Developing Electronic Health Record (EHR) Strategies Related to Health Center Patients' Social Determinants of Health

Integrating Social Needs into Health Care: An Implementation Science Perspective

Unmet social needs (e.g., housing instability, food insecurity, transportation barriers) impact a patient's ability to participate in health-seeking behaviors (e.g., physical activity, routine preventive care) and to achieve optimal health. A rapidly growing number of health care systems are incorporating social needs screening and assistance into clinical workflows, yet many implementation and sustainability challenges exist and require collaboration with social service organizations. This review highlights implementation approaches used within this rapidly changing US landscape and uses implementation science frameworks to systematically identify multilevel barriers to and facilitators of implementing and sustaining social needs care. Policies and economic investments are necessary as they determine critical barriers and facilitators within the clinical and social service contexts. Implementation may be further strengthened by cross-sector engagement, evidence-based implementation strategies, and capacity building within clinical and social service organizations. Successful, sustained implementation of social needs care may improve the quality of health care, population health, and health equity.

Housing Instability and Type 2 Diabetes Outcomes

Importance

Housing instability may worsen control of type 2 diabetes outcomes.

Objective

To estimate the association between stable vs unstable housing and type 2 diabetes outcomes.

Design, Setting, and Participants

This cohort study analyzed electronic health record data of adults with type 2 diabetes from US community-based health centers from June 2016 to April 2023. Targeted minimum loss estimation was used to examine the association between having vs not having housing instability and subsequent type 2 diabetes outcomes, adjusting for age, date of housing instability assessment, sex, race and ethnicity (social constructs that may indicate the experience of racism), language, comorbidities, health insurance, income, and census-tract level social vulnerability index. Analyses were conducted from July 2023 to September 2024.

Exposure

Report of housing stability or instability.

Main Outcomes and Measures

Mean hemoglobin A1c (HbA1c) level was the primary outcome; secondary outcomes included systolic and diastolic blood pressure (SBP and DBP) and low-density lipoprotein (LDL) cholesterol. The primary time point was 12 months after initial assessment, with secondary time points at 6, 18, 24, 30, and 36 months.

Results

A total of 90 233 individuals were included (mean [SD] age, 55.4 [13.7] years; 50 772 female [56.3%]; 25 602 Black [28.4%], 27 277 Hispanic [31.4%], 51 720 White [57.3%]); 28 784 individuals (31.9%) had a primary language other than English, and 15 713 (17.4%) reported housing instability. Prior to first housing instability assessment, mean (SD) HbA1c was 7.64% (1.94%), mean (SD) SBP was 130.0 (13.5) mm Hg, mean (SD) DBP was 78.2 (7.8) mm Hg, and mean (SD) LDL cholesterol was 101.1 (35.2) mg/dL. We estimated had all individuals experienced stable housing, compared with unstable housing, mean HbA1c would have been 0.12% lower (95% CI, -0.16% to -0.07%; P < .001), SBP would have been 0.77 mm Hg lower (95% CI, -1.14 mm Hg to -0.39 mm Hg; P < .001), and DBP 0.27 mm Hg lower (95% CI, -0.49 mm Hg to -0.06 mm Hg; P = .01), but LDL cholesterol would not have been lower (estimated difference, -1.46 mg/dL, 95% CI, -2.96 mg/dL to 0.03 mg/dL; P = .05) at 12 months, with numerically similar results at other time points.

Conclusions and Relevance

In this cohort study, housing stability was associated with small differences in type 2 diabetes outcomes; combining housing stability interventions with other diabetes interventions may be needed to improve type 2 diabetes outcomes more substantially.

