Expenditure Reductions Associated with a Social Service Referral Program

Piloting a Clinical Decision Support Tool to Identify Patients With Social Needs and Provide Navigation Services and Referral to Community-Based Organizations: Protocol for a Randomized Controlled Trial

BACKGROUND

Social needs and social determinants of health (SDOH) significantly outrank medical care when considering the impact on a person's length and quality of life, resulting in poor health outcomes and worsening life expectancy. Integrating social needs and SDOH data along with clinical risk information within operational clinical decision support (CDS) systems built into electronic health records (EHRs) is an effective approach to addressing health-related social needs. To achieve this goal, applied research is needed to develop EHR-integrated CDS tools and closed-loop referral systems and implement and test them in the digital and clinical workflows at health care systems and collaborating community-based organizations (CBOs).

OBJECTIVE

This study aims to describe the protocol for a mixed methods study including a randomized controlled trial and a qualitative phase assessing the feasibility, acceptability, and effectiveness of an EHR-integrated digital platform to identify patients with social needs and provide navigation services and closed-loop referrals to CBOs to address their social needs.

METHODS

The randomized controlled trial will enroll and randomize adult patients living in socioeconomically challenged neighborhoods in Baltimore City receiving care at a single academic health care institution in the 3-month intervention (using the digital platform) or the 3-month control (standard-of-care assessment and addressing of social needs) arms (n=295 per arm). To evaluate the feasibility and acceptability of the digital platform and its impact on the clinical and digital workflow and patient care, we will conduct focus groups with the care teams in the health care system (eg, clinical providers, social workers, and care managers) and collaborating CBOs. The outcomes will be the acceptability, feasibility, and effectiveness of the CDS tool and closed-loop referral system.

RESULTS

This clinical trial opened to enrollment in June 2023 and will be completed in March 2025. Initial results are expected to be published in spring 2025. We will report feasibility outcome measures as weekly use rates of the digital platform. The acceptability outcome measure will be the provider's and patient's responses to the truthfulness of a statement indicating a willingness to use the platform in the future. Effectiveness will be measured by tracking a 3-month change in identified social needs and provided navigation services as well as clinical outcomes such as hospitalization and emergency department visits.

CONCLUSIONS

The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of such interventions in digital and clinical workflows to enhance the health care system and CBO ability related to social needs assessment and intervention. These results may inform the construction of a future multi-institutional trial designed to test the effectiveness of this intervention across different health care systems and care settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05574699; https://clinicaltrials.gov/study/NCT05574699.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/57316.

Acceptance of Automated Social Risk Scoring in the Emergency Department: Clinician, Staff, and Patient Perspectives

Introduction

Healthcare organizations are under increasing pressure from policymakers, payers, and advocates to screen for and address patients' health-related social needs (HRSN). The emergency department (ED) presents several challenges to HRSN screening, and patients are frequently not screened for HRSNs. Predictive modeling using machine learning and artificial intelligence, approaches may address some pragmatic HRSN screening challenges in the ED. Because predictive modeling represents a substantial change from current approaches, in this study we explored the acceptability of HRSN predictive modeling in the ED.

Methods

Emergency clinicians, ED staff, and patient perspectives on the acceptability and usage of predictive modeling for HRSNs in the ED were obtained through in-depth semi-structured interviews (eight per group, total 24). All participants practiced at or had received care from an urban, Midwest, safety-net hospital system. We analyzed interview transcripts using a modified thematic analysis approach with consensus coding.

Results

Emergency clinicians, ED staff, and patients agreed that HRSN predictive modeling must lead to actionable responses and positive patient outcomes. Opinions about using predictive modeling results to initiate automatic referrals to HRSN services were mixed. Emergency clinicians and staff wanted transparency on data inputs and usage, demanded high performance, and expressed concern for unforeseen consequences. While accepting, patients were concerned that prediction models can miss individuals who required services and might perpetuate biases.

Conclusion

Emergency clinicians, ED staff, and patients expressed mostly positive views about using predictive modeling for HRSNs. Yet, clinicians, staff, and patients listed several contingent factors impacting the acceptance and implementation of HRSN prediction models in the ED.

Evaluating Intensity, Complexity, and Potential for Causal Inference in Social Needs Interventions: A Review of a Scoping Review

Importance

Interventions that address needs such as low income, housing instability, and safety are increasingly appearing in the health care sector as part of multifaceted efforts to improve health and health equity, but evidence relevant to scaling these social needs interventions is limited.

Objective

To summarize the intensity and complexity of social needs interventions included in randomized clinical trials (RCTs) and assess whether these RCTs were designed to measure the causal effects of intervention components on behavioral, health, or health care utilization outcomes.

Evidence Review

This review of a scoping review was based on a Patient-Centered Outcomes Research Institute-funded evidence map of English-language US-based RCTs of social needs interventions published between January 1, 1995, and April 6, 2023. Studies were assessed for features related to intensity (defined using modal values as providing as-needed interaction, 8 participant contacts or more, contacts occurring every 2 weeks or more often, encounters of 30 minutes or longer, contacts over 6 months or longer, or home visits), complexity (defined as addressing multiple social needs, having dedicated staff, involving multiple intervention components or practitioners, aiming to change multiple participant behaviors [knowledge, action, or practice], requiring or providing resources or active assistance with resources, and permitting tailoring), and the ability to assess causal inferences of components (assessing interventions, comparators, and context).

