Variation in Electronic Health Record Documentation of Social Determinants of Health Across a National Network of Community Health Centers

Centering the role of community health workers in social risk screening, referral, and follow-up within the primary care setting

BACKGROUND

Community health workers (CHWs) remain an underutilized resource in social risk diagnostics in the primary care setting. This process evaluation study seeks to assess the role of CHWs in social risk screening, referral, and follow-up through process mapping to identify barriers to the process for future quality improvement efforts.

METHODS

Researchers at the Arizona Prevention Research Center (AzPRC) engaged with two Federally Qualified Health Centers (FQHCs) in two of Arizona's major urban areas to evaluate their internal processes for social risk screening and intervention. The Consolidated Framework for Implementation Research (CFIR) was used to direct a process mapping exercise to visually describe the workflow, gaps, and barriers to identifying and addressing social risk.

RESULTS

The process unveiled key areas for health system improvements in the community setting, the organizational setting, and in the implementation of social risk screening, referral, and follow-up. Further, process maps highlight the potential resources needed for effective CHW integration to address social risk in the primary care setting.

CONCLUSIONS

Our findings demonstrate the importance of organizational tools, such as process mapping, to assist primary care settings in evaluating internal processes for quality improvement in addressing social risk and in effectively integrating the CHW workforce. Subsequent research will evaluate rates of social risk screening, referral, and follow-up within all of Arizona's FQHCs and propose models for CHW integration to address social risk in primary care and strengthen social risk screening reach and effectiveness.

Classifying Individuals With Rheumatic Conditions as Financially Insecure Using Electronic Health Record Data and Natural Language Processing: Algorithm Derivation and Validation

OBJECTIVE

We aimed to examine the feasibility of applying natural language processing (NLP) to unstructured electronic health record (EHR) documents to detect the presence of financial insecurity among patients with rheumatologic disease enrolled in an integrated care management program (iCMP).

METHODS

We incorporated supervised, rule-based NLP and statistical methods to identify financial insecurity among patients with rheumatic conditions enrolled in an iCMP (n = 20,395) in a multihospital EHR system. We constructed a lexicon for financial insecurity using data from available knowledge sources and then reviewed EHR notes from 538 randomly selected individuals (training cohort n = 366, validation cohort n = 172). We manually categorized records as having "definite," "possible," or "no" mention of financial insecurity. All available notes were processed using Narrative Information Linear Extraction, a rule-based version of NLP. Models were trained using the NLP features for financial insecurity using logistic, least absolute shrinkage operator (LASSO), and random forest performance characteristic and were compared with the reference standard.

RESULTS

A total of 245,142 notes were processed from 538 individual patient records. Financial insecurity was present among 100 (27%) individuals in the training cohort and 63 (37%) in the validation cohort. The LASSO and random forest models performed identically and slightly better than logistic regression, with positive predictive values of 0.90, sensitivities of 0.29, and specificities of 0.98.

CONCLUSION

The development of a context-driven lexicon used with rule-based NLP to extract data that identify financial insecurity is feasible for use and improved the capture for presence of financial insecurity with high accuracy. In the absence of a standard lexicon and construct definition for financial insecurity status, additional studies are needed to optimize the sensitivity of algorithms to categorize financial insecurity with construct validity.

Association between Services to Address Adverse Social Determinants of Health and Suicide Mortality among Veterans with Indicators of Housing Instability, Unemployment, and Justice Involvement

Suicide among Veterans continues to be a priority issue addressed by the U.S. Department of Veterans Affairs (VA). In addition to a variety of services specifically intended to prevent suicide, VA also offers a number of services to address Veterans' social determinants of health (SDH), several of which may be associated with elevated risk for suicide. For the present study, we assessed whether participation in services to address adverse SDH is associated with a reduction in risk of suicide mortality among Veterans using secondary data from VA datasets (1/1/2014-12/31/2019) for Veterans with an indicator of housing instability, unemployment, or justice involvement. Logistic regressions modeled suicide mortality; use of services to address SDH was the primary predictor. There was not a statistically significant association between services use and suicide mortality; significant correlates included race other than African American, low or no compensation related to disability incurred during military service, and suicidal ideation/attempt during observation period. Suicide is a complex outcome, difficult to predict, and likely the result of many factors; while there is not a consistent association between services use related to adverse SDH and suicide mortality, providers should intervene with Veterans who do not engage in SDH-focused services but have risk factors for suicide mortality.

A Qualitative Study of Physicians' Views on the Reuse of Electronic Health Record Data for Secondary Analysis

Electronic health records (EHRs) have become ubiquitous in clinical practice. Given the rich biomedical data captured for a large panel of patients, secondary analysis of these data for health research is also commonplace. Yet, there are many caveats to EHR data that the researchers must be aware of, such as the accuracy of and motive for documentation, and the reason for patients' visits to the clinic. The clinician-the author of the documentation-is thus central to the correct interpretation of EHR data for research purposes. In this study, I interviewed 11 physicians in various clinical specialties to bring attention to their view on the validity of research using EHR data. Qualitative, in-depth, one-on-one interviews were conducted with practicing physicians in inpatient and outpatient medicine. Content analysis using a data-driven, inductive approach to identify themes related to challenges and opportunities in the reuse of EHR data for secondary analysis generated seven themes. Themes that reflected challenges of EHRs for research included (1) audience, (2) accuracy of data, (3) availability of data, (4) documentation practices, and (5) representativeness. Themes that reflected opportunities of EHRs for research included (6) endorsement and (7) enablers. The greatest perceived barriers reflected the intended audience of the EHR, the interpretation and meaning of the data, and the quality of the data for research purposes. Physicians generally expressed more perceived challenges than opportunities in the reuse of EHR data for research purposes; however, they remained optimistic.

The Health Equity Explorer: An open-source resource for distributed health equity visualization and research across common data models

Introduction

There is an urgent need to address pervasive inequities in health and healthcare in the USA. Many areas of health inequity are well known, but there remain important unexplored areas, and for many populations in the USA, accessing data to visualize and monitor health equity is difficult.

Methods

We describe the development and evaluation of an open-source, R-Shiny application, the "Health Equity Explorer (H2E)," designed to enable users to explore health equity data in a way that can be easily shared within and across common data models (CDMs).

Results

We have developed a novel, scalable informatics tool to explore a wide variety of drivers of health, including patient-reported Social Determinants of Health (SDoH), using data in an OMOP CDM research data repository in a way that can be easily shared. We describe our development process, data schema, potential use cases, and pilot data for 705,686 people who attended our health system at least once since 2016. For this group, 996,382 unique observations for questions related to food and housing security were available for 324,630 patients (at least one answer for all 46% of patients) with 65,152 (20.1% of patients with at least one visit and answer) reporting food or housing insecurity at least once.

Conclusions

H2E can be used to support dynamic and interactive explorations that include rich social and environmental data. The tool can support multiple CDMs and has the potential to support distributed health equity research and intervention on a national scale.

