risks, and experiences tool to assess social determinants of health in the electronic medical record at an academic medical center

Objective

To describe the real-world deployment of a tool, the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE), to assess social determinants of health (SDoH) in an electronic medical record (EMR).

Methods

We employed the collection of the PRAPARE tool in the EMR of a large academic health system in the ambulatory clinic and emergency department setting. After integration, we evaluated SDoH prevalence, levels of missingness, and data anomalies to inform ongoing collection. We summarized responses using descriptive statistics and hand-reviewed data text fields and patterns in the data. Data on patients who were administered with the PRAPARE from February to December 2020 were extracted from the EMR. Patients missing ≥ 12 PRAPARE questions were excluded. Social risks were screened using the PRAPARE. Information on demographics, admittance status, and health coverage were extracted from the EMR.

Results

Assessments with N = 6531 were completed (mean age 54 years, female (58.6%), 43.8% Black). Missingness ranged from 0.4% (race) to 20.8% (income). Approximately 6% of patients were homeless; 8% reported housing insecurity; 1.4% reported food needs; 14.6% had healthcare needs; 8.4% needed utility assistance; and 5% lacked transportation related to medical care. Emergency department patients reported significantly higher proportions of suboptimal SDoH.

Conclusions

Integrating the PRAPARE assessment in the EMR provides valuable information on SDoH amenable to intervention, and strategies are needed to increase accurate data collection and to improve the use of data in the clinical encounter.

Discuss and remember: Clinician strategies for integrating social determinants of health in patient records and care

There is growing interest in standardizing data about social determinants of health (SDOH) in electronic health records (EHRs), yet little is known about how clinicians document SDOH in daily practice. This study investigates clinicians' strategies for working with SDOH data and the challenges confronting SDOH standardization. Drawing on ethnographic observation, interviews with patients and clinicians, and systematic review of patient EHRs-all at an urban teaching hospital in the US Midwest-we analyze three strategies clinicians deploy to integrate SDOH data into patient care. First, clinicians document SDOH using "signal phrases," keywords and short sentences that help them recall patients' social stories. Second, clinicians use other technology or face-to-face conversations to share about patients' SDOH with colleagues. Third, clinicians fold discussion of SDOH with patients into their personal relationships. While these local strategies facilitate personalized care and help clinicians minimize their computer workload, we also consider their limitations for efforts to coordinate care across institutions and attempts to identify SDOH in EHRs. These findings reveal ongoing tensions in projects of standardization in medicine, as well as the specific difficulty of standardizing data about SDOH. They have important clinical implications as they help explain how clinicians may attend to patients' SDOH in ways that are not legible in patient records. This paper is also relevant for policy at a time when mandates to include SDOH data in health records are expanding and strategies to standardize SDOH documentation are being developed.

Association of Coded Housing Instability and Hospitalization in the US

IMPORTANCE

Housing instability and other social determinants of health are increasingly being documented by clinicians. The most common reasons for hospitalization among patients with coded housing instability, however, are not well understood.

OBJECTIVE

To compare the most common reasons for hospitalization among patients with and without coded housing instability.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional, retrospective study identified hospitalizations of patients between age 18 and 99 years using the 2017 to 2019 National Inpatient Sample. Data were analyzed from May to September 2022.

EXPOSURES

Housing instability was operationalized using 5 International Classification of Diseases, 10th Revision, Social Determinants of Health Z-Codes addressing problems related to housing: homelessness; inadequate housing; discord with neighbors, lodgers, and landlords; residential institution problems; and other related problems.

MAIN OUTCOMES AND MEASURES

The primary outcome of interest was reason for inpatient admission. Bivariate comparisons of patient characteristics, primary diagnoses, length of stay, and hospitalization costs among patients with and without coded housing instability were performed.

RESULTS

Among the 87 348 604 hospitalizations analyzed, the mean (SD) age was 58 (20) years and patients were more likely to be women (50 174 117 [57.4%]) and White (58 763 014 [67.3%]). Housing instability was coded for 945 090 hospitalizations. Hospitalized patients with housing instability, compared with those without instability, were more likely to be men (668 255 patients with coded instability [70.7%] vs 36 506 229 patients without [42.3%]; P < .001), younger (mean [SD] age 45.5 [14.0] vs 58.4 [20.2] years), Black (235 355 patients [24.9%] vs 12 929 158 patients [15.0%]), Medicaid beneficiaries (521 555 patients [55.2%] vs 15 541 175 patients [18.0%]), uninsured (117 375 patients [12.4%] vs 3 476 841 patients [4.0%]), and discharged against medical advice (28 890 patients [8.4%] vs 451 855 patients [1.6%]). The most common reason for hospitalization among patients with coded housing instability was mental, behavioral, and neurodevelopmental disorders (475 575 patients [50.3%]), which cost a total of $3.5 billion. Other common reasons included injury (69 270 patients [7.3%]) and circulatory system diseases (64 700 patients [6.8%]). Coded housing instability was also significantly associated with longer mean (SD) hospital stays (6.7 [.06] vs 4.8 [.01] days) and a cost of $9.3 billion. Hospitalized patients with housing instability had 18.6 times greater odds of having a primary diagnosis of mental, behavioral, and neurodevelopmental disorders (475 575 patients [50.3%] vs 4 470 675 patients [5.2%]; odds ratio, 18.56; 95% CI, 17.86 to 19.29).

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, hospitalizations among patients with coded housing instability had higher admission rates for mental, behavioral, and neurodevelopmental disorders, longer stays, and increased costs. Findings suggest that efforts to improve housing instability, mental and behavioral health, and inpatient hospital utilization across multiple sectors may find areas for synergistic collaboration.