Psychometric Testing of the Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences (PRAPARE) in Patients With Heart Failure and Coronary Heart Disease

The social determinants of health (SDOH) have been recognized as an important contributor to an individual's health status. A valid and reliable instrument is needed for researchers and clinicians to measure SDOH. However, there is considerable variability in the screening methodologies, as well as a lack of standardization in definitions and methods for capturing and reporting SDOH data for both electronic health record software vendors and national experts on SDOH. The Protocol for Responding to and Assessing Patient Assets, Risks, and Experiences (PRAPARE) is a commonly used instrument for measuring SDOH. We evaluated the psychometric properties of the PRAPARE instrument in patients with coronary heart disease (CHD) and heart failure (HF), focusing on its reliability and validity for assessing SDOH. We assessed internal consistency, test-retest reliability, and construct validity using data from 234 patients with CHD and/or HF recruited from outpatient clinics in Kentucky. The PRAPARE instrument demonstrated high internal consistency (KR-20 score: 0.76) and test-retest reliability (correlation coefficient: 0.88). Factor analysis identified three distinct factors (Factor I: basic necessities and services, Factor II: housing and personal well-being, and Factor III: insurance, education, and work situation) of SDOH. PRAPARE scores were significantly correlated with depressive symptoms (PHQ-9 scores) and functional outcomes of sleep (FOSQ-10 scores). PRAPARE is a reliable and valid instrument for assessing SDOH in patients with CHD and HF, highlighting its potential for clinical and research applications.

Implementing Social Determinants of Health Screening in US Emergency Departments

Importance

Screening for adverse social determinants of health (SDOH) in the emergency department (ED) may help reduce health disparities in underserved populations.

Objective

To understand barriers and facilitators to screening, documenting, and addressing adverse SDOH in a diverse sample of US EDs.

Design, Setting, and Participants

This qualitative study used in-depth interviews with leaders of a purposive sample of EDs across urban, rural, academic, and community settings who self-reported screening for adverse SDOH on a prior National Emergency Department Inventory (NEDI) USA survey. EDs that completed the 2022 NEDI-USA survey and reported adverse SDOH screening were eligible for recruitment. Eligible participants were interviewed in April to September 2023. Inductive thematic analysis was conducted from September 2023 to January 2024 to identify themes and concepts.

Main Outcomes and Measures

Themes and concepts related to ED practices for adverse SDOH screening and referral.

Results

From 77 eligible EDs, 27 leaders agreed to be interviewed, (18 [66.7%] female; mean [range] age, 44 [30 to 63] years; mean [range] time in current role, 3.25 [<1 to 12] years). Participants worked in a variety of leadership roles (eg, chair or medical, nursing, or operations director). Findings centered around heterogeneity in ED adverse SDOH screening and documentation practices; skepticism of utility of ED adverse SDOH screening and referral; drivers of ED adverse SDOH screening, such as regulatory mandates for the expansion of adverse SDOH screening; resource, staffing, and time constraints in adverse SDOH screening and linkage to services processes; and recommendations and suggestions for improving the implementation of ED adverse SDOH screening, such as tailoring validated tools to the ED context and ED stakeholder engagement in designing the screening process. Other suggestions included having additional dedicated screening staff, particularly social workers, and strengthening relationships with existing non-ED SDOH initiatives and community resources dedicated to addressing adverse SDOH.

Conclusions and Relevance

This qualitative study of US EDs describes an overview of practices and challenges surrounding adverse SDOH screening and identified novel solutions and areas where more research is needed for the successful implementation of adverse SDOH screening in the ED setting. At the policy level, regulatory mandates instituting adverse SDOH screening should include provisions for funding to support patient needs identified by screening. Additional research on development and implementation of ED adverse SDOH screening programs is needed.

Health-Related Social Needs Discussions in Primary Care Encounters in Safety-Net Clinics: A Qualitative Analysis

Importance

Health-related social needs (HRSN) influence health outcomes and health care utilization. Clinicians face challenges addressing HRSN due to limited skills, expertise, and time. Further insight is needed on how patients and clinicians navigate HRSN in clinical encounters.

Objective

This study examines outpatient primary care encounters predating widespread HRSN screening to identify how discussions on HRSN are initiated and addressed.

Design, Setting, and Participants

This qualitative analysis was conducted on transcripts of 97 audiotaped English-speaking patient encounters from 3 clinics in New York City within a municipal health care system from January 2011 through April 2015. Patients were eligible if they were older than 18 years, self-identified as Black or White, had a diagnosis of hypertension, and had at least one prior encounter with the participating clinician. Codes were developed from social needs domains addressed by the Accountable Health Communities HRSN Screening Tool. Codes were added for further social needs identified, whether a patient or clinician initiated the HRSN discussion, and how a social need was addressed, if at all. Encounters were analyzed between June 2023 and February 2024.