Findings

This review of a scoping review of social needs interventions identified 77 RCTs in 93 publications with a total of 135 690 participants. Most articles (68 RCTs [88%]) reported 1 or more features of high intensity. All studies reported 1 or more features indicative of high complexity. Because most studies compared usual care with multicomponent interventions that were moderately or highly dependent on context and individual factors, their designs permitted causal inferences about overall effectiveness but not about individual components.

Conclusions and Relevance

Social needs interventions are complex, intense, and include multiple components. Our findings suggest that RCTs of these interventions address overall intervention effectiveness but are rarely designed to distinguish the causal effects of specific components despite being resource intensive. Future studies with hybrid effectiveness-implementation and sequential designs, and more standardized reporting of intervention intensity and complexity could help stakeholders assess the return on investment of these interventions.

Health-Related Social Needs: Which Patients Respond to Screening and Who Receives Resources?

BACKGROUND

Health systems are screening patients for health-related social needs (HRSN) but the optimal approach is unknown.

OBJECTIVE

To describe the variation in responding to an HRSN questionnaire delivered via patient portal, and whether referral to and resources provided by social workers differed by response status.

DESIGN

Retrospective observational study.

PARTICIPANTS

Primary care patients with a visit between June 2020 and January 2022.

INTERVENTION

HRSN questionnaire MAIN MEASURES: We identified each patient's index visit (e.g., date of their first questionnaire response for responders or their first visit within the study period for non-responders). Through the EHR, we identified patients' demographic characteristics. We linked the area deprivation index (ADI) to each patient and grouped patients into quintiles. We used multilevel logistic regressions to identify characteristics associated with responding to the questionnaire and, for responders, reporting a need. We also determined if responder status was associated with receiving a social worker referral or receiving a resource. We included patient demographics and ADI quintile as fixed variables and practice site as a random variable.

KEY RESULTS

Our study included 386,997 patients, of which 51% completed at least one HRSN questionnaire question. Patients with Medicaid insurance (AOR: 0.62, 95%CI: 0.61, 0.64) and those who lived in higher ADI neighborhoods had lower adjusted odds of responding (AOR: 0.76, 95% CI: 0.75, 0.78 comparing quintile 5 to quintile 1). Of responders, having Medicaid insurance (versus private) increased the adjusted odds of reporting each of the HRSN needs by two- to eightfold (p < 0.01). Patients who completed a questionnaire (versus non-responders) had similar adjusted odds of receiving a referral (AOR: 0.91, 95% CI: 0.80, 1.02) and receiving a resource from a SW (AOR: AOR: 1.18, 95%CI: 0.79, 1.77).

CONCLUSION

HRSN questionnaire responses may not accurately represent the needs of patients, especially when delivered solely via patient portal.

Non-response Bias in Social Risk Factor Screening Among Adult Emergency Department Patients

Healthcare organizations increasingly use screening questionnaires to assess patients' social factors, but non-response may contribute to selection bias. This study assessed differences between respondents and those refusing participation in a social factor screening. We used a cross-sectional approach with logistic regression models to measure the association between subject characteristics and social factor screening questionnaire participation. The study subjects were patients from a mid-western state safety-net hospital's emergency department. Subjects' inclusion criteria were: (1) ≥ 18 years old, (2) spoke English or Spanish, and (3) able to complete a self-administered questionnaire. We classified subjects that consented and answered the screening questionnaire in full as respondents. All others were non-respondents. Using natural language processing, we linked all subjects' participation status to demographic characteristics, clinical data, an area-level deprivation measure, and social risk factors extracted from clinical notes. We found that nearly 6 out of every 10 subjects approached (59.9%), consented, and completed the questionnaire. Subjects with prior documentation of financial insecurity were 22% less likely to respond to the screening questionnaire (marginal effect = -22.40; 95% confidence interval (CI) = -41.16, -3.63; p = 0.019). No other factors were significantly associated with response. This study uniquely contributes to the growing social determinants of health literature by confirming that selection bias may exist within social factor screening practices and research studies.

Models of social prescribing to address non-medical needs in adults: a scoping review

BACKGROUND

The health and wellbeing consequences of social determinants of health and health behaviours are well established. This has led to a growing interest in social prescribing, which involves linking people to services and supports in the community and voluntary sectors to address non-medical needs. However, there is considerable variability in approaches to social prescribing with little guidance on how social prescribing could be developed to reflect local health systems and needs. The purpose of this scoping review was to describe the types of social prescribing models used to address non-medical needs to inform co-design and decision-making for social prescribing program developers.

METHODS

We searched Ovid MEDLINE(R), CINAHL, Web of Science, Scopus, National Institute for Health Research Clinical Research Network, Cochrane Central Register of Controlled Trials, WHO International Clinical Trial Registry Platform, and ProQuest - Dissertations and Theses for articles and grey literature describing social prescribing programs. Reference lists of literature reviews were also searched. The searches were conducted on 2 August 2021 and yielded 5383 results following removal of duplicates.

RESULTS

148 documents describing 159 social prescribing programs were included in the review. We describe the contexts in which the programs were delivered, the program target groups and services/supports to which participants were referred, the staff involved in the programs, program funding, and the use of digital systems.

CONCLUSIONS

There is significant variability in social prescribing approaches internationally. Social prescribing programs can be summarised as including six planning stages and six program processes. We provide guidance for decision-makers regarding what to consider when designing social prescribing programs.