Assessing Alignment of Patient and Clinician Perspectives on Community Health Resources for Chronic Disease Management

Addressing social determinants of health (SDoH) is associated with improved clinical outcomes for patients with chronic diseases in safety-net settings. This qualitative study supplemented by descriptive quantitative analysis investigates the degree of alignment between patient and clinicians’ perceptions of SDoH resources and referrals in clinics within the public healthcare delivery system in San Francisco. We conducted a qualitative analysis of in-depth interviews, patient-led neighborhood tours, and in-person clinic visit observations with 10 patients and 7 primary care clinicians. Using a convergent parallel mixed methodology, we also completed a descriptive quantitative analysis comparing the categories of neighborhood health resources mentioned by patients or community leaders to the resources integrated into the electronic health record. We found that patients held a wealth of knowledge about neighborhood resources relevant to SDoH that were highly localized and specific to their communities. In addition, multiple stakeholders were involved in conducting SDoH screenings and referrals, including clinicians, system navigators such as case workers, and community-based organizations. Yet, the information flow between these stakeholders and patients lacked systematization, and the prioritization of social needs by patients and clinicians was misaligned, as represented by qualitative themes as well as quantitative differences in resource category distribution analysis (p < 0.001). Our results shed light upon opportunities for strengthening social care delivery in safety-net healthcare settings by improving patient engagement, clinic workflow, EHR engagement, and resource dissemination.

The Association Between Maternal Mortality, Adverse Childhood Experiences, and Social Determinant of Health: Where is the Evidence?

INTRODUCTION

Social determinants of health and adverse childhood experiences have been implicated as driving causes of maternal mortality but the empirical evidence to substantiate those relationships is lacking. We aimed to understand the prevalence and intersection of social determinants of health and adverse childhood experiences among maternal deaths in Colorado based on a review of records obtained for our state's maternal mortality review committee.

METHODS

A 5-member interdisciplinary team adapted the Protocol for Responding to and Assessing Patients' Assets, Risk, and Experiences and the Adverse Childhood Experiences tools to create a data collection tool. The team reviewed records collected for the purpose of maternal mortality review for pregnancy-associated deaths that occurred in Colorado between 2014 and 2016 (N = 94).

RESULTS

The review identified an overwhelming lack of information regarding social determinants of health or adverse childhood experiences in the records used to review maternal deaths. The most common finding of the social determinants of health was a lack of conclusive evidence in the record (35.1-94.7%). Similarly, the reviewers were unable to make a determination from the available records for 92.1% of adverse childhood experience indicators.

DISCUSSION

The lack of social and contextual information in the records points to challenges of relying on medical records for identification of non-medical causes of maternal mortality. Maternal mortality review committees would be well served to invest in alternative data sources, such as community dashboards and informant interviews, to inform a more comprehensive understanding of causes of maternal mortality.

National Cancer Institute-Funded Social Risk Research in Cancer Care Delivery: Opportunities for Future Research

BACKGROUND

Cancer patients and survivors with food insecurity, housing instability, and transportation-related barriers face challenges in access and utilization of quality cancer care thereby adversely impacting their health outcomes. This portfolio analysis synthesized and described National Cancer Institute (NCI)-supported social risk research focused on assessing food insecurity, housing instability, and transportation-related barriers among individuals diagnosed with cancer.

METHODS

We conducted a query using the National Institutes of Health iSearch tool to identify NCI-awarded extramural research and training grants (2010-2022). Grant abstracts, specific aims, and research strategies were coded for research characteristics, study population, and outcomes.

RESULTS

Of the 30 grants included in this analysis, most assessed transportation-related barriers as patient-level social needs. Grants focused on community-level social risks, food insecurity, and housing instability were largely absent. Most grants included activities that identified the presence of social risks and/or needs (n = 24), connected patients to social care resources (n = 10), and engaged community members or organizations to inform the research study (n = 9). Of the grants, 18 focused on a single type of cancer, primarily breast cancer, and more than half focused on the treatment and survivorship phases.

CONCLUSIONS

In the last decade, there has been limited NCI-funded social risk research grants focused on food insecurity and housing instability. Findings highlight opportunities for future cancer care delivery research, including community and health system-level approaches that integrate social and clinical care to address social risks and social needs. Such efforts can help improve outcomes of populations that experience cancer health and health-care disparities.

How can Big Data Analytics Support People-Centred and Integrated Health Services: A Scoping Review

Introduction:

Health systems in high-income countries face a variety of challenges calling for a systemic approach to improve quality and efficiency. Putting people in the centre is the main idea of the WHO model of people-centred and integrated health services. Integrating health services is fuelled by an integration of health data with great potentials for decision support based on big data analytics. The research question of this paper is “How can big data analytics support people-centred and integrated health services?”

Methods:

A scoping review following the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses – Scoping Review (PRISMA-ScR) statement was conducted to gather information on how big data analytics can support people-centred and integrated health services. The results were summarized in a role model of a people-centred and integrated health services platform illustrating which data sources might be integrated and which types of analytics might be applied to support the strategies of the people-centred and integrated health services framework to become more integrated across the continuum of care. Additional rapid literature reviews were conducted to generate frequency distributions of the most often used data types and analytical methods in the medical literature. Finally, the main challenges connected with big data analytics were worked out based on a content analysis of the results from the scoping literature review.