Main Outcomes and Measures

Characterization of the content and nature of HRSN discussions during clinical encounters within safety-net clinics.

Results

A total of 97 patients (55 [56.7%] women, 58 [59.8%] Black, mean [SD] age, 59.7 [10.6] years) had audiotaped encounters with 27 clinicians (18 [66.7%] women, 15 [55.6%] White, mean [SD] age, 36 [5.8] years). Physical activity (36% of encounters), financial strain (35%), mental health (34%), and substance use (28%) were the most discussed HRSN domains across the 97 encounters. Patients introduced financial strain most often (70% of the time), while clinicians led substance use (75%), physical activity (51%) and mental health (51%) discussions. Patients initiated conversations on employment (77%), food insecurity (62%), and housing instability (52%). Interventions included prescriptions, forms, counseling, and referrals. Domains frequently intervened on included health care navigation needs (85% of discussions), substance use (33%), and mental health (27%).

Conclusions and Relevance

In this qualitative study of HRSN discussions in primary care encounters, clinicians were more likely to initiate discussions on substance use, physical activity, and mental health, behaviors routinely assessed in primary care, but different from topics introduced by patients. Findings underscore the need for standardized screening to improve identification of domains less frequently addressed by clinicians. Additional interventions are also needed, including clinician training for how to address HRSN in resource-constrained settings and integration of other health care team members, to enhance HRSN identification and intervention.

Area Deprivation and Patient Complexity Predict Low-Value Healthcare Utilization in Persons With Heart Failure

BACKGROUND

Heart failure (HF) is a debilitating condition affecting over 6.7 million adults in the United States. Social risks and complexity, or personal, social, and clinical aspects of persons' experiences, have been found to influence healthcare utilization and hospitalizations in persons with HF. Low-value utilization, or irregular outpatient visits with frequent emergency room use, or hospitalization is common among persons with complex conditions and social risk and requires further investigation in the HF population.

OBJECTIVES

The purpose of this research was to assess the influence of complexity and social risk on low-value utilization in persons with HF using machine learning approaches.

METHODS

Supervised machine learning, tree-based predictive modeling was conducted on an existing data set of adults with HF in the eight-county region of Western New York for the year 2022. Decision tree and random forest models were validated using a 70/30 training/testing data set and k -fold cross-validation. The models were compared for accuracy and interpretability using the area under the curve, Matthews correlation coefficient, sensitivity, specificity, precision, and negative predictive value.

RESULTS

Area deprivation index, a proxy for social risk, number of chronic conditions, age, and substance use disorders were predictors of low-value utilization in both the decision tree and random forest models. The decision tree model performed moderately, whereas the random forest model performed excellently and added hardship as an additional important variable.

DISCUSSION

This is the first known study to look at the outcome of low-value utilization, targeting individuals who are underutilizing outpatient services. The random forest model performed better than the decision tree; however, features were similar in both models, with area deprivation index as the key variable in predicting low-value utilization. The decision tree was able to produce specific cutoff points, making it more interpretable and useful for clinical application. Both models can be used to create clinical tools for identifying and targeting individuals for intervention and follow-up.

Potential source of bias in AI models: Lactate measurement in the ICU as a template

Objective

Health inequities may be driven by demographics such as sex, language proficiency, and race-ethnicity. These disparities may manifest through likelihood of testing, which in turn can bias artificial intelligence models. The goal of this study is to evaluate variation in serum lactate measurements in the Intensive Care Unit (ICU).

Methods

Utilizing MIMIC-IV (2008-2019), we identified adults fulfilling sepsis-3 criteria. Exclusion criteria were ICU stay <1-day, unknown race-ethnicity, <18 years of age, and recurrent stays. Employing targeted maximum likelihood estimation analysis, we assessed the likelihood of a lactate measurement on day 1. For patients with a measurement on day 1, we evaluated the predictors of subsequent readings.