Evaluation of the Predictive Value of Routinely Collected Health-Related Social Needs Measures

The objective was to assess the value of routinely collected patient-reported health-related social needs (HRSNs) measures for predicting utilization and health outcomes. The authors identified Mayo Clinic patients with cancer, diabetes, or heart failure. The HRSN measures were collected as part of patient-reported screenings from June to December 2019 and outcomes (hospitalization, 30-day readmission, and death) were ascertained in 2020. For each outcome and disease combination, 4 models were used: gradient boosting machine (GBM), random forest (RF), generalized linear model (GLM), and elastic net (EN). Other predictors included clinical factors, demographics, and area-based HRSN measures-area deprivation index (ADI) and rurality. Predictive performance for models was evaluated with and without the routinely collected HRSN measures as change in area under the curve (AUC). Variable importance was also assessed. The differences in AUC were mixed. Significant improvements existed in 3 models of death for cancer (GBM: 0.0421, RF: 0.0496, EN: 0.0428), 3 models of hospitalization (GBM: 0.0372, RF: 0.0640, EN: 0.0441), and 1 of death (RF: 0.0754) for diabetes, and 1 model of readmissions (GBM: 0.1817), and 3 models of death (GBM: 0.0333, RF: 0.0519, GLM: 0.0489) for heart failure. Age, ADI, and the Charlson comorbidity index were the top 3 in variable importance and were consistently more important than routinely collected HRSN measures. The addition of routinely collected HRSN measures resulted in mixed improvement in the predictive performance of the models. These findings suggest that existing factors and the ADI are more important for prediction in these contexts. More work is needed to identify predictors that consistently improve model performance.

The relationship between perceptions of electronic health record usability and clinical importance of social and environmental determinants of health on provider documentation

Social and environmental determinants of health (SEDH) data in the electronic health record (EHR) can be inaccurate and incomplete. Providers are in a unique position to impact this issue as they both obtain and enter this data, however, the variability in screening and documentation practices currently limits the ability to mobilize SEDH data for secondary uses. This study explores whether providers' perceptions of clinical importance of SEDH or EHR usability influenced data entry by analyzing two relationships: (1) provider charting behavior and clinical consideration of SEDH and (2) provider charting behavior and ease of EHR use in charting. We performed a cross-sectional study using an 11-question electronic survey to assess self-reported practices related to clinical consideration of SEDH elements, EHR usability and SEDH documentation of all staff physicians, identified using administrative listserves, at Penn State Health Hershey Medical Center during September to October 2021. A total of 201 physicians responded to and completed the survey out of a possible 2,478 identified staff physicians (8.1% response rate). A five-point Likert scale from "never" to "always" assessed charting behavior and clinical consideration. Responses were dichotomized as consistent/inconsistent and vital/not vital respectively. EHR usability was assessed as "yes" or "no" responses. Fisher's exact tests assessed the relationship between charting behavior and clinical consideration and to compare charting practices between different SEDHs. Cumulative measures were constructed for consistent charting and ease of charting. A generalized linear mixed model (GLMM) compared SDH and EDH with respect to each cumulative measure and was quantified using odds ratios (OR) and 95% confidence intervals (CI). Our results show that provider documentation frequency of an SEDH is associated with perceived clinical utility as well as ease of charting and that providers were more likely to consistently chart on SDH versus EDH. Nuances in these relationships did exist with one notable example comparing the results of smoking (SDH) to infectious disease outbreaks (EDH). Despite similar percentages of physicians reporting that both smoking and infectious disease outbreaks are vital to care, differences in charting consistency and ease of charting between these two were seen. Taken as a whole, our results suggest that SEDH quality optimization efforts cannot consider physician perceptions and EHR usability as siloed entities and that EHR design should not be the only target for intervention. The associations found in this study provide a starting point to understand the complexity in how clinical utility and EHR usability influence charting consistency of each SEDH element, however, further research is needed to understand how these relationships intersect at various levels in the SEDH data optimization process.

Community Health Center Staff Perspectives on Financial Payments for Social Care

Policy Points State and federal payers are actively considering strategies to increase the adoption of social risk screening and interventions in health care settings, including through the use of financial incentives. Activities related to social care in Oregon community health centers (CHCs) provided a unique opportunity to explore whether and how fee-for-service payments for social risk screening and navigation influence CHC activities. CHC staff, clinicians, and administrative leaders were often unaware of existing financial payments for social risk screening and navigation services. As currently designed, fee-for-service payments are unlikely to strongly influence CHC social care practices.

CONTEXT

A growing crop of national policies has emerged to encourage health care delivery systems to ask about and try to address patients' social risks, e.g., food, housing, and transportation insecurity, in care delivery contexts. In this study, we explored how community health center (CHC) staff perceive the current and potential influence of fee-for-service payments on clinical teams' engagement in these activities.

METHODS

We interviewed 42 clinicians, frontline staff, and administrative leaders from 12 Oregon CHC clinical sites about their social care initiatives, including about the role of existing or anticipated financial payments intended to promote social risk screening and referrals to social services. Data were analyzed using both inductive and deductive thematic analysis approaches.

FINDINGS

We grouped findings into three categories: participants' awareness of existing or anticipated financial incentives, uses for incentive dollars, and perceived impact of financial incentives on social care activities in clinical practices. Lack of awareness of existing incentives meant these incentives were not perceived to influence the behaviors of staff responsible for conducting screening and providing referrals. Current or anticipated meaningful uses for incentive dollars included paying for social care staff, providing social services, and supporting additional fundraising efforts. Frontline staff reported that the strongest motivator for clinic social care practices was the ability to provide responsive social services. Clinic leaders/managers noted that for financial incentives to substantively change CHC practices would require payments sizable enough to expand the social care workforce as well.

CONCLUSIONS

Small fee-for-service payments to CHCs for social risk screening and navigation services are unlikely to markedly influence CHC social care practices. Refining the design of financial incentives-e.g., by increasing clinical teams' awareness of incentives, linking screening to well-funded social services, and changing incentive amounts to support social care staffing needs-may increase the uptake of social care practices in CHCs.

Area-level social determinants of health and individual-level social risks: Assessing predictive ability and biases in social risk screening

Introduction

Area-level social determinants of health (SDoH) and individual-level social risks are different, yet area-level measures are frequently used as proxies for individual-level social risks. This study assessed whether demographic factors were associated with patients being screened for individual-level social risks, the percentage who screened positive for social risks, and the association between SDoH and patient-reported social risks in a nationwide network of community-based health centers.

Methods

Electronic health record data from 1,330,201 patients with health center visits in 2021 were analyzed using multilevel logistic regression. Associations between patient characteristics, screening receipt, and screening positive for social risks (e.g., food insecurity, housing instability, transportation insecurity) were assessed. The predictive ability of three commonly used SDoH measures (Area Deprivation Index, Social Deprivation Index, Material Community Deprivation Index) in identifying individual-level social risks was also evaluated.

Results

Of 244,155 (18%) patients screened for social risks, 61,414 (25.2%) screened positive. Sex, race/ethnicity, language preference, and payer were associated with both social risk screening and positivity. Significant health system-level variation in both screening and positivity was observed, with an intraclass correlation coefficient of 0.55 for social risk screening and 0.38 for positivity. The three area-level SDoH measures had low accuracy, sensitivity, and area under the curve when used to predict individual social needs.

Conclusion

Area-level SDoH measures may provide valuable information about the communities where patients live. However, policymakers, healthcare administrators, and researchers should exercise caution when using area-level adverse SDoH measures to identify individual-level social risks.

Finding meaning: a realist-informed perspective on social risk screening and relationships as mechanisms of change

Background

Social risk screening rates in many US primary care settings remain low. This realist-informed evaluation explored the mechanisms through which a tailored coaching and technical training intervention impacted social risk screening uptake in 26 community clinics across the United States.

Methods

Evaluation data sources included the documented content of interactions between the clinics and implementation support team and electronic health record (EHR) data. Following the realist approach, analysis was composed of iterative cycles of developing, testing and refining program theories about how the intervention did-or didn't-work, for whom, under what circumstances. Normalization Process Theory was applied to the realist program theories to enhance the explanatory power and transferability of the results.