Results:

Based on the results from the rapid literature reviews the most often used data sources for big data analytics (BDA) in healthcare were biomarkers (39.3%) and medical images (30.9%). The most often used analytical models were support vector machines (27.3%) and neural networks (20.4%). The people-centred and integrated health services framework defines different strategic interventions for health services to become more integrated. To support all aspects of these interventions a comparably integrated platform of health-related data would be needed, so that a role model labelled as people-centred health platform was developed. Based on integrated data the results of the scoping review (n = 72) indicate, that big data analytics could for example support the strategic intervention of tailoring personalized health plans (43.1%), e.g. by predicting individual risk factors for different therapy options. Also BDA might enhance clinical decision support tools (31.9%), e.g. by calculating risk factors for disease uptake or progression. BDA might also assist in designing population-based services (26.4% by clustering comparable individuals in manageable risk groups e.g. mentored by specifically trained, non-medical professionals. The main challenges of big data analytics in healthcare were categorized in regulatory, (information-) technological, methodological, and cultural issues, whereas methodological challenges were mentioned most often (55.0%), followed by regulatory challenges (43.7%).

Discussion:

The BDA applications presented in this literature review are based on findings which have already been published. For some important components of the framework on people-centred care like enhancing the role of community care or establishing intersectoral partnerships between health and social care institutions only few examples of enabling big data analytical tools were found in the literature. Quite the opposite does this mean that these strategies have less potential value, but rather that the source systems in these fields need to be further developed to be suitable for big data analytics.

Conclusions:

Big data analytics can support people-centred and integrated health services e.g. by patient similarity stratifications or predictions of individual risk factors. But BDA fails to unfold its full potential until data source systems are still disconnected and actions towards a comprehensive and people-centred health-related data platform are politically insufficiently incentivized. This work highlighted the potential of big data analysis in the context of the model of people-centred and integrated health services, whereby the role model of the person-centered health platform can be used as a blueprint to support strategies to improve person-centered health care. Likely because health data is extremely sensitive and complex, there are only few practical examples of platforms to some extent already capable of merging and processing people-centred big data, but the integration of health data can be expected to further proceed so that analytical opportunities might also become reality in the near future.

Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania

Machine learning methods for health care delivery optimization have the potential to improve retention in HIV care, a critical target of global efforts to end the epidemic. However, these methods have not been widely applied to medical record data in low- and middle-income countries. We used an ensemble decision tree approach to predict risk of disengagement from HIV care (missing an appointment by ≥28 days) in Tanzania. Our approach used routine electronic medical records (EMR) from the time of antiretroviral therapy (ART) initiation through 24 months of follow-up for 178 adults (63% female). We compared prediction accuracy when using EMR-based predictors alone and in combination with sociodemographic survey data collected by a research study. Models that included only EMR-based indicators and incorporated changes across past clinical visits achieved a mean accuracy of 75.2% for predicting risk of disengagement in the next 6 months, with a mean sensitivity of 54.7% for targeting the 30% highest-risk individuals. Additionally including survey-based predictors only modestly improved model performance. The most important variables for prediction were time-varying EMR indicators including changes in treatment status, body weight, and WHO clinical stage. Machine learning methods applied to existing EMR data in resource-constrained settings can predict individuals' future risk of disengagement from HIV care, potentially enabling better targeting and efficiency of interventions to promote retention in care.

"It's Not Just the Right Thing . . . It's a Survival Tactic": Disentangling Leaders' Motivations and Worries on Social Care

Health care organizations face growing pressure to improve their patients’ social conditions, such as housing, food, and economic insecurity. Little is known about the motivations and concerns of health care organizations when implementing activities aimed at improving patients’ social conditions. We used semi-structured interviews with 29 health care organizations to explore their motivations and tensions around social care. Administrators described an interwoven set of motivations for delivering social care: (a) doing the right thing for their patients, (b) improving health outcomes, and (c) making the business case. Administrators expressed tensions around the optimal role for health care in social care including uncertainty around (a) who should be responsible, (b) whether health care has the needed capacity/skills, and (c) sustainability of social care activities. Health care administrators could use guidance and support from policy makers on how to effectively prioritize social care activities, partner with other sectors, and build the needed workforce.

"Engaging stakeholders in integrating social determinants of health into electronic health records: a scoping review"

Social, environmental, and behavioural factors impact human health. Integrating these social determinants of health (SDOH) into electronic health records (EHR) may improve individual and population health. But how these data are collectedand their use in clinical settings remain unclear. We reviewed efforts to integrate SDOH into EHR in the U.S. and Canada, especially how this implementation serves Indigenous peoples. We followed an established scoping review process, performing iterative keyword searches in subject-appropriate databases, reviewing identified works' bibliographies, and soliciting recommendations from subject-matter experts. We reviewed 20 articles from an initial set of 2,459. Most discussed multiple SDOH indicator standards, with the National Academy of Medicine's (NAM) the most frequently cited (n = 10). Common SDOH domains were demographics, economics, education, environment, housing, psychosocial factors, and health behaviours. Twelve articles discussed project acceptability and feasibility; eight mentioned stakeholder engagement (none specifically discussed engaging ethnic or social minorities); and six adapted SDOH measures to local cultures . Linking SDOH data to EHR as related to Indigenous communities warrants further exploration, especially how to best align cultural strengths and community expectations with clinical priorities. Integrating SDOH data into EHR appears feasible and acceptable may improve patient care, patient-provider relationships, and health outcomes.

Care transitions and social needs: A Geriatric Emergency care Applied Research (GEAR) Network scoping review and consensus statement

OBJECTIVES

Individual-level social needs have been shown to substantially impact emergency department (ED) care transitions of older adults. The Geriatric Emergency care Applied Research (GEAR) Network aimed to identify care transition interventions, particularly addressing social needs, and prioritize future research questions.