Results

We studied 15,601 patients (19.5% racial-ethnic minority, 42.4% female, and 10.0% limited English proficiency). After adjusting for confounders, Black patients had a slightly higher likelihood of receiving a lactate measurement on day 1 (odds ratio 1.19, 95% confidence interval (CI) 1.06-1.34), but not the other minority groups. Subsequent frequency was similar across race-ethnicities, but women had a lower incidence rate ratio (IRR) 0.94 (95% CI 0.90-0.98). Interestingly, patients with elective admission and private insurance also had a higher frequency of repeated serum lactate measurements (IRR 1.70, 95% CI 1.61-1.81, and 1.07, 95% CI, 1.02-1.12, respectively).

Conclusion

We found no disparities in the likelihood of a lactate measurement among patients with sepsis across demographics, except for a small increase for Black patients, and a reduced frequency for women. Variation in biomarker monitoring can be a source of data bias when modeling patient outcomes, and thus should be accounted for in every analysis.

Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making

There is a growing need to document sociodemographic factors in electronic medical records to produce representative cohorts for medical research and to perform focused research for potentially vulnerable populations. The objective of this work was to assess the content of family physicians' electronic medical records and characterize the quality of the documentation of sociodemographic characteristics. Descriptive statistics were reported for each sociodemographic characteristic. The association between the completeness rates of the sociodemographic data and the various clinics, electronic medical record vendors, and physician characteristics was analyzed. Supervised machine learning models were used to determine the absence or presence of each characteristic for all adult patients over the age of 18 in the database. Documentation of marital status (51.0%) and occupation (47.2%) were significantly higher compared to the rest of the variables. Race (1.4%), sexual orientation (2.5%), and gender identity (0.8%) had the lowest documentation rates with a 97.5% missingness rate or higher. The correlation analysis for vendor type demonstrated that there was significant variation in the availability of marital and occupation information between vendors (χ2 > 6.0, P < 0.05). Variability in documentation between clinics indicated that the majority of characteristics exhibited high variation in completeness rates with the highest variation for occupation (median: 47.2, interquartile range: 60.6%) and marital status (median: 45.6, interquartile: 59.7%). Finally, physician sex, years since a physician graduated, and whether a physician was a foreign vs a Canadian medical graduate were significantly associated with documentation rates of place of birth, citizenship status, occupation, and education in the electronic medical records. Our findings suggest a crucial need to implement better documentation strategies for sociodemographic information in the healthcare setting. To improve completeness rates, healthcare systems should monitor, encourage, enforce, or incentivize sociodemographic data collection standards.

Use of ICD-10-CM Codes for Adverse Social Determinants of Health Across Health Systems

OBJECTIVE

This study investigated ICD-10-CM codes for adverse social determinants of health (SDoH) across 12 U.S. health systems by using data from multiple health care encounter types for diverse patients covered by multiple payers.

METHODS

The authors described documentation of 11 SDoH ICD-10-CM code categories (e.g., educational problems or social environmental problems) between 2016 and 2021; assessed changes over time by using chi-square tests for trend in proportions; compared documentation in 2021 by gender, age, race-ethnicity, and site with chi-square tests; and compared all patients' mental health outcomes in 2021 with those of patients with documented SDoH ICD-10-CM codes by using exact binomial tests and one-proportion z tests.

RESULTS

Documentation of any SDoH ICD-10-CM code significantly increased, from 1.7% of patients in 2016 to 2.7% in 2021, as did that for all SDoH categories except educational problems. Documentation was often more prevalent among female patients and those of other or unknown gender than among male patients and among American Indian or Alaska Native, Black or African American, and Hispanic individuals than among those belonging to other race-ethnicity categories. More educational problems were documented for younger patients, and more social environmental problems were documented for older patients. Psychiatric diagnoses and emergency department visits and hospitalizations related to mental health were more common among patients with documented SDoH codes.

CONCLUSIONS

SDoH ICD-10-CM code documentation was infrequent and differed by population subgroup. Differences may reflect documentation practices or true SDoH prevalence variation. Standardized SDoH documentation methods are needed in health care settings.