Addressing Health-Related Social Needs Via Community Resources: Lessons From Accountable Health Communities

The Center for Medicare and Medicaid Innovation launched the Accountable Health Communities (AHC) Model in 2017 to assess whether identifying and addressing Medicare and Medicaid beneficiaries' health-related social needs reduced health care use and spending. We surveyed a subset of AHC Model beneficiaries with one or more health-related social needs and two or more emergency department visits in the prior twelve months to assess their use of community services and whether their needs were resolved. Survey findings indicated that navigation-connecting eligible patients with community services-did not significantly increase the rate of community service provider connections or the rate of needs resolution, relative to a randomized control group. Findings from interviews with AHC Model staff, community service providers, and beneficiaries identified challenges connecting beneficiaries to community services. When connections were made, resources often were insufficient to resolve beneficiaries' needs. For navigation to be successful, investments in additional resources to assist beneficiaries in their communities may be required.

How can we define social care and what are the levels of true integration in integrated care? A narrative review

Purpose

Integrated health and social care initiatives are increasing and health and social care systems are aiming to improve health and social outcomes in disadvantaged groups. There is a global dialogue surrounding improving services by shifting to an integrated health and social care approach. There is consensus of what is “health care”; however, the “social care” definition remains less explored. The authors describe the state of “social care” within the current integrated care literature and identify the depth of integration in current health and social care initiatives.

Design/methodology/approach

A narrative literature review, searching Medline, PsychINFO, CINAHL, PubMed, Scopus and Cochrane databases and grey literature (from 2016 to 2021), employing a search strategy, was conducted.

Findings

In total. 276 studies were eligible for full-text review, and 33 studies were included and categorised in types: “social care as community outreach dialogues”, “social care as addressing an ageing population”, “social care as targeting multimorbidity and corresponding social risks factors” and “social care as initiatives addressing the fragmentation of services”. Most initiatives were implemented in the United Kingdom. In total, 21 studies reported expanding integrated governance and partnerships; 27 studies reported having health and social care staff with clear integrated governance; 17 had dedicated funding and 11 used data-sharing and the integration of systems’ records.

Originality/value

The authors' demonstrate that social care approaches are expanding beyond the elderly, and these models have been used to respond to multimorbidity [including coronavirus disease 2019 (COVID-19)], targeting priority groups and individuals with complex presentations.

Preventing Homelessness Through the National Call Center for Homeless Veterans: Analysis of Calls and Service Referrals

OBJECTIVE

This study was the first to examine the characteristics and referral outcomes for veterans calling the National Call Center for Homeless Veterans (NCCHV).

METHODS

The authors analyzed data from NCCHV and U.S. Department of Veterans Affairs (VA) health care records.

RESULTS

Between December 2018 and October 2020, the NCCHV received 266,100 messages, with no major increase in the first 6 months of the COVID-19 pandemic. Of 110,197 veterans who contacted NCCHV, 69.6% were at risk for homelessness, and 20.1% were homeless. Most contacts (90.2%) resulted in a referral or transfer to a local resource. About 59.5% of NCCHV veterans had a medical record in the Veterans Health Administration; their use of homeless programs increased from 25.9% to 81.3%. Uses of mental health services, substance use treatment, and medical services showed small-to-moderate increases after NCCHV contacts.

CONCLUSION

NCCHV is important for linking veterans to health and social care. Additional work is needed to assess veterans' outcomes after an NCCHV contact.

Incorporating Health Policy and Advocacy Curricula Into Undergraduate Medical Education in the United States

Physicians serve as crucial advocates for their patients. Undergraduate medical education (UME) must move beyond the biomedical model, built upon the perception that health is defined purely in the absence of illness, to also incorporate population health through health policy, advocacy, and community engagement to account for structural and social determinants of health. Currently, the US guidelines for UME lack structured training in health policy or advocacy, leaving trainees ill-equipped to assume their role as physician-advocates or to engage with communities. There is an undeniable need to educate future physicians on legislative advocacy toward improving the social determinants of health through the creation of evidence-based health policy, in addition to training in effective techniques to engage in partnership with the communities in which physicians serve. The authors of this article also present curricular case studies around two programs at their institution that could be used to implement similar programs at other US medical schools.

Screening for Social Determinants of Health: Active and Passive Information Retrieval Methods

Screening for social determinants of health (SDOH) is recommended, but numerous barriers exist to implementing SDOH screening in clinical spaces. In this study, the authors identified how both active and passive information retrieval methods may be used in clinical spaces to screen for SDOH and meet patient needs. The authors conducted a retrospective sequential cohort analysis comparing the active identification of SDOH through a patient-led digital manual screening process completed in primary care offices from September 2019 to January 2020 and passive identification of SDOH through natural language processing (NLP) from September 2016 to August 2018, among 1735 patients at a large midwestern tertiary referral hospital system and its associated outlying primary care and outpatient facilities. The percent of patients identified by both the passive and active identification methods as experiencing SDOH varied from 0.3% to 4.7%. The active identification method identified social integration, domestic safety, financial resources, food insecurity, transportation, housing, and stress in proportions ranging from 5% to 36%. The passive method contributed to the identification of financial resource issues and stress, identifying 9.6% and 3% of patients to be experiencing these issues, respectively. SDOH documentation varied by provider type. The combination of passive and active SDOH screening methods can provide a more comprehensive picture by leveraging historic patient interactions, while also eliciting current patient needs. Using passive, NLP-based methods to screen for SDOH will also help providers overcome barriers that have historically prevented screening.