METHODS

GEAR engaged 49 interdisciplinary stakeholders, derived clinical questions, and conducted searches of electronic databases to identify ED discharge care transition interventions in older adult populations. Informed by the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE) framework, data extraction and synthesis of included studies included the degree that intervention components addressed social needs and their association with patient outcomes. GEAR convened a consensus conference to identify topics of highest priority for future care transitions research.

RESULTS

Our search identified 248 unique articles addressing care transition interventions in older adult populations. Of these, 17 individual care transition intervention studies were included in the current literature synthesis. Overall, common care transition interventions included coordination efforts, comprehensive geriatric assessments, discharge planning, and telephone or in-person follow-up. Fourteen of the 17 care transition intervention studies in older adults specifically addressed at least one social need within the PRAPARE framework, most commonly related to access to food, medicine, or health care. No care transition intervention addressing social needs in older adult populations consistently reduced subsequent health care utilization or other patient-centered outcomes. GEAR stakeholders identified that determining optimal outcome measures for ED-home transition interventions was the highest priority area for future care transitions research.

CONCLUSIONS

ED care transition intervention studies in older adults frequently address at least one social need component and exhibit variation in the degree of success on a wide array of health care utilization outcomes.

Essentialism and Exclusion: Racism in Cancer Risk Prediction Models

Cancer risk prediction models have the potential to revolutionize the science and practice of cancer prevention and control by identifying the likelihood that a patient will develop cancer at some point in the future, likely experience more benefit than harm from a given intervention, and survive their cancer for a certain number of years. The ability of risk prediction models to produce estimates that are valid and reliable for people from diverse socio-demographic backgrounds-and consequently their utility for broadening the reach of precision medicine to marginalized populations-depends on ensuring that the risk factors included in the model are represented as thoroughly and as accurately as possible. However, cancer risk prediction models created in the United States have a critical limitation, the origins of which stem from the country's earliest days: they either erroneously treat the social construct of race as an immutable biological factor (ie, they "essentialize" race), or they exclude from the model those socio-contextual factors that are associated with both race and health outcomes. Models that essentialize race and/or exclude socio-contextual factors sometimes incorporate "race corrections" that adjust a patient's risk estimate up or down based on their race. This commentary discusses the origins of race corrections, potential flaws with such corrections, and strategies for developing cohorts for developing risk prediction models that do not essentialize race or exclude key socio-contextual factors. Such models will help move the science of cancer prevention and control towards its goal of eliminating cancer disparities and achieving health equity.

Medical and Social Factors Associated with Referral for Elder Abuse Services in a National Healthcare System

BACKGROUND

Elder abuse (EA) is common and has devastating health consequences yet is not systematically assessed or documented in most health systems, limiting efforts to target healthcare-based interventions. Our objective was to examine sociodemographic and medical characteristics associated with documented referrals for EA assessment or services in a national US healthcare system.

METHODS

We conducted a national case-control study in US Veterans Health Administration facilities of primary care (PC)-engaged Veterans age ≥60 years who were evaluated by social work (SW) for EA-related concerns between 2010-18. Cases were matched 1:5 to controls with a PC visit within 60 days of the matched case SW encounter. We examined the association of patient sociodemographic and health factors with receipt of EA services in unadjusted and adjusted models.

RESULTS

Of 5,567,664 Veterans meeting eligibility criteria during the study period, 15,752 (0.3%) received services for EA (cases). Cases were mean age 74, and 54% unmarried. In adjusted logistic regression models (aOR; 95%CI), age ≥85 (3.56 v. age 60-64; 3.24-3.91), female sex (1.96; 1.76-2.21), child as next-of-kin (1.70 v. spouse; 1.57-1.85), lower neighborhood socioeconomic status (1.18 per higher quartile; 1.15-1.21), dementia diagnosis (3.01; 2.77-3.28) and receiving a VA pension (1.34; 1.23-1.46) were associated with receiving EA services.

CONCLUSION

In the largest cohort of patients receiving EA-related healthcare services studied to date, this study identified novel factors associated with clinical suspicion of EA that can be used to inform improvements in healthcare-based EA surveillance and detection.

Integrated Health and Social Care in the United States: A Decade of Policy Progress

Introduction:

Over the last decade in the United States (US), the burden of chronic disease, health care costs, and fragmented care delivery have increased at alarming rates. To address these challenges, policymakers have prioritized new payment and delivery models to incentivize better integrated health and social services.

Policy practice:

This paper outlines three major national and state policy initiatives to improve integrated health and social care over the last ten years in the US, with a focus on the Medicaid public insurance program for Americans with low incomes. Activities supported by these initiatives include screening patients for social risks in primary care clinics; building new cross-sector collaborations; financing social care with healthcare dollars; and sharing data across health, social and community services. Stakeholders from the private sector, including health systems and insurers, have partnered to advance and scale these initiatives. This paper describes the implementation and effectiveness of such efforts, and lessons learned from translating policy to practice.

Discussion and Conclusion:

National policies have catalyzed initiatives to test new integrated health and social care models, with the ultimate goal of improving population health and decreasing costs. Preliminary findings demonstrated the need for validated measures of social risk, engagement across levels of organizational leadership and frontline staff, and greater flexibility from national policymakers in order to align incentives across sectors.