Results

Analysis identified three overarching realist program theories. First, clinic staff perceptions about the role of standardized social risk screening in person-centered care-considered "good" care and highly valued-strongly impacted receptivity to the intervention. Second, the physicality of the intervention materials facilitated collaboration and impacted clinic leaders' perception of the legitimacy of the social risk screening implementation work. Third, positive relationships between the implementation support team members, between the support team and clinic champions, and between clinic champions and staff motivated and inspired clinic staff to engage with the intervention and to tailor workflows to their settings' needs. Study clinics did not always exhibit the social risk screening patterns anticipated by the program theories due to discrepant definitions of success between clinic staff (improved ability to provide contextualized, person-centered care) and the trial (increased rates of EHR-documented social risk screening). Aligning the realist program theories with Normalization Process Theory constructs clarified that the intervention as implemented emphasized preparation over operationalization and appraisal, providing insight into why the intervention did not successfully embed sustained systematic social risk screening in participating clinics.

Conclusion

The realist program theories highlighted the effectiveness and importance of intervention components and implementation strategies that support trusting relationships as mechanisms of change. This may be particularly important in social determinants of health work, which requires commitment and humility from health care providers and vulnerability on the part of patients.

Food Insecurity Screening in Primary Care: Patterns During the COVID-19 Pandemic by Encounter Modality

INTRODUCTION

Screening for food insecurity in clinical settings is recommended, but implementation varies widely. This study evaluated the prevalence of screening for food insecurity and other social risks in telehealth versus in-person encounters during the COVID-19 pandemic and changes in screening before versus after widespread COVID-19 vaccine availability.

METHODS

These cross-sectional analyses used electronic health record and ancillary clinic data from a national network of 400+ community health centers with a shared electronic health record. Food insecurity screening was characterized in 2022 in a sample of 275,465 first encounters for routine primary care at any network clinic during March 11, 2020-December 31, 2021. An adjusted multivariate multilevel probit model estimated screening prevalence on the basis of encounter mode (in-person versus telehealth) and time period (initial pandemic versus after vaccine availability) in a random subsample of 11,000 encounters.

RESULTS

Encounter mode was related to food insecurity screening (p<0.0001), with an estimated 9.2% screening rate during in-person encounters, compared with 5.1% at telehealth encounters. There was an interaction between time period and encounter mode (p<0.0001), with higher screening prevalence at in-person versus telehealth encounters after COVID-19 vaccines were available (11.7% vs 4.9%) than before vaccines were available (7.8% vs 5.2%).

CONCLUSIONS

Food insecurity screening in first primary care encounters is low overall, with lower rates during telehealth visits and the earlier phase of the COVID-19 pandemic. Future research should explore the methods for enhancing social risk screening in telehealth encounters.

Predictors and consequences of homelessness in whole-population observational studies that used administrative data: a systematic review

BACKGROUND

Homelessness is a complex societal and public health challenge. Limited information exists about the population-level health and social care-related predictors and consequences of persons with lived experience of homelessness (PEH). Studies that focus on population subgroups or ad hoc questionnaires to gather data are of relatively limited generalisability to whole-population health surveillance and planning. The aim of this study was to find and synthesise information about the risk factors for, and consequences of, experiencing homelessness in whole-population studies that used routine administrative data.

METHOD

We performed a systematic search using EMBASE, MEDLINE, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL) and PsycINFO research databases for English-language studies published from inception until February 2023 that reported analyses of administrative data about homelessness and health and social care-related predictors and consequences. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

RESULTS

Of the 1224 articles reviewed, 30 publications met the inclusion criteria. The included studies examined a wide range of topic areas, and the homelessness definitions used in each varied considerably. Studies were categorised into several topic areas: Mortality, morbidity and COVID-19; health care usage and hospital re-admission; care home admission and shelter stay; and other (e.g. employment, crime victimisation). The studies reported that that the physical and mental health of people who experience homelessness was worse than that of the general population. Homeless individuals were more likely to have higher risk of hospitalisation, more likely to use emergency departments, have higher mortality rates and were at greater risk of needing intensive care or of dying from COVID-19 compared with general population. Additionally, homeless individuals were more likely to be incarcerated or unemployed. The effects were strongest for those who experienced being homeless as a child compared to those who experienced being homeless later on in life.

CONCLUSIONS

This is the first systematic review of whole-population observational studies that used administrative data to identify causes and consequences associated with individuals who are experiencing homelessness. While the scientific literature provides evidence on some of the possible risk factors associated with being homeless, research into this research topic has been limited and gaps still remain. There is a need for more standardised best practice approaches to understand better the causes and consequences associated with being homeless.

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.

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.

Co-Occurrence of Social Risk Factors and Associated Outcomes in Patients With Heart Failure

Background Among patients with heart failure (HF), social risk factors (SRFs) are associated with poor outcomes. However, less is known about how co-occurrence of SRFs affect all-cause health care utilization for patients with HF. The objective was to address this gap using a novel approach to classify co-occurrence of SRFs. Methods and Results This was a cohort study of residents living in an 11-county region of southeast Minnesota, aged ≥18 years with a first-ever diagnosis for HF between January 2013 and June 2017. SRFs, including education, health literacy, social isolation, and race and ethnicity, were obtained via surveys. Area-deprivation index and rural-urban commuting area codes were determined from patient addresses. Associations between SRFs and outcomes (emergency department visits and hospitalizations) were assessed using Andersen-Gill models. Latent class analysis was used to identify subgroups of SRFs; associations with outcomes were examined. A total of 3142 patients with HF (mean age, 73.4 years; 45% women) had SRF data available. The SRFs with the strongest association with hospitalizations were education, social isolation, and area-deprivation index. We identified 4 groups using latent class analysis, with group 3, characterized by more SRFs, at increased risk of emergency department visits (hazard ratio [HR], 1.33 [95% CI, 1.23-1.45]) and hospitalizations (HR, 1.42 [95% CI, 1.28-1.58]). Conclusions Low educational attainment, high social isolation, and high area-deprivation index had the strongest associations. We identified meaningful subgroups with respect to SRFs, and these subgroups were associated with outcomes. These findings suggest that it is possible to apply latent class analysis to better understand the co-occurrence of SRFs among patients with HF.

Sex and gender in infection and immunity: addressing the bottlenecks from basic science to public health and clinical applications

Although sex and gender are recognized as major determinants of health and immunity, their role is rarely considered in clinical practice and public health. We identified six bottlenecks preventing the inclusion of sex and gender considerations from basic science to clinical practice, precision medicine and public health policies. (i) A terminology-related bottleneck, linked to the definitions of sex and gender themselves, and the lack of consensus on how to evaluate gender. (ii) A data-related bottleneck, due to gaps in sex-disaggregated data, data on trans/non-binary people and gender identity. (iii) A translational bottleneck, limited by animal models and the underrepresentation of gender minorities in biomedical studies. (iv) A statistical bottleneck, with inappropriate statistical analyses and results interpretation. (v) An ethical bottleneck posed by the underrepresentation of pregnant people and gender minorities in clinical studies. (vi) A structural bottleneck, as systemic bias and discriminations affect not only academic research but also decision makers. We specify guidelines for researchers, scientific journals, funding agencies and academic institutions to address these bottlenecks. Following such guidelines will support the development of more efficient and equitable care strategies for all.

Racial and Ethnic Disparities in Opioid Access and Urine Drug Screening Among Older Patients With Poor-Prognosis Cancer Near the End of Life

PURPOSE

To characterize racial and ethnic disparities and trends in opioid access and urine drug screening (UDS) among patients dying of cancer, and to explore potential mechanisms.

METHODS

Among 318,549 non-Hispanic White (White), Black, and Hispanic Medicare decedents older than 65 years with poor-prognosis cancers, we examined 2007-2019 trends in opioid prescription fills and potency (morphine milligram equivalents [MMEs] per day [MMEDs]) near the end of life (EOL), defined as 30 days before death or hospice enrollment. We estimated the effects of race and ethnicity on opioid access, controlling for demographic and clinical factors. Models were further adjusted for socioeconomic factors including dual-eligibility status, community-level deprivation, and rurality. We similarly explored disparities in UDS.