Transportation Barriers and Diabetes Outcomes: A Longitudinal Analysis

OBJECTIVE

To estimate associations between transportation barriers and diabetes outcomes.

METHODS

Longitudinal cohort study; 86 977 adults with type 2 diabetes mellitus in community-based health centers were assessed for transportation barriers, with up to 36 months of follow-up after initial assessment. We compared scenarios in which individuals did not experience transportation barriers to scenarios in which they did, to estimate differences in mean hemoglobin a1c (HbA1c), systolic and diastolic blood pressure (SBP and DBP), and LDL cholesterol. For analysis, we used targeted minimum loss estimation at the following timepoints after initial transportation barrier assessment: 12 (primary), 6, 18, 24, 30, and 36 months. The study period was June 24, 2016 to April 30, 2023.

RESULTS

We estimated that if participants did not experience transportation barriers, mean HbA1c would have been 0.09% lower (95% CI = -0.14% to -0.04%, P = .0002) at 12 months, compared to a scenario in which they did experience transportation barriers. These results were similar at other time points. We also estimated that absence of transportation barriers was associated with, at 12 months, lower SBP (-0.6mm Hg, 95% CI = -1.0mm Hg to -0.2mm Hg, P = .004) and DBP (-0.3mm Hg, 95% CI = -0.5mm Hg to -0.1mm Hg, P = .02), but not LDL (-1.1mg/dL, 95% CI = -2.6 mg/dL to 0.5 mg/dL, P = .19). Results at other time points for SBP, DBP, and LDL outcomes were similar.

CONCLUSIONS

Absence of transportation barriers was associated with slightly lower hemoglobin A1c and blood pressure, but the small magnitude of the differences suggests that also addressing other factors may be needed to improve diabetes outcomes more meaningfully.

Extracting social determinants of health from inpatient electronic medical records using natural language processing

BACKGROUND

Social determinants of health (SDOH) have been shown to be important predictors of health outcomes. Here we developed methods to extract them from inpatient electronic medical record (EMR) data using techniques compatible with current EMR systems.

METHODS

Four social determinants were targeted: patient language barriers, employment status, education, and whether the patient lives alone. Inpatients aged 18 and older with records in the Calgary-wide EMR system were studied. Algorithms were developed on the January 2019 hospital admissions (n=8,999) and validated on the January 2018 hospital admissions (n=8,839). SDOH documented as structured data were compared against those extracted from unstructured free-text notes.

RESULTS

More than twice as many patients had a note documenting a language barrier in EMR data than in structured data; 12 % of patients indicated by EMR notes to be living alone had a partner noted in their structured marital status. The Positive Predictive Value (PPV) of the elements extracted from notes was high, at 99 % (95 % CI 94.0 %-100.0 %) for language barriers, 98 % (95 % CI 92.6 %-99.9 %) for living alone, 96 % (95 % CI 89.8 %-98.8 %) for unemployment, and 88 % (95 % CI 80.0 %-93.1 %) for retirement.

CONCLUSIONS

All SDOH elements were extracted with high PPV. SDOH documentation was largely missing in structured data and sometimes misleading.

Implementation strategies to improve adoption of unmet social needs screening and referrals in care management using enabling technologies: study protocol for a cluster randomized trial

Background

Adverse social determinants of health contribute to health inequities. Practice guidelines now recommend incorporating patient unmet social needs into patient care, and payors increasingly reimburse for screening and providing related referrals to community organizations. Emergent electronic health record (EHR)-based tools can enable clinical-community linkages, but their adoption commonly faces workflow and infrastructure barriers. Targeted implementation support such as training, championship, practice facilitation, and audit and feedback, can enhance such tools' adoption, but no prior research has assessed such strategies' impact on the adoption of 'enabling technologies' supporting clinical-community linkages. This study will test whether providing targeted implementation support to safety-net primary care health center care management teams improves the sustained adoption of EHR-based enabling technologies used to 1) screen for social needs and 2) link patients to community organizations.