Sensitivity and Specificity of Real-World Social Factor Screening Approaches

Health care organizations are increasingly documenting patients for social risk factors in structured data. Two main approaches to documentation, ICD-10 Z codes and screening questions, face limited adoption and conceptual challenges. This study compared estimates of social risk factors obtained via screening questions and ICD-10 Z diagnoses coding, as used in clinical practice, to estiamtes from validated survey instruments in a sample of adult primary care and emergency department patients at an urban safety-net health system. Financial strain, transportation barriers, food insecurity, and housing instability were independently assessed using instruments with published reliability and validity. These four social factors were also being collected by the health system in screening questions or could be mapped to ICD-10 Z code diagnosis code concepts. Neither the screening questions nor ICD-10 Z codes performed particularly well in terms of accuracy. For the screening questions, the Area Under the Curve (AUC) scores were 0.609 for financial strain, 0.703 for transportation, 0.698 for food insecurity, and 0.714 for housing instability. For the ICD-10 Z codes, AUC scores tended to be lower in the range of 0.523 to 0.535. For both screening questions and ICD-10 Z codes, the measures were much more specific than sensitive. Under real world conditions, ICD-10 Z codes and screening questions are at the minimal, or below, threshold for being diagnostically useful approaches to identifying patients’ social risk factors. Data collection support through information technology or novel approaches combining data sources may be necessary to improve the usefulness of these data.

Non-medical patient-related factor influence in proximal humeral fracture outcomes: a multicentric study

PURPOSE

Age, sex, and type of fracture have traditionally been described as prognostic factors for proximal humeral fractures (PHFs). Some non-medical patient-related factors may play a role in the outcome. This paper evaluates the association of comorbidities and socioeconomic factors with clinical outcomes for PHF.

METHODS

A total of 217 patients with PHF were evaluated according to Neer's classification with X-ray. Comorbidities were assessed through the Charlson comorbidity index and, non-medical patient-related factors were determined with a 52-item questionnaire concerning personal behaviors such as social activities, family support, economic solvency, and leisure-time activities. The clinical outcome was assessed with the Constant-Murley Score (CMS), with a minimum 1-year follow-up. The minimal clinically relevant difference for the CMS was set at 10 points. A multivariable analysis was performed to adjust for comorbidities and non-medical patient-related factors, such as age, sex, fracture classification, and treatment.

RESULTS

One hundred and eighty-three patients completed the initial research protocol, while 126 of them completed the 1-year follow-up. The mean age was 71.6 years (SD ± 13.3), and 79.3% of the patients were women. In the bivariable analysis, age and comorbidities were correlated with the CMS (correlation coefficient: - 0.34 [- 0.49, 0.17] and 0.35 [0.18, 0.50], respectively), as well as non-medical patient-related factors and the fracture pattern (p value ANOVA < 0.001). In the multivariable regression model, the effects of considering oneself socially active, without economic problems, and self-sufficient were associated with a higher CMS than the effect of the fracture pattern (beta coefficient: 11.69 [6.09-17.30], 15.54 [8.32-22.75], and 10.61 [3.34-17.88], respectively).

CONCLUSION

Socioeconomic status had a higher impact on functional outcomes than fracture pattern in patients with PHF.

Resource Brokering: Efforts to Assist Patients With Housing, Transportation, and Economic Needs in Primary Care Settings

PURPOSE

Clinicians and policy makers are exploring the role of primary care in improving patients' social conditions, yet little research examines strategies used in clinical settings to assist patients with social needs.

METHODS

Study used semistructured interviews with leaders and frontline staff at 29 diverse health care organizations with active programs used to address patients' social needs. Interviews focused on how organizations develop and implement case management-style programs to assist patients with social needs including staffing, assistance intensity, and use of referrals to community-based organizations (CBOs).

RESULTS

Organizations used case management programs to assist patients with social needs through referrals to CBOs and regular follow-up with patients. About one-half incorporated care for social needs into established case management programs and the remaining described standalone programs developed specifically to address social needs independent of clinical needs. Referrals were the foundation for assistance and included preprinted resource lists, patient-tailored lists, and warm handoffs to the CBOs. While all organizations referred patients to CBOs, some also provided more intense services such as assistance completing patients' applications for services or conducting home visits. Organizations described 4 operational challenges in addressing patients' social needs: (1) effectively engaging CBOs; (2) obtaining buy-in from clinical staff; (3) considering patients' perspectives; and (4) ensuring program sustainability.

CONCLUSION

As the US health care sector faces pressure to improve quality while managing costs, many health care organizations will likely develop or rely on case management approaches to address patients' social conditions. Health care organizations may require support to address the key operational challenges.Visual abstract.

Social Determinants of Health and After-Hours Electronic Health Record Documentation: A National Survey of US Physicians

Identifying patients' social determinants of health (SDoH) can improve patient outcomes but may increase clinicians' documentation time. However, there is limited evidence of how many physicians document SDoH and the associated burden. To address this gap, this study examines documentation of SDoH and after-hours electronic health record (EHR) work among a nationally representative sample of US office-based physicians. This was a cross-sectional analysis of the 2018-2019 National Electronic Health Records Survey. A survey design-adjusted bivariate analysis was used to estimate the prevalence of SDoH documentation and compare this activity between physicians' and practices' characteristics. A modified multivariable Poisson model was used to estimate prevalence ratios of SDoH documentation and after-hours work. The study sample included a weighted sample of 303,389 US physicians (31.5%, female; 72.5%, aged ≥50 years; 48.8% primary care specialty). Of those, 84.3% reported documenting patients' SDoH information. Physicians documenting patients' SDoH tend to be younger (<50 years). Prevalence estimates of after-hours EHR documentation were comparable between physicians recording patients' SDoH and those not (33.7% vs. 33.0%) and this difference did not reach statistical significance in adjusted analysis (adjusted prevalence ratio, 0.94, 95% confidence interval, 0.64-1.39). Thus, documenting patients' SDoH appears to be common among US physicians, and this activity is not associated with after-hours EHR documentation. Future studies should examine how patients' SDoH information is used and its association with patient health outcomes.