RESULTS

Between 2007 and 2019, White, Black, and Hispanic decedents experienced steady declines in EOL opioid access and rapid expansion of UDS. Compared with White patients, Black and Hispanic patients were less likely to receive any opioid (Black, -4.3 percentage points, 95% CI, -4.8 to -3.6; Hispanic, -3.6 percentage points, 95% CI, -4.4 to -2.9) and long-acting opioids (Black, -3.1 percentage points, 95% CI, -3.6 to -2.8; Hispanic, -2.2 percentage points, 95% CI, -2.7 to -1.7). They also received lower daily doses (Black, -10.5 MMED, 95% CI, -12.8 to -8.2; Hispanic, -9.1 MMED, 95% CI, -12.1 to -6.1) and lower total doses (Black, -210 MMEs, 95% CI, -293 to -207; Hispanic, -179 MMEs, 95% CI, -217 to -142); Black patients were also more likely to undergo UDS (0.5 percentage points; 95% CI, 0.3 to 0.8). Disparities in EOL opioid access and UDS disproportionately affected Black men. Adjustment for socioeconomic factors did not attenuate the EOL opioid access disparities.

CONCLUSION

There are substantial and persistent racial and ethnic inequities in opioid access among older patients dying of cancer, which are not mediated by socioeconomic variables.

Screening Health-Related Social Needs in Hospitals: A Systematic Review of Health Care Professional and Patient Perspectives

Health outcomes are markedly influenced by health-related social needs (HRSN) such as food insecurity and housing instability. Under new Joint Commission requirements, hospitals have recently increased attention to HRSN to reduce health disparities. To evaluate prevailing attitudes and guide hospital efforts, the authors conducted a systematic review to describe patients' and health care providers' perceptions related to screening for and addressing patients' HRSN in US hospitals. Articles were identified through PubMed and by expert recommendations, and synthesized by relevance of findings and basic study characteristics. The review included 22 articles, which showed that most health care providers believed that unmet social needs impact health and that screening for HRSN should be a standard part of hospital care. Notable differences existed between perceived importance of HRSN and actual screening rates, however. Patients reported high receptiveness to screening in hospital encounters, but cautioned to avoid stigmatization and protect privacy when screening. Limited knowledge of resources available, lack of time, and lack of actual resources were the most frequently reported barriers to screening for HRSN. Hospital efforts to screen and address HRSN will likely be facilitated by stakeholders' positive perceptions, but common barriers to screening and referral will need to be addressed to effectively scale up efforts and impact health disparities.

Automatic extraction of social determinants of health from medical notes of chronic lower back pain patients

OBJECTIVE

We applied natural language processing and inference methods to extract social determinants of health (SDoH) information from clinical notes of patients with chronic low back pain (cLBP) to enhance future analyses of the associations between SDoH disparities and cLBP outcomes.

MATERIALS AND METHODS

Clinical notes for patients with cLBP were annotated for 7 SDoH domains, as well as depression, anxiety, and pain scores, resulting in 626 notes with at least one annotated entity for 364 patients. We used a 2-tier taxonomy with these 10 first-level classes (domains) and 52 second-level classes. We developed and validated named entity recognition (NER) systems based on both rule-based and machine learning approaches and validated an entailment model.

RESULTS

Annotators achieved a high interrater agreement (Cohen's kappa of 95.3% at document level). A rule-based system (cTAKES), RoBERTa NER, and a hybrid model (combining rules and logistic regression) achieved performance of F1 = 47.1%, 84.4%, and 80.3%, respectively, for first-level classes.

DISCUSSION

While the hybrid model had a lower F1 performance, it matched or outperformed RoBERTa NER model in terms of recall and had lower computational requirements. Applying an untuned RoBERTa entailment model, we detected many challenging wordings missed by NER systems. Still, the entailment model may be sensitive to hypothesis wording.

CONCLUSION

This study developed a corpus of annotated clinical notes covering a broad spectrum of SDoH classes. This corpus provides a basis for training machine learning models and serves as a benchmark for predictive models for NER for SDoH and knowledge extraction from clinical texts.

Association between Estimated Cardiorespiratory Fitness and Abnormal Glucose Risk: A Cohort Study

BACKGROUND

Cardiorespiratory fitness (CRF) is a predictor of chronic disease that is impractical to routinely measure in primary care settings. We used a new estimated cardiorespiratory fitness (eCRF) algorithm that uses information routinely documented in electronic health care records to predict abnormal blood glucose incidence.

METHODS

Participants were adults (17.8% female) 20-81 years old at baseline from the Aerobics Center Longitudinal Study between 1979 and 2006. eCRF was based on sex, age, body mass index, resting heart rate, resting blood pressure, and smoking status. CRF was measured by maximal treadmill testing. Cox proportional hazards regression models were established using eCRF and CRF as independent variables predicting the abnormal blood glucose incidence while adjusting for covariates (age, sex, exam year, waist girth, heavy drinking, smoking, and family history of diabetes mellitus and lipids).

RESULTS

Of 8602 participants at risk at baseline, 3580 (41.6%) developed abnormal blood glucose during an average of 4.9 years follow-up. The average eCRF of 12.03 ± 1.75 METs was equivalent to the CRF of 12.15 ± 2.40 METs within the 10% equivalence limit. In fully adjusted models, the estimated risks were the same (HRs = 0.96), eCRF (95% CIs = 0.93-0.99), and CRF (95% CI of 0.94-0.98). Each 1-MET increase was associated with a 4% reduced risk.

CONCLUSIONS

Higher eCRF is associated with a lower risk of abnormal glucose. eCRF can be a vital sign used for research and prevention.

Implementation Support for a Social Risk Screening and Referral Process in Community Health Centers

Evidence is needed about how to effectively support health care providers in implementing screening for social risks (adverse social determinants of health) and providing related referrals meant to address identified social risks. This need is greatest in underresourced care settings. The authors tested whether an implementation support intervention (6 months of technical assistance and coaching study clinics through a five-step implementation process) improved adoption of social risk activities in community health centers (CHCs). Thirty-one CHC clinics were block-randomized to six wedges that occurred sequentially. Over the 45-month study period from March 2018 to December 2021, data were collected for 6 or more months preintervention, the 6-month intervention period, and 6 or more months postintervention. The authors calculated clinic-level monthly rates of social risk screening results that were entered at in-person encounters and rates of social risk-related referrals. Secondary analyses measured impacts on diabetes-related outcomes. Intervention impact was assessed by comparing clinic performance based on whether they had versus had not yet received the intervention in the preintervention period compared with the intervention and postintervention periods. In assessing the results, the authors note that five clinics withdrew from the study for various bandwidth-related reasons. Of the remaining 26, a total of 19 fully or partially completed all 5 implementation steps, and 7 fully or partially completed at least the first 3 steps. Social risk screening was 2.45 times (95% confidence interval [CI], 1.32-4.39) higher during the intervention period compared with the preintervention period; this impact was not sustained postintervention (rate ratio, 2.16; 95% CI, 0.64-7.27). No significant difference was seen in social risk referral rates during the intervention or postintervention periods. The intervention was associated with greater blood pressure control among patients with diabetes and lower rates of diabetes biomarker screening postintervention. All results must be interpreted considering that the Covid-19 pandemic began midway through the trial, which affected care delivery generally and patients at CHCs particularly. Finally, the study results show that adaptive implementation support was effective at temporarily increasing social risk screening. It is possible that the intervention did not adequately address barriers to sustained implementation or that 6 months was not long enough to cement this change. Underresourced clinics may struggle to participate in support activities over longer periods without adequate resources, even if lengthier support is needed. As policies start requiring documentation of social risk activities, safety-net clinics may be unable to meet these requirements without adequate financial and coaching/technical support.