A Linear Decomposition Approach to Explain Excess Direct Healthcare Expenditures Associated with Pain Among Adults with Osteoarthritis

Objective:

Many patients with osteoarthritis experience pain which can lead to higher healthcare expenditures. It is important to understand the factors that drive the excess expenditures associated with pain in osteoarthritis.

Design:

Cross-sectional.

Study sample:

Our study sample consisted of adults (age ⩾ 18 years) from the Medical Expenditure Panel Survey (MEPS, 2018).

Methods:

Adults who were alive during the calendar year and had pain status were included in this study (N = 2804 weighted N = 32.03 million). Osteoarthritis was identified from the medical conditions file and household file. We used multivariable ordinary least squares regression to identify the statistically significant association of pain with direct healthcare expenditures. The Blinder-Oaxaca post-linear decomposition on log-transformed total direct healthcare expenditures was used to estimate the extent to which differences in characteristics contribute to the excess expenditures associated with pain.

Results:

Adults with osteoarthritis and pain had higher average expenditures ($21 814 vs $10 827, P < .001; 9.318 vs 8.538 in logtransformed expenditures) compared to those without pain. Pooled regression weights explained 62.9% of excess expenditures differences in characteristics between the 2 groups. The 2 main drivers of excess healthcare expenditures among adults with osteoarthritis and pain were (i) comorbidities (diabetes, asthma, chronic obstructive pulmonary disease, depression, heart diseases, cancer, and non-cancer pain conditions and (ii) prescription medications (NSAIDs, opioids, and polypharmacy).

Conclusion:

Need factors such as comorbid conditions, and prescription treatment explained the excess healthcare expenditures among adults with osteoarthritis and pain. The study findings suggest that reducing polypharmacy and appropriate management of comorbid conditions may be a pathway to reduce excess expenditures among adults with osteoarthritis and pain.

The Social Determinants of Mental Health: A Descriptive Study of State Mental Health Agencies' Priorities

Social determinants are receiving renewed attention as research demonstrates the effects of social factors on individuals' physical and mental health and elucidates the biological and psychological mechanisms underlying those effects. Through spheres of influence from policy and regulation development to direct service provision, state mental health agencies are in a unique position to lead primary and secondary prevention efforts aimed at addressing social determinants with both client-level and structural-level interventions. A survey of social determinants-related activity was sent to the Medical Directors of the state offices of mental health in all 50 states. The survey results suggest consensus among respondents as to the importance of addressing specific social determinants. However, few state mental health agencies have taken on a comprehensive and intentional approach to addressing social determinants as a unique area of activity. Specific activities are reviewed, and implications for future work is discussed.

Resource mobilization combined with motivational interviewing to promote healthy behaviors and healthy weight in low-income families: An intervention feasibility study

Objectives:

This non-randomized pilot trial examined the feasibility and acceptability of an intervention for low-income families with one parent with obesity, glucose intolerance and/or diabetes.

Methods:

The 12-month intervention combined health coaching using motivational interviewing to promote lifestyle behavior change and community resource mobilization to assist with basic needs plus diet quality and physical activity. Outcome measures included process measures, open-ended questions, and the Family Nutrition and Physical Activity scale.

Results:

Forty-five families completed an average of 2.1 health coach in-person visits, including 15 families lost to follow-up. Parents who stayed in the intervention reported the intervention was helpful. Some families and the health coach had difficulties contacting one another, and some of these families reported they would have liked more sessions with the coach. The Family Nutrition and Physical Activity scores improved significantly for all children (6 months: 2.9; p < .01; 12 months: 3.2; p < .05) and at 6 months for index children (6 months: 3.5; p < .01; 12 months: 2.9; p = .09). There was variation in the FNPA and other outcome changes between families.

Conclusion:

This intervention was feasible in terms of recruitment and delivery of family sessions and community referrals and acceptable to participants, but maintaining contact with participants was difficult. Findings warrant improvements to help retention and logistical aspects of communication between families and coaches and testing in a randomized, controlled trial.

State Expectations and Medicaid Managed Care Organizations' Efforts to Address the Social Needs of Medicaid Enrollees

Existing work on states' efforts to address the social needs of Medicaid enrollees indicate the implementation of several state-level strategies to move Medicaid Managed Care Organizations (MMCOs) toward the provision of whole-person care. However, less is known about how these expectations drive MMCOs' SDOH efforts. To address this gap, we interviewed representatives of eight MMCOs (N=28) and 12 state Medicaid offices (N=17). Participants described varying state-implemented instruments for encouraging an SDOH-focus among MMCOs, including both coercive (e.g., contractual mandates) and subtle approaches (e.g., request for proposal process and performance measurement expectations). However, regardless of states' expectations, MMCOs, driven by organizational and industry-related factors, recognized the importance of addressing SDOH as part of a holistic approach to health care. Collectively, regulatory pressures, organizational strategy, and market forces influenced MMCOs' efforts to address SDOH leading to a normalization of their role in addressing members' social needs within a medical paradigm.

Development and assessment of a natural language processing model to identify residential instability in electronic health records’ unstructured data: a comparison of 3 integrated healthcare delivery systems

Objective

To evaluate whether a natural language processing (NLP) algorithm could be adapted to extract, with acceptable validity, markers of residential instability (ie, homelessness and housing insecurity) from electronic health records (EHRs) of 3 healthcare systems.