Methods

Formative evaluation of barriers and facilitators to adopting EHR-enabled social needs referrals and ascertainment of services received will include semi-structured interviews and a 'guided tour' of enabling technology used by care managers serving patients with complex health and/or social needs. A modified Delphi process conducted with care management staff and subject matter experts will then inform the development of an intervention targeting adoption of social risk EHR-enabled tools. The intervention will be piloted in three health centers, refined, then tested in a pragmatic stepped-wedge cluster-randomized trial in 20 health centers (five wedges of four health centers) that provide care management to high-risk patients with social needs.

Discussion

This study is among the first to evaluate an intervention designed to support care management teams' adoption of enabling technologies to increase clinical-community linkages. It was funded in September 2023 by the National Institute of Nursing Research. Formative activities will take place from January to June 2024, the intervention will be developed in July-December 2024, the pilot study will be conducted from January-March 2025, and the cluster-randomized trial will occur from July 2025 -September 2026. Study data will be analyzed and results disseminated in 2027-2028. Study results have the potential to improve clinical-community linkages and in so doing to advance health equity.

Trial registration

Clinicaltrials.gov registration # NCT06489002. Registered July 5, 2024, https//clinicaltrials.gov/study/NCT06489002?term=NCT06489002&rank=1.

Development of a Referral Pathway to Address Health-Harming Legal Needs at a Federally Qualified Health Center

BACKGROUND

Health-harming legal needs are legal burdens that negatively affect a person's overall health. Medical-legal partnerships (MLPs) are a cost-effective way for health care systems to improve overall health and access to health care and empower health care providers to become more active in addressing health-harming legal needs and social determinants of health. This article describes the implementation of a referral pathway to an MLP in a nurse-managed community health center. This pathway was used by the health center's clinical team to help connect patients who had burdensome legal needs with legal professionals who could further help evaluate those needs.

METHODS

An MLP team developed a referral pathway in which all adult patients were asked to complete a legal screening tool to assess whether they had legal needs that could be addressed by an MLP's intervention. If a legal need was identified, the patient would meet with the community health worker for further assessment. The community health worker would then present these cases for further review to the MLP team. The Plan-Do-Study-Act approach was used to make improvements to the pathway throughout the initiative.

RESULTS

The referral pathway was used in 70.8% of patient visits in the first seven weeks of implementation, with 209 legal screenings completed. Of those, 38 patients (18.2%) reported a legal need, 12 of whom (31.6%) were referred to the MLP.

CONCLUSIONS

The referral pathway is a useful means of determining legal needs while also screening for social determinants of health. This process allows health care teams to address both health-harming legal needs and social determinants of health in a community health center.

Estimating the impact of addressing food needs on diabetes outcomes

Objective

To estimate the association between food needs and diabetes outcomes.

Research design and methods

Longitudinal cohort study, using a target trial emulation approach. 96,792 adults with type 2 diabetes mellitus who underwent food need assessment in a network of community-based health centers were followed up to 36 months after initial assessment. We used targeted minimum loss estimation to estimate the association between not experiencing food needs, compared with experiencing food needs, and hemoglobin a1c (HbA1c), systolic and diastolic blood pressure (SBP and DBP), and LDL cholesterol. The study period was June 24th, 2016 to April 30th, 2023.

Results

We estimated that not experiencing food needs, compared with experiencing food needs, would be associated with 0.12 percentage points lower (95% Confidence Interval [CI] -0.16% to -0.09%, p = < 0.0001) mean HbA1c at 12 months. We further estimated that not experiencing food needs would be associated with a 12-month SBP that was 0.67 mm Hg lower (95%CI -0.97 to -0.38 mm Hg, p < .0001), DBP 0.21 mm Hg lower (95%CI -0.38 to -0.04 mm Hg, p = .01). There was no association with lower LDL cholesterol. Results were similar at other timepoints, with associations for HbA1c, SBP, and DBP of similar magnitude, and no difference in LDL cholesterol.

Conclusions

We estimated that not experiencing food needs may be associated with modestly better diabetes outcomes. These findings support testing interventions that address food needs as part of their mechanism of action.