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.

Physician awareness of social determinants of health documentation capability in the electronic health record

Healthcare organizations are increasing social determinants of health (SDH) screening and documentation in the electronic health record (EHR). Physicians may use SDH data for medical decision-making and to provide referrals to social care resources. Physicians must be aware of these data to use them, however, and little is known about physicians' awareness of EHR-based SDH documentation or documentation capabilities. We therefore leveraged national physician survey data to measure level of awareness and variation by physician, practice, and EHR characteristics to inform practice- and policy-based efforts to drive medical-social care integration. We identify higher levels of social needs documentation awareness among physicians practicing in community health centers, those participating in payment models with social care initiatives, and those aware of other advanced EHR functionalities. Findings indicate that there are opportunities to improve physician education and training around new EHR-based SDH functionalities.

Toward informatics-enabled preparedness for natural hazards to minimize health impacts of climate change

Natural hazards (NHs) associated with climate change have been increasing in frequency and intensity. These acute events impact humans both directly and through their effects on social and environmental determinants of health. Rather than relying on a fully reactive incident response disposition, it is crucial to ramp up preparedness initiatives for worsening case scenarios. In this perspective, we review the landscape of NH effects for human health and explore the potential of health informatics to address associated challenges, specifically from a preparedness angle. We outline important components in a health informatics agenda for hazard preparedness involving hazard-disease associations, social determinants of health, and hazard forecasting models, and call for novel methods to integrate them toward projecting healthcare needs in the wake of a hazard. We describe potential gaps and barriers in implementing these components and propose some high-level ideas to address them.

Adjusting for Patient Economic/Access Issues in a Hypertension Quality Measure

INTRODUCTION

The American Heart Association and American College of Cardiology have proposed adjusting hypertension-related care quality measures by excluding patients with economic/access issues from the denominator of rate calculations. No research to date has explored the methods to operationalize this recommendation or how to measure economic/access issues. This study applied and compared different approaches to populating these denominator exceptions.

METHODS

Electronic health record data from 2019 were used in 2021 to calculate hypertension control rates in 84 community health centers. A total of 10 different indicators of patient economic/access barriers to care were used as denominator exclusions to calculate and then compare adjusted quality measure performance. Data came from a nonprofit health center‒controlled network that hosts a shared electronic health record for community health centers located in 22 states.

RESULTS

A total of 5 of 10 measures yielded an increase in adjusted hypertension control rates in ≥50% of clinics (average rate increases of 0.7-3.71 percentage points). A total of 3 of 10 measures yielded a decrease in adjusted hypertension control rates in >50% of clinics (average rate decreases of 1.33-13.82 percentage points). A total of 5 measures resulted in excluding >50% of the clinic's patient population from quality measure assessments.

CONCLUSIONS

Changes in clinic-level hypertension control rates after adjustment differed depending on the measure of economic/access issue. Regardless of the exclusion method, changes between baseline and adjusted rates were small. Removing community health center patients experiencing economic/access barriers from a hypertension control quality measure resulted in excluding a large proportion of patients, raising concerns about whether this calculation can be a meaningful measure of clinical performance.

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.

Cross-Sectional Associations: Social Risks and Diabetes Care Quality, Outcomes

INTRODUCTION

Social risks (e.g., food/transportation insecurity) can hamper type 2 diabetes mellitus (T2DM) self-management, leading to poor outcomes. To determine the extent to which high-quality care can overcome social risks' health impacts, this study assessed the associations between reported social risks, receipt of guideline-based T2DM care, and T2DM outcomes when care is up to date among community health center patients.

METHODS

A cross-sectional study of adults aged ≥18 years (N=73,484) seen at 186 community health centers, with T2DM and ≥1 year of observation between July 2016 and February 2020. Measures of T2DM care included up-to-date HbA1c, microalbuminuria, low-density lipoprotein screening, and foot examination, and active statin prescription when indicated. Measures of T2DM outcomes among patients with up-to-date care included blood pressure, HbA1c, and low-density lipoprotein control on or within 6‒12 months of an index encounter. Analyses were conducted in 2021.

RESULTS

Individuals reporting transportation or housing insecurity were less likely to have up-to-date low-density lipoprotein screening; no other associations were seen between social risks and clinical care quality. Among individuals with up-to-date care, food insecurity was associated with lower adjusted rates of controlled HbA1c (79% vs 75%, p<0.001), and transportation insecurity was associated with lower rates of controlled HbA1c (79% vs 74%, p=0.005), blood pressure (74% vs 72%, p=0.025), and low-density lipoprotein (61% vs 57%, p=0.009) than among those with no reported need.

CONCLUSIONS

Community health center patients received similar care regardless of the presence of social risks. However, even among those up to date on care, social risks were associated with worse T2DM control. Future research should identify strategies for improving HbA1c control for individuals with social risks.

TRIAL REGISTRATION

This study is registered at www.

CLINICALTRIALS

gov NCT03607617.

Association of Patient and System-Level Factors With Social Determinants of Health Screening

BACKGROUND

Health systems are increasingly recognizing the importance of collecting social determinants of health (SDoH) data. However, gaps remain in our understanding of facilitators or barriers to collection. To address these gaps, we evaluated a real-world implementation of a SDoH screening tool.

METHODS

We conducted a retrospective analysis of the implementation of the SDoH screening tool at Mayo Clinic in 2019. The outcomes are: (1) completion of screening and (2) the modality used (MyChart: filled out on patient portal; WelcomeTablet: filled out by patient on a PC-tablet; EpicCare: data obtained directly by provider and entered in chart). We conducted logistic regression for completion and multinomial logistic regression for modality. The factors of interest included race and ethnicity, use of an interpreter, and whether the visit was for primary care.

RESULTS

Overall, 58.7% (293,668/499,931) of screenings were completed. Patients using interpreters and racial/ethnic minorities were less likely to complete the screening. Primary care visits were associated with an increase in completion compared with specialty care visits. Patients who used an interpreter, racial and ethnic minorities, and primary care visits were all associated with greater WelcomeTablet and lower MyChart use.

CONCLUSION

Patient and system-level factors were associated with completion and modality. The lower completion and greater WelcomeTablet use among patients who use interpreters and racial and ethnic minorities points to the need to improve screening in these groups and that the availability of the WelcomeTablet may have prevented greater differences. The higher completion in primary care visits may mean more outreach is needed for specialists.

Social Determinants of Health Data Availability for Patients with Eye Conditions

Purpose

To quantify and characterize social determinants of health (SDoH) data coverage using single-center electronic health records (EHRs) and the National Institutes of Health All of Us research program.

Design

Retrospective cohort study from June 2014 through June 2021.

Participants

Adults 18 years of age or older with a diagnosis of diabetic retinopathy, glaucoma, cataracts, or age-related macular degeneration.

Methods

For All of Us, research participants completed online survey forms as part of a nationwide prospective cohort study. In local EHRs, patients were selected based on diagnosis codes.

Main Outcome Measures

Social determinants of health data coverage, characterized by the proportion of each disease cohort with available data regarding demographics and socioeconomic factors.