Materials and methods

We included patients 18 years and older who received care at 1 of 3 healthcare systems from 2016 through 2020 and had at least 1 free-text note in the EHR during this period. We conducted the study independently; the NLP algorithm logic and method of validity assessment were identical across sites. The approach to the development of the gold standard for assessment of validity differed across sites. Using the EntityRuler module of spaCy 2.3 Python toolkit, we created a rule-based NLP system made up of expert-developed patterns indicating residential instability at the lead site and enriched the NLP system using insight gained from its application at the other 2 sites. We adapted the algorithm at each site then validated the algorithm using a split-sample approach. We assessed the performance of the algorithm by measures of positive predictive value (precision), sensitivity (recall), and specificity.

Results

The NLP algorithm performed with moderate precision (0.45, 0.73, and 1.0) at 3 sites. The sensitivity and specificity of the NLP algorithm varied across 3 sites (sensitivity: 0.68, 0.85, and 0.96; specificity: 0.69, 0.89, and 1.0).

Discussion

The performance of this NLP algorithm to identify residential instability in 3 different healthcare systems suggests the algorithm is generally valid and applicable in other healthcare systems with similar EHRs.

Conclusion

The NLP approach developed in this project is adaptable and can be modified to extract types of social needs other than residential instability from EHRs across different healthcare systems.

Social and demographic characteristics of frequent or high-charge emergency department users: A quantile regression application

OBJECTIVE

Heavy users of the emergency department (ED) are a heterogeneous population. Few studies have captured the social and demographic complexity of patients with the largest burden of ED use. Our objective was to model associations between social and demographic patient characteristics and quantiles of the distributions of ED use, defined as frequent and high-charge.

METHODS

We conducted a cross-sectional analysis of electronic health and billing records of 99 637 adults residing in an urban North Carolina county who visited an ED within Atrium Health, a large integrated health care system, in 2017. Mid-quantile and standard quantile regression models were used for count and continuous responses, respectively. Frequent and high-charge use outcomes were defined as the median (0.50) and upper quantiles (0.75, 0.95, 0.99) of the outcome distributions for total billed ED visits and associated charges during the study period. Patient characteristic predictors were: insurance coverage (Medicaid, Medicare, private, uninsured), total visits to ambulatory care during the study period (0, 1, >1), and patient demographics: age, gender, race, ethnicity, and living in an underprivileged community called a public health priority area (PHPA).

RESULTS

Results showed heterogeneous relationships that were stronger at higher quantiles. Having Medicaid or Medicare insurance was positively associated with ED visits and ED charges at most quantiles. Racial and geographic disparities were observed. Black patients had more ED visits and lower ED charges than their White counterparts at most quantiles of the outcome distributions. Patients living in PHPAs, had lower charges than their counterparts at the median but higher charges at the 0.95 and 0.99 quantiles.

CONCLUSIONS

The relationships between patient characteristics and frequent and high-charge use of the ED vary based on the level of use. These findings can be used to inform targeted interventions, tailored policy, and population health management initiatives.

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.

Pediatric Primary Care-Based Social Needs Services and Health Care Utilization

OBJECTIVE

To evaluate the relationship between use of primary care-based social needs services and subsequent utilization of ambulatory, emergency, and inpatient services.

METHODS

This retrospective 2012 to 2015 cohort study uses electronic medical record data from an academic pediatric primary care practice that screens universally for social needs and delivers services via in-house social work staff. Logistic regression (N = 7300) examines how patient characteristics relate to practice-based social service use. Negative binomial models with inverse probability of treatment weights (N = 4893) estimate adjusted incidence rate ratios for ambulatory, emergency, and inpatient service use among those who used social services compared to those who did not.

RESULTS

Forty-five percent of patients used primary care-based social needs services. This use was significantly greater among those with disabling or complex medical conditions than those without (adjusted odds ratio and 95% confidence interval (CI) of 9.81 [7.39-13.01] and 2.76 [2.44-3.13], respectively); those from low-income versus high-income backgrounds (1.40 [1.21-1.61]); and Blacks and Latinos than Whites (1.33 [1.09-1.62] and 1.29 [1.05-1.59], respectively). Patients who used social services subsequently utilized ambulatory, emergency, and inpatient services at significantly higher rates than those who did not (adjusted incidence rate ratios and 95% CI of 1.54 [1.45-1.63], 1.50 [1.36-1.65], and 3.23 [2.31-4.51], respectively).

CONCLUSIONS

Primary care-based social needs service use was associated with increased utilization of ambulatory services without reductions in emergency or inpatient admissions. This pattern suggests increased health care needs or access and could have payment model-dependent financial implications for practices.

Screening and Interventions for Social Risk Factors: Technical Brief to Support the US Preventive Services Task Force

IMPORTANCE

Evidence-based guidance is limited on how clinicians should screen for social risk factors and which interventions related to these risk factors improve health outcomes.

OBJECTIVE

To describe research on screening and interventions for social risk factors to inform US Preventive Services Task Force considerations of the implications for its portfolio of recommendations.

DATA SOURCES

Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, Ovid MEDLINE, Sociological Abstracts, and Social Services Abstracts (through 2018); Social Interventions Research and Evaluation Network evidence library (January 2019 through May 2021); surveillance through May 21, 2021; interviews with 17 key informants.

STUDY SELECTION

Individual-level and health care system-level interventions with a link to the health care system that addressed at least 1 of 7 social risk domains: housing instability, food insecurity, transportation difficulties, utility needs, interpersonal safety, education, and financial strain.