Preparing to Address Social Determinants of Health (SDOH): Approaches to Clinic Transformation

CONTEXT

Pennsylvanians' health is influenced by numerous social determinants of health (SDOH). Integrating SDOH data into electronic health records (EHRs) is critical to identifying health disparities, informing public health policies, and devising interventions. Nevertheless, challenges remain in its implementation within clinical settings. In 2018, the Pennsylvania Department of Health (PADOH) received the Centers for Disease Control and Prevention's DP18-1815 "Improving the Health of Americans Through Prevention and Management of Diabetes and Heart Disease and Stroke" grant to strengthen SDOH data integration in Pennsylvania practices.

IMPLEMENTATION

Quality Insights was contracted by PADOH to provide training tailored to each practice's readiness, an International Classification of Diseases, Tenth Revision (ICD-10) guide for SDOH, Continuing Medical Education on SDOH topics, and introduced the PRAPARE toolkit to streamline SDOH data integration and address disparities. Dissemination efforts included a podcast highlighting success stories and lessons learned from practices. From 2019 to 2022, Quality Insights and the University of Pittsburgh Evaluation Institute for Public Health (Pitt evaluation team) executed a mixed-methods evaluation.

FINDINGS

During 2019-2022, Quality Insights supported 100 Pennsylvania practices in integrating SDOH data into EHR systems. Before COVID-19, 82.8% actively collected SDOH data, predominantly using PRAPARE tool (62.7%) and SDOH ICD-10 codes (80.4%). Amidst COVID-19, these statistics shifted to 65.1%, 45.2%, and 42.7%, respectively. Notably, the pandemic highlighted the importance of SDOH assessment and catalyzed some practices' utilization of SDOH data. Progress was evident among practices, with additional contribution to other DP18-1815 objectives. The main challenge was the variable understanding, utilization, and capability of handling SDOH data across practices. Effective strategies involved adaptable EHR systems, persistent efforts by Quality Insights, and the presence of change champions within practices.

DISCUSSION

The COVID-19 pandemic strained staffing in many practices, impeding SDOH data integration into EHRs. Addressing the diverse understanding and use of SDOH data requires standardized training and procedures. Customized support and sustained engagement by facilitating organizations are paramount in ensuring practices' efficient SDOH data collection and integration.

Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations

OBJECTIVE

Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU).

METHODS

Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency.

RESULTS

We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings.

CONCLUSION

We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.

Development of a comprehensive survey to assess key socioecological determinants of health

BACKGROUND

Although disparities in surgical outcomes are well-documented, our understanding of how socioecological factors drive these disparities remains limited. Comprehensive and efficient assessment tools are needed. This study's objective was to develop and assess the acceptability and feasibility of a comprehensive tool evaluating socioecological determinants of health in patients requiring colorectal surgery.

METHODS

In the first phase, a comprehensive socioecological determinant of health assessment tool was developed. A review of validated socioecological health evaluation instruments was conducted, and a 2-step modified Delphi method addressed the length, clarity, appropriateness, and redundancy of each instrument. A comprehensive tool was then finalized. In the second phase, the tool was tested for acceptability and feasibility in adult patients requiring colorectal surgery using a theory-guided framework at 3 Alabama hospitals. Relationships between survey responses and measures of acceptability and feasibility were evaluated using results from initial pilot tests of the survey.

RESULTS

In Phase 1, a modified Delphi process led to the development of a comprehensive tool that included 31 socioecological determinants of health (88 questions). Results of acceptability and feasibility were globally positive (>65%) for all domains. Overall, 83% of participants agreed that others would have no trouble completing the survey, 90.4% of respondents reported the survey was not burdensome, 97.6% of patients reported having enough time to complete the survey, and 80.9% agreed the survey was well-integrated into their appointment.

CONCLUSION

An 88-item assessment tool measuring 31 socioecological determinants of health was developed with high acceptability and feasibility for patients who required colorectal surgery. This work aids in the development of research needed to understand and address surgical disparities.

Evaluating the Relationship between Neighborhood-Level Social Vulnerability and Patient Adherence to Ophthalmology Appointments

PURPOSE

To examine the association between neighborhood-level social vulnerability and adherence to scheduled ophthalmology appointments.