Results

In All of Us, we identified 23 806 unique adult patients, of whom 2246 had a diagnosis of diabetic retinopathy, 13 448 had a diagnosis of glaucoma, 6634 had a diagnosis of cataracts, and 1478 had a diagnosis of age-related macular degeneration. Survey completion rates were high (99.5%-100%) across all cohorts for demographic information, overall health, income, education, and lifestyle. However, health care access (12.7%-29.4%), housing (0.7%-1.1%), social isolation (0.2%-0.3%), and food security (0-0.1%) showed significantly lower response rates. In local EHRs, we identified 80 548 adult patients, of whom 6616 had a diagnosis of diabetic retinopathy, 26 793 had a diagnosis of glaucoma, 40 427 had a diagnosis of cataracts, and 6712 had a diagnosis of age-related macular degeneration. High data coverage was found across all cohorts for variables related to tobacco use (82.84%-89.07%), alcohol use (77.45%-83.66%), and intravenous drug use (84.76%-93.14%). However, low data coverage (< 50% completion) was found for all other variables, including education, finances, social isolation, stress, physical activity, food insecurity, and transportation. We used chi-square testing to assess whether the data coverage varied across different disease cohorts and found that all fields varied significantly (P < 0.001).

Conclusions

The limited and highly variable data coverage in both local EHRs and All of Us highlights the need for researchers and providers to develop SDoH data collection strategies and to assemble complete datasets.

Individual-Level and Neighborhood-Level Factors Associated with Longitudinal Changes in Cardiometabolic Measures in Participants of a Clinic-Based Care Coordination Program: A Secondary Data Analysis

Background: Identifying individual and neighborhood-level factors associated with worsening cardiometabolic risks despite clinic-based care coordination may help identify candidates for supplementary team-based care. Methods: Secondary data analysis of data from a two-year nurse-led care coordination program cohort of Medicare, Medicaid, dual-eligible adults, Leveraging Information Technology to Guide High Tech, High Touch Care (LIGHT2), from ten Midwestern primary care clinics in the U.S. Outcome Measures: Hemoglobin A1C, low-density-lipoprotein (LDL) cholesterol, and blood pressure. Multivariable generalized linear regression models assessed individual and neighborhood-level factors associated with changes in outcome measures from before to after completion of the LIGHT2 program. Results: 6378 participants had pre-and post-intervention levels reported for at least one outcome measure. In adjusted models, higher pre-intervention cardiometabolic measures were associated with worsening of all cardiometabolic measures. Women had worsening LDL-cholesterol compared with men. Women with pre-intervention HbA1c > 6.8% and systolic blood pressure > 131 mm of Hg had worse post-intervention HbA1c and systolic blood pressure compared with men. Adding individual’s neighborhood-level risks did not change effect sizes significantly. Conclusions: Increased cardiometabolic risks and gender were associated with worsening cardiometabolic outcomes. Understanding unresolved gender-specific needs and preferences of patients with increased cardiometabolic risks may aid in tailoring clinic-community-linked care planning.

Clinically Screening Hospital Patients for Social Risk Factors Across Multiple Hospitals: Results and Implications for Intervention Development

BACKGROUND

Hospitals are increasingly screening patients for social risk factors to help improve patient and population health. Intelligence gained from such screening can be used to inform social need interventions, the development of hospital-community collaborations, and community investment decisions.

OBJECTIVE

We evaluated the frequency of admitted patients' social risk factors and examined whether these factors differed between hospitals within a health system. A central goal was to determine if community-level social need interventions can be similar across hospitals.

DESIGN AND PARTICIPANTS

We described the development, implementation, and results from Northwell Health's social risk factor screening module. The statistical sample included patients admitted to 12 New York City/Long Island hospitals (except for maternity/pediatrics) who were clinically screened for social risk factors at admission from June 25, 2019, to January 24, 2020.

MAIN MEASURES

We calculated frequencies of patients' social needs across all hospitals and for each hospital. We used chi-square and Friedman tests to evaluate whether the hospital-level frequency and rank order of social risk factors differed across hospitals.

RESULTS

Patients who screened positive for any social need (n = 5196; 6.6% of unique patients) had, on average, 2.3 of 13 evaluated social risk factors. Among these patients, the most documented social risk factor was challenges paying bills (29.4%). The frequency of 12 of the 13 social risk factors statistically differed across hospitals. Furthermore, a statistically significant variance in rank orders between the hospitals was identified (Friedman test statistic 30.8 > 19.6: χ2 critical, p = 0.05). However, the hospitals' social need rank orders within their respective New York City/Long Island regions were similar in two of the three regions.

CONCLUSIONS

Hospital patients' social needs differed between hospitals within a metropolitan area. Patients at different hospitals have different needs. Local considerations are essential in formulating social need interventions and in developing hospital-community partnerships to address these needs.

A Systematic Literature Review of Health Center Efforts to Address Social Determinants of Health

Health centers (HCs) play a crucial and integral role in addressing social determinants of health (SDOH) among vulnerable and underserved populations, yet data on SDOH assessment and subsequent actions is limited. We conducted a systematic review to understand the existing evidence of integration of SDOH into HC primary-care practices. Database searches yielded 3,516 studies, of which 41 articles met the inclusion criteria. A majority of studies showed that HCs primarily captured patient-level rather than community-level SDOH data. Studies also showed that HCs utilized SDOH in electronic health records but capabilities varied widely. A few studies indicated that HCs measured health-related outcomes of integrating SDOH data. The review highlighted that many knowledge gaps exist in the collection, use, and assessment of impact of these data on outcomes, and future research is needed to address this knowledge gap.

ICD-10 Z-Code Health-Related Social Needs and Increased Healthcare Utilization

INTRODUCTION

Health-related social needs are known drivers of health and health outcomes, yet work to date to examine health-related social needs using ICD-10 Z-codes remains limited. This study seeks to evaluate the differences in the prevalence of conditions as well as utilization and cost between patients with and without health-related social needs.

METHODS

Using the 2017 Florida State Emergency Department and State Inpatient Databases, this study identified patients with documented health-related social needs using ICD-10 Z-codes. The prevalence ratio was calculated for 14 conditions that are the leading causes of mortality and economic costs. In addition, ratios for the median total number of negative health events and total annual costs between patients with health-related social needs and those without health-related social needs across these conditions were calculated. Data analysis was conducted in 2021.

RESULTS

Of 4,477,772 patients, 46,081 (1.0%) had documented health-related social needs and had 4 times the negative health events and 9.3 times the total annual costs. Trends of increased negative health events and costs were seen across all examined conditions; patients with health-related social needs had 2.5-3.5 times the negative health events and 2-18 times greater total costs. The biggest difference in negative health events was seen in patients with unintentional injuries and depression and psychoses (3.5 times for patients with health-related social needs), whereas the biggest difference in total costs was for unintentional injuries (18.4 times for patients with health-related social needs).

CONCLUSIONS

This study shows the increased prevalence of numerous high-priority conditions as well as increased utilization and costs among patients with documented health-related social needs.

The Influence of the Electronic Health Record on Achieving Equity and Eliminating Health Disparities for Children

The electronic health record (EHR) has been heralded as a transformative approach to modernizing health care and advancing health equity. Access to the EHR can facilitate shared clinical decision-making and improved communication with patients, families, and among health care providers. Recent legislative and regulatory efforts have been passed to increase the transparency as well as the initiatives to increase the meaningful use of the EHR. Yet despite these well-intended efforts, challenges to addressing health equity through the EHR persist. This article reviews three distinct challenges to addressing health equity related to the EHR. We discuss (1) both the implicit and explicit bias that exist in EHR documentation, (2) the gaps that remain between screening for social determinants of health and the effective inclusion and billing of that screening into the EHR, and (3) the disparities that exist with the use of patient portals. Addressing these three areas will enhance the opportunities to advance health equity through the use of the EHR and bring us one step closer to eliminating health disparities in pediatric health care. [Pediatr Ann. 2022;51(3):e112-e117.].