DATA EXTRACTION AND SYNTHESIS

One investigator abstracted data from studies and a second investigator evaluated data abstractions for completeness and accuracy; key informant interviews were recorded, transcribed, summarized, and integrated with evidence from the literature; narrative synthesis with supporting tables and figures.

MAIN OUTCOMES AND MEASURES

Validity of multidomain social risk screening tools; all outcomes reported for social risk-related interventions; challenges or unintended consequences of screening and interventions.

RESULTS

Many multidomain social risk screening tools have been developed, but they vary widely in their assessment of social risk and few have been validated. This technical brief identified 106 social risk intervention studies (N = 5 978 596). Of the interventions studied, 73 (69%; n = 127 598) addressed multiple social risk domains. The most frequently addressed domains were food insecurity (67/106 studies [63%], n = 141 797), financial strain (52/106 studies [49%], n = 111 962), and housing instability (63/106 studies [59%], n = 5 881 222). Food insecurity, housing instability, and transportation difficulties were identified by key informants as the most important social risk factors to identify in health care. Thirty-eight studies (36%, n = 5 850 669) used an observational design with no comparator, and 19 studies (18%, n = 15 205) were randomized clinical trials. Health care utilization measures were the most commonly reported outcomes in the 68 studies with a comparator (38 studies [56%], n = 111 102). The literature and key informants described many perceived or potential challenges to implementation of social risk screening and interventions in health care.

CONCLUSIONS AND RELEVANCE

Many interventions to address food insecurity, financial strain, and housing instability have been studied, but more randomized clinical trials that report health outcomes from social risk screening and intervention are needed to guide widespread implementation in health care.

Primary care-based interventions to address the financial needs of patients experiencing poverty: a scoping review of the literature

BACKGROUND

It is broadly accepted that poverty is associated with poor health, and the health impact of poverty has been explored in numerous high-income country settings. There is a large and growing body of evidence of the role that primary care practitioners can play in identifying poverty as a health determinant, and in interventions to address it.

PURPOSE OF STUDY

This study maps the published peer-reviewed and grey literature on primary care setting interventions to address poverty in high-income countries in order to identify key concepts and gaps in the research. This scoping review seeks to map the tools in use to identify and address patients' economic needs; describe the key types of primary care-based interventions; and examine barriers and facilitators to successful implementation.

METHODS

Using a scoping review methodology, we searched five databases, the grey literature and the reference lists of relevant studies to identify studies on interventions to address the economic needs-related social determinants of health that occur in primary health care delivery settings, in high-income countries. Findings were synthesized narratively, and examined using thematic analysis, according to iteratively identified themes.

RESULTS

Two hundred and fourteen papers were included in the review and fell into two broad categories of description and evaluation: screening tools, and economic needs-specific interventions. Primary care-based interventions that aim to address patients' financial needs operate at all levels, from passive sociodemographic data collection upon patient registration, through referral to external services, to direct intervention in addressing patients' income needs.

CONCLUSION

Tools and processes to identify and address patients' economic social needs range from those tailored to individual health practices, or addressing one specific dimension of need, to wide-ranging protocols. Primary care-based interventions to address income needs operate at all levels, from passive sociodemographic data collection, through referral to external services, to direct intervention. Measuring success has proven challenging. The decision to undertake this work requires courage on the part of health care providers because it can be difficult, time-consuming and complex. However, it is often appreciated by patients, even when the scope of action available to health care providers is quite narrow.

The Case for Whole-Person Integrative Care

Rationale: There is a need for medicine to deliver more whole-person care. This is a narrative review of several models of whole-person care and studies that illustrate the business case for whole-person models in primary care. Objectives: To provide an overview of what whole-person care models exist and explore evidence to support these models. Study Selection: Representative whole-person care models widely used in the United States are summarized and evaluated. Selected studies focused on outpatient primary care with examples from programs that integrate the delivery of conventional medical care, complementary and alternative medicine, and self-care within the context of social and cultural environments. Methods: Pubmed search conducted December 2020-February 2021. Two iterative searches using terms for "Whole Health Veterans Administration," "integrative medicine," "integrative health," "complementary and alternative medicine," and, as they related to the outcomes, of "health outcomes," "cost-effectiveness," "cost reduction," "patient satisfaction," and "physician satisfaction." Additional studies were identified from an initial search and the authors' experience of over 50 years. We looked for studies of whole-person care used in general primary care, those not using a single modality and only from United States practices. Results: A total of 125 (out of 1746) studies were found and met our inclusion criteria. We found that whole-person models of primary care exist, are quite heterogeneous in their approaches, and routinely report substantial benefits for improving the patient experience, clinical outcomes and in reducing costs. Conclusions: Evidence for the benefit of whole-person care models exist but definitions are quite heterogenous and unfocused. There is a need for more standardization of whole-person models and more research using whole systems approaches rather than reductionistic attempts using isolated components.

Linking Underserved Patients to Social Services Using the SScAR Tool

BACKGROUND

The need to screen for social determinants of health (SDOH) is well documented in the literature. Addressing SDOH also requires linking patients to available social service resources.

OBJECTIVE

This Social Screen Assess and Refer (SScAR) Quality Improvement (QI) project purpose was threefold: (1) develop an agency specific tool to screen for SODH; (2) implement this tool in all new patient evaluations; and (3) provide referral to patients for necessary social services.