Integrating social determinants of health screening and referral during routine emergency department care: evaluation of reach and implementation challenges

Background

Despite the importance of social determinants in health outcomes, little is known about the best practices for screening and referral during clinical encounters. This study aimed to implement universal social needs screening and community service referrals in an academic emergency department (ED), evaluating for feasibility, reach, and stakeholder perspectives.

Methods

Between January 2019 and February 2020, ED registration staff screened patients for social needs using a 10-item, low-literacy, English-Spanish screener on touchscreens that generated automatic referrals to community service outreach specialists and data linkages. The RE-AIM framework, specifically the constructs of reach and adoption, guided the evaluation. Reach was estimated through a number of approaches, completed screenings, and receipt of community service referrals. Adoption was addressed qualitatively via content analysis and qualitative coding techniques from (1) meetings, clinical interactions, and semi-structured interviews with ED staff and (2) an iterative “engagement studio” with an advisory group composed of ED patients representing diverse communities.

Results

Overall, 4608 participants were approached, and 61% completed the screener. The most common reason for non-completion was patient refusal (43%). Forty-seven percent of patients with completed screeners communicated one or more needs, 34% of whom agreed to follow-up by resource specialists. Of the 482 participants referred, 20% were reached by outreach specialists and referred to community agencies. Only 7% of patients completed the full process from screening to community service referral; older, male, non-White, and Hispanic patients were more likely to complete the referral process. Iterative staff (n = 8) observations and interviews demonstrated that, despite instruction for universal screening, patient presentation (e.g., appearance, insurance status) drove screening decisions. The staff communicated discomfort with, and questioned the usefulness of, screening. Patients (n = 10) communicated a desire for improved understanding of their unmet needs, but had concerns about stigmatization and privacy, and communicated how receptivity of screenings and outreach are influenced by the perceived sincerity of screening staff.

Conclusions

Despite the limited time and technical barriers, few patients with social needs ultimately received service referrals. Perspectives of staff and patients suggest that social needs screening during clinical encounters should incorporate structure for facilitating patient-staff relatedness and competence, and address patient vulnerability by ensuring universal, private screenings with clear intent.

Trial registration

ClinicalTrials.gov, NCT04630041.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43058-021-00212-y.

Distinguishing neighborhood and individual social risk factors in health care

OBJECTIVE

To investigate (a) the magnitude of the independent associations of neighborhood-level and person-level social risk factors (SRFs) with quality, (b) whether neighborhood-level SRF associations may be proxies for person-level SRF associations, and (c) how the association of person-level SRFs and quality varies by neighborhood-level SRFs.

DATA SOURCES

2015-2016 Medicare Advantage HEDIS data, Medicare beneficiary administrative data, and 2016 American Community Survey (ACS).

STUDY DESIGN

Mixed effects linear regression models (1) estimated overall inequities by neighborhood-level and person-level SRFs, (2) compared neighborhood-level associations to person-level associations, and (3) tested the interactions of person-level SRFs with corresponding neighborhood-level SRFs.

DATA COLLECTION/EXTRACTION METHODS

Beneficiary-level SES and disability administrative data and five-year ACS neighborhood-level SRF information were each linked to HEDIS data.

PRINCIPAL FINDINGS

For all or nearly all HEDIS measures, quality was worse in neighborhoods lower in SES and in neighborhoods with higher proportions of residents with a disability. Quality by neighborhood racial and ethnic composition was mixed. Accounting for corresponding person-level SRFs reduced neighborhood SRF associations by 25% for disability, 43% for SES, and 74%-102% for racial and ethnic groups. Person-level SRF coefficients were not consistently reduced in models that added neighborhood-level SRFs. In 19 of 35 instances, there were significant (p < 0.05) interactions between neighborhood-level and corresponding person-level SRFs. Significant interactions were always positive for disability, SES, Black, and Hispanic, indicating more negative neighborhood effects for people with SRFs that did not match their neighborhood and more positive neighborhood effects for people with SRFs that matched their neighborhood.

CONCLUSIONS

Relying solely on neighborhood-level SRF models that omit similar person-level SRFs overattributes inequities to neighborhood characteristics. Neighborhood-level characteristics account for much less variation in these measures' scores than similar person-level SRFs. Inequity-reduction programs may be most effective when targeting neighborhoods with a high proportion of people with a given SRF.

Implementation of social needs screening in primary care: a qualitative study using the health equity implementation framework

BACKGROUND

Screening in primary care for unmet individual social needs (e.g., housing instability, food insecurity, unemployment, social isolation) is critical to addressing their deleterious effects on patients' health outcomes. To our knowledge, this is the first study to apply an implementation science framework to identify implementation factors and best practices for social needs screening and response.

METHODS

Guided by the Health Equity Implementation Framework (HEIF), we collected qualitative data from clinicians and patients to evaluate barriers and facilitators to implementing the Protocol for Responding to and Assessing Patients' Assets, Risks, and Experiences (PRAPARE), a standardized social needs screening and response protocol, in a federally qualified health center. Eligible patients who received the PRAPARE as a standard of care were invited to participate in semi-structured interviews. We also obtained front-line clinician perspectives in a semi-structured focus group. HEIF domains informed a directed content analysis.

RESULTS

Patients and clinicians (i.e., case managers) reported implementation barriers and facilitators across multiple domains (e.g., clinical encounters, patient and provider factors, inner context, outer context, and societal influence). Implementation barriers included structural and policy level determinants related to resource availability, discrimination, and administrative burden. Facilitators included evidence-based clinical techniques for shared decision making (e.g., motivational interviewing), team-based staffing models, and beliefs related to alignment of the PRAPARE with patient-centered care. We found high levels of patient acceptability and opportunities for adaptation to increase equitable adoption and reach.