Changes in health and health care utilization following eviction from public housing

OBJECTIVES

This study sought to (1) determine the number of persons evicted from the Durham Housing Authority (DHA) over a 5-year period, (2) explore changes in the number of persons with various medical diagnoses and health care utilization patterns before and after eviction, and (3) examine how many persons evicted from DHA became literally homeless.

DESIGN

This was a pre/post cross-sectional quantitative study.

SAMPLE

Heads of households evicted from DHA properties from January 1, 2013 through December 31, 2017 were included in the study.

MEASUREMENTS

We matched people evicted by the DHA in a university health system electronic health record system to determine changes in diagnoses and health care utilization before and after eviction. We also matched the cohort in the homeless management information system to determine how many persons evicted became literally homeless.

RESULTS

Findings indicate statistically significant increases in persons with medical diagnoses in five of ten categories, total hospital admissions, and emergency department visits after eviction. Of the 152 people included in the study, 34 (22%) became literally homeless.

CONCLUSIONS

Health and health care utilization patterns were different before and after eviction. Implications for clinicians are explored.

Evaluation of a social determinants of health screening questionnaire and workflow pilot within an adult ambulatory clinic

BACKGROUND

There is increased recognition in clinical settings of the importance of documenting, understanding, and addressing patients' social determinants of health (SDOH) to improve health and address health inequities. This study evaluated a pilot of a standardized SDOH screening questionnaire and workflow in an ambulatory clinic within a large integrated health network in Northern California.

METHODS

The pilot screened for SDOH needs using an 11-question Epic-compatible paper questionnaire assessing eight SDOH and health behavior domains: financial resource, transportation, stress, depression, intimate partner violence, social connections, physical activity, and alcohol consumption. Eligible patients for the pilot receiving a Medicare wellness, adult annual, or new patient visits during a five-week period (February-March, 2020), and a comparison group from the same time period in 2019 were identified. Sociodemographic data (age, sex, race/ethnicity, and payment type), visit type, length of visit, and responses to SDOH questions were extracted from electronic health records, and a staff experience survey was administered. The evaluation was guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.

RESULTS

Two-hundred eighty-nine patients were eligible for SDOH screening. Responsiveness by domain ranged from 55 to 67%, except for depression. Half of patients had at least one identified social need, the most common being stress (33%), physical activity (22%), alcohol (12%), and social connections (6%). Physical activity needs were identified more in females (81% vs. 19% in males, p < .01) and at new patient/transfer visits (48% vs. 13% at Medicare wellness and 38% at adult wellness visits, p < .05). Average length of visit was 39.8 min, which was 1.7 min longer than that in 2019. Visit lengths were longer among patients 65+ (43.4 min) and patients having public insurance (43.6 min). Most staff agreed that collecting SDOH data was relevant and accepted the SDOH questionnaire and workflow but highlighted opportunities for improvement in training and connecting patients to resources.

CONCLUSION

Use of evidence-based SDOH screening questions and associated workflow was effective in gathering patient SDOH information and identifying social needs in an ambulatory setting. Future studies should use qualitative data to understand patient and staff experiences with collecting SDOH information in healthcare settings.

Documentation and review of social determinants of health data in the EHR: measures and associated insights

OBJECTIVE

Electronic Health Records (EHRs) increasingly include designated fields to capture social determinants of health (SDOH). We developed measures to characterize their use, and use of other SDOH data types, to optimize SDOH data integration.

MATERIALS AND METHODS

We developed 3 measures that accommodate different EHR data types on an encounter or patient-year basis. We implemented these measures-documented during encounter (DDE) captures documentation occurring during the encounter; documented by discharge (DBD) includes DDE plus documentation occurring any time prior to admission; and reviewed during encounter (RDE) captures whether anyone reviewed documented data-for the newly available structured SDOH fields and 4 other comparator SDOH data types (problem list, inpatient nursing question, social history free text, and social work notes) on a hospital encounter basis (with patient-year metrics in the Supplementary Appendix). Our sample included all patients (n = 27 127) with at least one hospitalization at UCSF Health (a large, urban, tertiary medical center) over a 1-year period.

RESULTS

We observed substantial variation in the use of different SDOH EHR data types. Notably, social history question fields (newly added at study period start) were rarely used (DDE: 0.03% of encounters, DBD: 0.26%, RDE: 0.03%). Free-text patient social history fields had higher use (DDE: 12.1%, DBD: 49.0%, RDE: 14.4%).

DISCUSSION

Our measures of real-world SDOH data use can guide current efforts to capture and leverage these data. For our institution, measures revealed substantial variation across data types, suggesting the need to engage in efforts such as EHR-user education and targeted workflow integration.

CONCLUSION

Measures revealed opportunities to optimize SDOH data documentation and review.

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.

The quality of social determinants data in the electronic health record: a systematic review

OBJECTIVE

The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH).

MATERIALS AND METHODS

We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process.

RESULTS

The most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully.

DISCUSSION

The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups.

CONCLUSION

Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.

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.

Using Electronic Health Record-Based Clinical Decision Support to Provide Social Risk-Informed Care in Community Health Centers: Protocol for the Design and Assessment of a Clinical Decision Support Tool

Background

Consistent and compelling evidence demonstrates that social and economic adversity has an impact on health outcomes. In response, many health care professional organizations recommend screening patients for experiences of social and economic adversity or social risks—for example, food, housing, and transportation insecurity—in the context of care. Guidance on how health care providers can act on documented social risk data to improve health outcomes is nascent. A strategy recommended by the National Academy of Medicine involves using social risk data to adapt care plans in ways that accommodate patients’ social risks.

Objective

This study’s aims are to develop electronic health record (EHR)–based clinical decision support (CDS) tools that suggest social risk–informed care plan adaptations for patients with diabetes or hypertension, assess tool adoption and its impact on selected clinical quality measures in community health centers, and examine perceptions of tool usability and impact on care quality.

Methods

A systematic scoping review and several stakeholder activities will be conducted to inform development of the CDS tools. The tools will be pilot-tested to obtain user input, and their content and form will be revised based on this input. A randomized quasi-experimental design will then be used to assess the impact of the revised tools. Eligible clinics will be randomized to a control group or potential intervention group; clinics will be recruited from the potential intervention group in random order until 6 are enrolled in the study. Intervention clinics will have access to the CDS tools in their EHR, will receive minimal implementation support, and will be followed for 18 months to evaluate tool adoption and the impact of tool use on patient blood pressure and glucose control.

Results

This study was funded in January 2020 by the National Institute on Minority Health and Health Disparities of the National Institutes of Health. Formative activities will take place from April 2020 to July 2021, the CDS tools will be developed between May 2021 and November 2022, the pilot study will be conducted from August 2021 to July 2022, and the main trial will occur from December 2022 to May 2024. Study data will be analyzed, and the results will be disseminated in 2024.

Conclusions

Patients’ social risk information must be presented to care teams in a way that facilitates social risk–informed care. To our knowledge, this study is the first to develop and test EHR-embedded CDS tools designed to support the provision of social risk–informed care. The study results will add a needed understanding of how to use social risk data to improve health outcomes and reduce disparities.

International Registered Report Identifier (IRRID)

PRR1-10.2196/31733

Scaling-up social needs screening in practice: a retrospective, cross-sectional analysis of data from electronic health records from Bronx county, New York, USA

OBJECTIVES

There has been renewed focus on health systems integrating social care to improve health outcomes with relatively less related research focusing on 'real-world' practice. This study describes a health system's experience from 2018 to 2020, following the successful pilot in 2017, to scale social needs screening of patients within a large urban primary care ambulatory network.

SETTING

Academic medical centre with an ambulatory network of 18 primary care practices located in an urban county in New York City (USA).