METHODS

The SScAR tool adapted questions from published screening tools and created new questions centered on available social services. A pilot study implemented the tool. Documentation of social needs before the pilot, SScAR utilization during the pilot, number of social needs, and referrals made were measured.

RESULTS

Documentation of SDOH screening significantly improved with the tool (α .05, χ2(1) = 21.85, p < .001). Follow-through referral to specific social resources only increased by 5%.

CONCLUSIONS

The SScAR is a feasible tool for identifying SDOH needs and creates a mechanism for linking patients with social services.

IMPLICATIONS FOR NURSING

Future studies must measure social service referrals, utilization of social services, and the resulting impact on specific health outcomes such as blood pressure, hemoglobin A1c, and cholesterol.

Bridge to Health/ Puente a la Salud: Rationale and design of a pilot feasibility randomized trial to address diabetes self-management and unmet basic needs among racial/ethnic minority and low-income patients

Introduction

Racial/ethnic and socioeconomic disparities in diabetes prevalence and management persist. Unmet basic needs such as food insecurity and unstable housing interfere with optimal diabetes self-management. Bridge to Health/Puente a la Salud is a randomized pilot trial designed to examine the feasibility of testing the effectiveness of addressing unmet basic needs via navigation services versus navigation plus diabetes self-management support (DSMS) on improving diabetes-related outcomes among racial/ethnic minority and low-income patients with uncontrolled diabetes.

Material and methods

We recruited and randomized 110 African American, Hispanic, and Medicaid patients (any race/ethnicity) with diabetes and recent hemoglobin A1C ≥ 8% to one of two 6-month interventions: 1) Navigation only; or 2) Navigation + DSMS. In both arms, practice-embedded patient navigators help participants navigate social services and community-based resources to address unmet basic needs. In Navigation + DSMS, participants are also assigned to a community health worker (CHW) embedded in a local community-based organization who provides additional navigation support and delivers DSMS. A1C and unmet basic needs data are collected via routine lab and survey, respectively, at baseline and 6-month follow-up. Qualitative interviews with participants, health system leaders, CHWs, and patient navigators are conducted to explore intervention acceptability and determinants of implementation in a health care setting.

Discussion

Findings from this pilot feasibility study will enhance understanding about acceptability, preliminary clinical effectiveness, and facilitators and barriers to implementation of the Navigation only and Navigation + DSMS interventions and inform refinements of the overall study design for the larger, randomized clinical trial.

The implementation cost of a safety-net hospital program addressing social needs in Atlanta

OBJECTIVE

To describe the cost of integrating social needs activities into a health care program that works toward health equity by addressing socioeconomic barriers.

DATA SOURCES/STUDY SETTING

Costs for a heart failure health care program based in a safety-net hospital were reported by program staff for the program year May 2018-April 2019. Additional data sources included hospital records, invoices, and staff survey.

STUDY DESIGN

We conducted a retrospective, cross-sectional, case study of a program that includes health education, outpatient care, financial counseling and free medication; transportation and home services for those most in need; and connections to other social services. Program costs were summarized overall and for mutually exclusive categories: health care program (fixed and variable) and social needs activities.

DATA COLLECTION

Program cost data were collected using a activity-based, micro-costing approach. In addition, we conducted a survey that was completed by key staff to understand time allocation.

PRINCIPAL FINDINGS

Program costs were approximately $1.33 million, and the annual per patient cost was $1455. Thirty percent of the program costs was for social needs activities: 18% for 30-day supply of medications and addressing socioeconomic barriers to medication adherence, 18% for mobile health services (outpatient home visits), 53% for navigating services through a financial counselor and community health worker, and 12% for transportation to visits and addressing transportation barriers. Most of the program costs were for personnel: 92% of the health care program fixed, 95% of the health care program variable, and 78% of social needs activities.

DISCUSSION

Historically, social and health care services are funded by different systems and have not been integrated. We estimate the cost of implementing social needs activities into a health care program. This work can inform implementation for hospitals attempting to address social determinants of health and social needs in their patient population.

The Direct Clinic-Level Cost of the Implementation and Use of a Protocol to Assess and Address Social Needs in Diverse Community Health Center Primary Care Clinical Settings

PURPOSE

Social determinants of health, including food insecurity, housing instability, social isolation, and unemployment are important drivers of health outcomes and utilization. To inform implementation of social needs screening and response protocols, there is a need to identify the associated costs in routine primary care encounters.

METHODS

We interviewed key stakeholders in four diverse community health centers that had adopted a widely used social needs screening and response protocol. We evaluated costs using an activity-based costing tool across both the initial implementation phase and ongoing maintenance phase.

RESULTS

Clinic costs were associated with workforce development, planning, and electronic health record integration. These initial implementation costs varied by site ($6,644-$49,087). On a per-patient basis, ongoing maintenance costs ranged from $9.76 to $47.98.

CONCLUSION

Our findings can aid in designing reimbursement mechanisms tied to social needs screening and response to accelerate translational efforts and promote health equity.

Using natural language processing to classify social work interventions

OBJECTIVES

Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activities in health care settings are largely captured as text data within electronic health records (EHRs), making measurement and analysis difficult. This study aims to extract and classify, from EHR notes, interventions intended to address patients' social needs using natural language processing (NLP) and machine learning (ML) algorithms.

STUDY DESIGN

Secondary data analysis of a longitudinal cohort.

METHODS

We extracted 815 SW encounter notes from the EHR system of a federally qualified health center. We reviewed the literature to derive a 10-category classification scheme for SW interventions. We applied NLP and ML algorithms to categorize the documented SW interventions in EHR notes according to the 10-category classification scheme.