CONCLUSION

Our results provide practical insight into the implementation of the PRAPARE or similar social needs screening and response protocols in primary care at the individual encounter, organizational, community, and societal levels. Future research should focus on developing discrete implementation strategies to promote social needs screening and response, and associated multisector care coordination to improve health outcomes and equity for vulnerable and marginalized patient populations.

Racial/Ethnic Variations in Social Determinants of Mental Health Among Medicare Advantage Beneficiaries

OBJECTIVES

We examine associations between social determinants and mental health and assess how the associations vary by race/ethnicity using a large, diverse sample of older adults.

METHOD

A retrospective study of 444,057 older adults responding to the Medicare Health Outcomes Survey in 2015-2017 was conducted. Using a multilevel linear regression, we examined the associations between the self-reported number of unhealthy days due to mental health and social determinants, stratified by race/ethnicity.

RESULTS

Health factors were most strongly associated with unhealthy days across all racial/ethnic groups. Strength of other factors varied by race/ethnicity. Social/economic factors had stronger associations among Whites, Asians, and multiracial individuals, while such factors were not significant for American Indians/Alaska Natives and Native Hawaiians/Other Pacific Islanders.

DISCUSSION

We found varying degrees of associations between social determinants and poor mental health by racial/ethnic groups. These results suggest that homogeneous interventions may not meet the mental health needs of all.

Evaluating the association of social needs assessment data with cardiometabolic health status in a federally qualified community health center patient population

BACKGROUND

Health systems are increasingly using standardized social needs screening and response protocols including the Protocol for Responding to and Assessing Patients' Risks, Assets, and Experiences (PRAPARE) to improve population health and equity; despite established relationships between the social determinants of health and health outcomes, little is known about the associations between standardized social needs assessment information and patients' clinical condition.

METHODS

In this cross-sectional study, we examined the relationship between social needs screening assessment data and measures of cardiometabolic clinical health from electronic health records data using two modelling approaches: a backward stepwise logistic regression and a least absolute selection and shrinkage operation (LASSO) logistic regression. Primary outcomes were dichotomized cardiometabolic measures related to obesity, hypertension, and atherosclerotic cardiovascular disease (ASCVD) 10-year risk. Nested models were built to evaluate the utility of social needs assessment data from PRAPARE for risk prediction, stratification, and population health management.

RESULTS

Social needs related to lack of housing, unemployment, stress, access to medicine or health care, and inability to afford phone service were consistently associated with cardiometabolic risk across models. Model fit, as measured by the c-statistic, was poor for predicting obesity (logistic = 0.586; LASSO = 0.587), moderate for stage 1 hypertension (logistic = 0.703; LASSO = 0.688), and high for borderline ASCVD risk (logistic = 0.954; LASSO = 0.950).

CONCLUSIONS

Associations between social needs assessment data and clinical outcomes vary by cardiometabolic condition. Social needs assessment data may be useful for prospectively identifying patients at heightened cardiometabolic risk; however, there are limits to the utility of social needs data for improving predictive performance.

Insurance Coverage for Non-standard Workers: Experiences of Temporary Workers, Freelancers, and Part-time Workers in the USA, 2010-2017

OBJECTIVE

To estimate insurance disparities across non-standard employment categories and to determine how coverage disparities shifted following health reform in 2014.

METHODS

We analyzed nationally representative data on working-age adults from the Medical Expenditure Panel Survey (MEPS) (2010-2012 and 2015-2017, N=79,182) to estimate insurance rates across three groups of non-standard workers (full-time temporary workers, freelancers, and part-time workers) compared to standard workers.

RESULTS

Uninsurance decreased after health reform for all groups of non-standard workers, ranging from a 10.0- to 14.3-percentage point decline (p<0.001). Yet, uninsurance rates remained high for freelancers (30.8%), full-time temporary workers (25.1%), and part-time workers (17.9%) relative to standard workers (11.9%) in 2015-2017 (p<0.001). Residence in a Medicaid expansion state was associated with lower uninsurance rates for all categories of workers.

CONCLUSIONS

Workers in non-standard jobs continue to face challenges obtaining health insurance coverage. Our findings highlight the continued high risk of uninsurance for full-time temporary workers and freelancers.

Social and Behavioral Variables in the Electronic Health Record: A Path Forward to Increase Data Quality and Utility

PURPOSE

Social and behavioral determinants of health (SBDH) are important factors that affect the health of individuals but are not routinely captured in a structured and systematic manner in electronic health records (EHRs). The purpose of this study is to generate recommendations for systematic implementation of SBDH data collection in EHRs through (1) reviewing SBDH conceptual and theoretical frameworks and (2) eliciting stakeholder perspectives on barriers to and facilitators of using SBDH information in the EHR and priorities for data collection.

METHOD

The authors reviewed SBDH frameworks to identify key social and behavioral variables and conducted focus groups and interviews with 17 clinicians and researchers at Johns Hopkins Health System between March and May 2018. Transcripts were coded and common themes were extracted to understand the barriers to and facilitators of accessing SBDH information.

RESULTS

The authors found that although the frameworks agreed that SBDH affect health outcomes, the lack of model consensus complicates the development of specific recommendations for the prioritization of SBDH data collection. Study participants recognized the importance of SBDH information and individual health and agreed that patient-reported information should be captured, but clinicians and researchers cited different priorities for which variables are most important. For the few SBDH variables that are captured, participants reported that data were often incomplete, unclear, or inconsistent, affecting both researcher and clinician responses to SBDH barriers to health.

CONCLUSIONS

Health systems need to identify and prioritize the systematic implementation of collection of a high-impact but limited list of SBDH variables in the EHR. These variables should affect care and be amenable to change and collection should be integrated into clinical workflows. Improved data collection of SBDH variables can lead to a better understanding of how SBDH affect health outcomes and ways to better address underlying health disparities that need urgent action.