PARTICIPANTS

This retrospective, cross-sectional study used electronic health records of 244 764 patients who had a clinical visit between 10 April 2018 and 8 December 2019 across any one of 18 primary care practices.

METHODS

We organised measures using the RE-AIM framework domains of reach and adoption to ascertain the number of patients who were screened and the number of providers who adopted screening and associated documentation, respectively. We used descriptive statistics to summarise factors comparing patients screened versus those not screened, the prevalence of social needs screening and adoption across 18 practices.

RESULTS

Between April 2018 and December 2019, 53 093 patients were screened for social needs, representing approximately 21.7% of the patients seen. Almost one-fifth (19.6%) of patients reported at least one unmet social need. The percentage of screened patients varied by both practice location (range 1.6%-81.6%) and specialty within practices. 51.8% of providers (n=1316) screened at least one patient.

CONCLUSIONS

These findings demonstrate both the potential and challenges of integrating social care in practice. We observed significant variability in uptake across the health system. More research is needed to better understand factors driving adoption and may include harmonising workflows, establishing unified targets and using data to drive improvement.

Implementation of Health-Related Social Needs Screening at Michigan Health Centers: A Qualitative Study

PURPOSE

Federally qualified health centers (FQHCs) are leaders in screening for and addressing patient's health-related social needs but variation exists in screening practices. This variation is relatively unexplored, particularly the influences of organizational and state policies. We employed a qualitative descriptive approach to study social needs screening practices at Michigan FQHCs to characterize screening processes and identify drivers of variation in screening implementation.

METHODS

Site visits and semistructured interviews were conducted from October 2016 through March 2017, to explore implementation of social needs screening in clinical practice. Five FQHCs were selected through maximum variation sampling. Within each site, snowball sampling identified care team members highly knowledgeable about social needs screening. We conducted 4 to 5 interviews per site. Transcripts were analyzed using a thematic approach.

RESULTS

We interviewed 23 participants from 5 sites; these sites varied by geography, age distribution, and race/ethnicity. We identified 4 themes: (1) statewide initiatives and local leadership drove variation in screening practices; (2) as community health workers (CHWs) played an integral role in identifying patients' needs, their roles often shifted from that of screener to implementer; (3) social needs screening data was variably integrated into electronic health records and infrequently used for population health management; and (4) sites experienced barriers to social needs screening that limited the perceived impact and sustainability.

CONCLUSIONS

FQHCs placed value on the role of CHWs, on sustainable initiatives, and on funding to support continued social needs screening in primary care settings.

Examining the Interfacility Variation of Social Determinants of Health in the Veterans Health Administration

Introduction

Recently, numerous studies have linked social determinants of health (SDoH) with clinical outcomes. While this association is well known, the interfacility variability of these risk favors within the Veterans Health Administration (VHA) is not known. Such information could be useful to the VHA for resource and funding allocation. The aim of this study is to explore the interfacility variability of 5 SDoH within the VHA.

Methods

In a cohort of patients (aged ≥ 65 years) hospitalized at VHA acute care facilities with either acute myocardial infarction (AMI), heart failure (HF), or pneumonia in 2012, we assessed (1) the proportion of patients with any of the following five documented SDoH: lives alone, marginal housing, alcohol use disorder, substance use disorder, and use of substance use services, using administrative diagnosis codes and clinic stop codes; and (2) the documented facility-level variability of these SDoH. To examine whether variability was due to regional coding differences, we assessed the variation of living alone using a validated natural language processing (NLP) algorithm.

Results

The proportion of veterans admitted for AMI, HF, and pneumonia with SDoH was low. Across all 3 conditions, lives alone was the most common SDoH (2.2% [interquartile range (IQR), 0.7-4.7]), followed by substance use disorder (1.3% [IQR, 0.5-2.1]), and use of substance use services (1.2% [IQR, 0.6-1.8]). Using NLP, the proportion of hospitalized veterans with lives alone was higher for HF (14.4% vs 2.0%, P < .01), pneumonia (11% vs 1.9%, P < .01), and AMI (10.2% vs 1.4%, P < .01) compared with International Classification of Diseases, Ninth Edition codes. Interfacility variability was noted with both administrative and NLP extraction methods.

Conclusions

The presence of SDoH in administrative data among patients hospitalized for common medical issues is low and variable across VHA facilities. Significant facility-level variation of 5 SDoH was present regardless of extraction method.

Comparison of Community-Level and Patient-Level Social Risk Data in a Network of Community Health Centers

IMPORTANCE

Responding to the substantial research on the relationship between social risk factors and health, enthusiasm has grown around social risk screening in health care settings, and numerous US health systems are experimenting with social risk screening initiatives. In the absence of standard social risk screening recommendations, some health systems are exploring using publicly available community-level data to identify patients who live in the most vulnerable communities as a way to characterize patient social and economic contexts, identify patients with potential social risks, and/or to target social risk screening efforts.

OBJECTIVE

To explore the utility of community-level data for accurately identifying patients with social risks by comparing the social deprivation index score for the census tract where a patient lives with patient-level social risk screening data.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional study using patient-level social risk screening data from the electronic health records of a national network of community health centers between June 24, 2016, and November 15, 2018, linked to geocoded community-level data from publicly available sources. Eligible patients were those with a recorded response to social risk screening questions about food, housing, and/or financial resource strain, and a valid address of sufficient quality for geocoding.

EXPOSURES

Social risk screening documented in the electronic health record.

MAIN OUTCOMES AND MEASURES

Community-level social risk was assessed using census tract-level social deprivation index score stratified by quartile. Patient-level social risks were identified using food insecurity, housing insecurity, and financial resource strain screening responses.

RESULTS

The final study sample included 36 578 patients from 13 US states; 22 113 (60.5%) received public insurance, 21 181 (57.9%) were female, 17 578 (48.1%) were White, and 10 918 (29.8%) were Black. Although 6516 (60.0%) of those with at least 1 social risk factor were in the most deprived quartile of census tracts, patients with social risk factors lived in all census tracts. Overall, the accuracy of the community-level data for identifying patients with and without social risks was 48.0%.

CONCLUSIONS AND RELEVANCE

Although there is overlap, patient-level and community-level approaches for assessing patient social risks are not equivalent. Using community-level data to guide patient-level activities may mean that some patients who could benefit from targeted interventions or care adjustments would not be identified.

Feasibility of Mobile Technology to Identify and Address Patients' Unmet Social Needs in a Primary Care Clinic

Mobile health tools may overcome barriers to social needs screening; however, there are limited data on the feasibility of using these tools in clinical settings. The objective was to determine the feasibility of using a mobile health system to screen for patients' social needs. In one large primary care clinic, the authors tested a tablet-based system that screens patients for social needs, transmits results to the electronic health record, and alerts providers. All adult patients presenting for a nonurgent visit were eligible. The authors evaluated the feasibility of the system and conducted follow-up surveys to determine acceptability and if patients accessed resources through the process. All providers were surveyed. Of the 252 patients approached, 219 (86.9%) completed the screen. Forty-three (19.6%) required assistance with the tablet, and 150 (68.5%) screened positive for at least 1 unmet need (food, housing, or transportation). Of the 150, 103 (68.7%) completed a follow-up survey. The majority agreed that people would learn to use the tablet quickly. Forty-eight patients (46.6%) reported contacting at least 1 community organization through the process. Of the 27 providers, 23 (85.2%) completed a survey and >70% agreed the system would result in patients having better access to resources. It was feasible to use a tablet-based system to screen for social needs. Clinics considering using mobile tools will need to determine how to screen patients who may need assistance with the tool and how to connect patients to resources through the system based on the burden of unmet needs.