RESULTS

Most of the SW notes (n = 598; 73.4%) contained at least 1 SW intervention. The most frequent interventions offered by social workers included care coordination (21.5%), education (21.0%), financial planning (18.5%), referral to community services and organizations (17.1%), and supportive counseling (15.3%). High-performing classification algorithms included the kernelized support vector machine (SVM) (accuracy, 0.97), logistic regression (accuracy, 0.96), linear SVM (accuracy, 0.95), and multinomial naive Bayes classifier (accuracy, 0.92).

CONCLUSIONS

NLP and ML can be utilized for automated identification and classification of SW interventions documented in EHRs. Health care administrators can leverage this automated approach to gain better insight into the most needed social interventions in the patient population served by their organizations. Such information can be applied in managerial decisions related to SW staffing, resource allocation, and patients' social needs.

Assessing the Impact of Social Needs and Social Determinants of Health on Health Care Utilization: Using Patient- and Community-Level Data

As the US health care system moves to expand access to and quality of medical care, the importance of addressing patient-level social needs and community-level social determinants of health (SDOH) is increasingly being recognized. This study evaluates individual- and community-level needs of housing (one of the SDOH domains) across the patient population of an academic medical center and explores how the level of housing needs impacts health care utilization. The authors performed a descriptive analysis of housing issues identified in both structured and unstructured (eg, clinical notes) data extracted from the electronic health record (EHR) and compared this to community-level characteristics of patients' neighborhood as measured by the Area Deprivation Index. Multivariate analyses were performed to assess the association between these and other factors on the frequency of service encounters. Among the 1,034,683 study participants, 59,703 (5.8%) had at least 1 housing issue identified in their EHR from structured or unstructured data combined. After adjusting for other factors, patients with housing instability and homelessness had 49% and 34% more encounters with the health care system compared to patients without housing issues (P < 0.00001). Patients living in the most disadvantaged neighborhoods had 55% more encounters with the health care system compared to those living in the most advantaged neighborhoods (P < 0.00001). This data collection approach and findings can inform health care systems aiming to make use of their EHRs and community-level SDOH information to provide a full assessment of patients' social needs and challenges.

Role of Supplemental Nutrition Assistance Program in Improving Diet Quality: Implications for Nurses

The Supplemental Nutrition Assistance Program (SNAP) provides access to healthy food for low-income individuals and households. Food security, however, does not necessarily achieve higher diet quality for beneficiaries. Diet quality is an important consideration for the development and management of chronic illness, a significant public health concern. In this study, we review incentives and disincentives implemented to improve the diet quality, the evidence on SNAP including benefits, challenges, and the politics of funding. New interventions and policies will be needed in order to improve the overall diet quality of SNAP households. SNAP should align with nutritional science to meet national public health goals. Nurses are trusted advocates for patients and the public and are uniquely positioned to aid in this effort. Informed by evidence, nurses willing to leverage their influence, can lead this needed change.

Financing Diabetes Care in the U.S. Health System: Payment Innovations for Addressing the Medical and Social Determinants of Health

PURPOSE OF REVIEW

Review innovations in health care financing promoting health system investments in addressing medical and social determinants of health (SDH) for patients with diabetes.

RECENT FINDINGS

Particular payment models implemented in the public and private sectors increasingly offer flexibility in health care organizations (HCOs) to allocate resources towards helping patients with diabetes overcome the medical and socio-economic problems driving poor population and individual health. The barriers imposed by the traditional fee-for-service (FFS) payment model to incorporating SDH into health care delivery across the health system are being overcome with new payment approaches rewarding the quality of care provided rather than strictly the volume of health services rendered. Evidence suggests health care financing changes will facilitate the realization of health reform goals to provide the right care to the right people at the right time through the expansion of the role of integrated care teams that can address patients' medical and health-related social needs.

Study protocol: a pragmatic, stepped-wedge trial of tailored support for implementing social determinants of health documentation/action in community health centers, with realist evaluation

BACKGROUND

National leaders recommend documenting social determinants of health and actions taken to address social determinants of health in electronic health records, and a growing body of evidence suggests the health benefits of doing so. However, little evidence exists to guide implementation of social determinants of health documentation/action.

METHODS

This paper describes a 5-year, mixed-methods, stepped-wedge trial with realist evaluation, designed to test the impact of providing 30 community health centers with step-by-step guidance on implementing electronic health record-based social determinants of health documentation. This guidance will entail 6 months of tailored support from an interdisciplinary team, including training and technical assistance. We will report on tailored support provided at each of five implementation steps; impact of tailored implementation support; a method for tracking such tailoring; and context-specific pathways through which these tailored strategies effect change. We will track the competencies and resources needed to support the study clinics' implementation efforts.

DISCUSSION

Results will inform how to tailor implementation strategies to meet local needs in real-world practice settings. Secondary analyses will assess impacts of social determinants of health documentation and referral-making on diabetes outcomes. By learning whether and how scalable, tailored implementation strategies help community health centers adopt social determinants of health documentation and action, this study will yield timely guidance to primary care providers. We are not aware of previous studies exploring implementation strategies that support adoption of social determinants of action using electronic health and interventions, despite the pressing need for such guidance.

TRIAL REGISTRATION

clinicaltrials.gov, NCT03607617 , registration date: 7/31/2018-retrospectively registered.