Health-Related Social Needs and Increased Readmission Rates: Findings from the Nationwide Readmissions Database

BACKGROUND

While health-related social needs (HRSN) are known to compromise health, work to date has not clearly demonstrated the relationship between clinically acknowledged social needs, via ICD-10 Z-codes, and readmission.

OBJECTIVE

Assess the rate of 30-, 60-, and 90-day readmission by the level of ICD-10-identified social need. In addition, we examined the associations between demographics, social need, hospital characteristics, and comorbidities on 30-day readmission.

DESIGN

Retrospective study using the 2017 Nationwide Readmission Database PARTICIPANTS: We identified 5 domains of HRSN from ICD-10 diagnosis codes including employment, family, housing, psychosocial, and socioeconomic status (SES) and identified how many and which an individual was coded with during the year.

MAIN MEASURES

The proportion of patients with 30-, 60-, and 90-day readmission stratified by the number of HRSN domains with a multivariable logistic regression to examine the relationship between the number/type of and readmission adjusting for sex, age, payer, hospital characteristics, functional limitations, and comorbidities.

KEY RESULTS

From 13,217,506 patients, only 2.4% had at least one HRSN diagnosis. Among patients without HRSN, 11.5% had a 30-day readmission, compared to 27.0% of those with 1 domain, increasing to 63.5% for patients with codes in 5 domains. Similar trends were observed for 60- and 90-day readmission; 78.7% of patients with documented HRSN in all 5 domains were hospitalized again within 90 days. The adjusted odds ratio for readmission for individuals with all 5 domains was 12.55 (95% CI: 9.04, 17.43). Housing and employment emerged as two of the most commonly documented HRSN, as well as having the largest adjusted odds ratio.

CONCLUSIONS

There is a dose-response relationship between the number of HRSN diagnoses and hospital readmission. This work calls attention to the need to develop interventions to reduce readmissions for those at social risk and demonstrates the significance of ICD-10 Z-codes in health outcomes studies.

COVID-19 in patients with and without cancer: Examining differences in patient characteristics and outcomes

This study examines differences between patients with and without cancer in patient demographic and clinical characteristics and COVID-19 mortality and discusses the implications of these differences in relation to existing cancer disparities and COVID-19 vulnerabilities. Data was collected as a part of a retrospective study on a cohort of COVID-19 positive patients across Mount Sinai Health System from March 28, 2020 to April 26, 2020. Descriptive, comparative, and regression analyses were applied to examine differences between patients with and without cancer in demographic and clinical characteristics and COVID-19 mortality and whether cancer status predicts COVID-19 mortality controlling for these covariates using SAS 9.4. Results showed that, of 4641 patients who tested positive for COVID-19, 5.1% (N=236) had cancer. The median age of the total sample was 58 years (Q1-Q3: 41-71); 55.3% were male, 19.2% were current/former smokers, 6.1% were obese. The most commonly reported comorbidities were hypertension (22.6%) and diabetes (16.0%). Overall, the COVID-19 mortality rate was 8.3%. Examining differences between COVID-19 patients with and without cancer revealed significant differences (p<0.05) in COVID-19 mortality, hospitalization rates, age, gender, race, smoking status, obesity, and comorbidity indicators (e.g., diabetes) with cancer patients more likely to be older, male, black, obese, smokers, and with existing comorbidities. Controlling for these clinical, demographic, and behavioral characteristics, results of logistic regression analyses showed significant effects of older age and male gender on COVID-19 mortality (p<0.05). While cancer patients with COVID-19 were more likely to experience worse COVID-19 outcomes, these associations might be related to common cancer and COVID-19 vulnerability factors such as older age and gender. The coexistence of these vulnerability age and gender factors in both cancer and COVID-19 populations emphasizes the need for better understanding of their implications for cancer and COVID-19 disparities, both diseases prevention efforts, policies, and clinical management.

Receipt of Social Needs Assistance and Health Center Patient Experience of Care

INTRODUCTION

Community health centers often screen for and address patients' unmet social needs. This study examines the degree to which community health center patients report receiving social needs assistance and compares measures of access and quality between patients who received assistance versus similar patients who did not.

METHODS

A nationally representative sample of 4,699 nonelderly adults receiving care at community health centers from the 2014-2015 Health Resources and Services Administration Health Center Patient Survey was used, representing 12.6 million patients. The exposure-having "received social needs assistance"-was based on whether a patient received any community health center assistance accessing social programs (e.g., applying for government benefits) or basic needs (e.g., obtaining transportation, housing, food). Using logistic regression models with inverse probability of treatment weights, outcomes for patients who received social needs assistance with similar patients who did not were compared. Study outcomes, reported as absolute adjusted differences, included reporting a community health center as a usual source of care, reporting the emergency department as a usual source of care, perceived quality of care, and willingness to recommend the community health center to others. Data were analyzed in 2020.

RESULTS

Of the sample, 36% reported receiving social needs assistance, where the most common form of assistance was applying for government benefits. Relative to similar patients who did not receive social needs assistance, patients receiving assistance were significantly more likely to report a community health center as their usual source of care (adjusted difference=7.2 percentage points, 95% CI=2.2, 12.1) and to report perceived quality of care as "the best" (adjusted difference=11.1, 95% CI=5.4, 16.9). They were significantly less likely to report the emergency department as their usual source of care (adjusted difference= -4.2, 95% CI= -7.0, -1.3).

CONCLUSIONS

As community health centers and other providers consider providing social needs assistance to patients, these results suggest that doing so may be associated with improved access to and quality of care.