METHODS

In this retrospective cohort study, records of all patients ≥18 years scheduled for an ophthalmology appointment between September 12, 2020, and February 8, 2021, were reviewed. Primary exposure is neighborhood-level Social Vulnerability Index (SVI) based on the patient's residential location. SVI is a rank score of 15 social factors into four themes (socioeconomic status, household composition/disability, minority status/language, and housing type/transportation), ranging from 0 to 1.0, with higher ranks indicating greater social vulnerability. The overall SVI score and each theme were analyzed separately as the primary exposure of interest in multivariable logistic regression models that controlled for age, sex, appointment status (new or established), race, and distance from clinic. The primary outcome, non-adherence, was defined as missing more than 25% of scheduled appointments.

RESULTS

Of 8,322 patients (41% non-Hispanic Black, 24% Hispanic, 22% non-Hispanic White) with scheduled appointments, 28% were non-adherent. Non-adherence was associated with greater social vulnerability (adjusted odds ratio [aOR] per 0.01 increase in overall SVI = 2.46 [95% confidence interval, 1.99, 3.06]) and each SVI theme (socioeconomic status: aOR = 2.38 [1.94, 2.91]; household composition/disability: aOR = = 1.51 [1.26, 1.81]; minority status/language: aOR = 2.03 [1.55, 2.68]; housing type/transportation: aOR = 1.41 [1.16, 1.73]).

CONCLUSION

Neighborhood-level social vulnerability is associated with greater risk of non-adherence to scheduled ophthalmology appointments, controlling for individual characteristics. Multi-level intervention strategies that incorporate neighborhood-level vulnerabilities are needed to reduce disparities in access to ophthalmology care.

Toward standardization, harmonization, and integration of social determinants of health data: A Texas Clinical and Translational Science Award institutions collaboration

Introduction

The focus on social determinants of health (SDOH) and their impact on health outcomes is evident in U.S. federal actions by Centers for Medicare & Medicaid Services and Office of National Coordinator for Health Information Technology. The disproportionate impact of COVID-19 on minorities and communities of color heightened awareness of health inequities and the need for more robust SDOH data collection. Four Clinical and Translational Science Award (CTSA) hubs comprising the Texas Regional CTSA Consortium (TRCC) undertook an inventory to understand what contextual-level SDOH datasets are offered centrally and which individual-level SDOH are collected in structured fields in each electronic health record (EHR) system potentially for all patients.

Methods

Hub teams identified American Community Survey (ACS) datasets available via their enterprise data warehouses for research. Each hub's EHR analyst team identified structured fields available in their EHR for SDOH using a collection instrument based on a 2021 PCORnet survey and conducted an SDOH field completion rate analysis.

Results

One hub offered ACS datasets centrally. All hubs collected eleven SDOH elements in structured EHR fields. Two collected Homeless and Veteran statuses. Completeness at four hubs was 80%-98%: Ethnicity, Race; < 10%: Education, Financial Strain, Food Insecurity, Housing Security/Stability, Interpersonal Violence, Social Isolation, Stress, Transportation.

Conclusion

Completeness levels for SDOH data in EHR at TRCC hubs varied and were low for most measures. Multiple system-level discussions may be necessary to increase standardized SDOH EHR-based data collection and harmonization to drive effective value-based care, health disparities research, translational interventions, and evidence-based policy.

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

Introduction

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

Description

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

Discussion

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

Conclusion

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

Economic Determinants of Health Disparities and the Role of the Primary Care Provider

The economic determinants of adverse personal health outcomes and population level disparities pose a daunting challenge for primary care providers in promoting health for persons experiencing poverty and neighborhood deprivation. Until they are addressed, however, the health and economic well-being of persons experiencing neighborhood deprivation is not likely to be improved. There is growing evidence of effective interventions that primary care providers can adopt to address social and economic determinants of health. Primary care providers can participate in clinic and community-based approaches that target individual, neighborhood and social level drives of health and disparities.

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.

Integrating social care into gynecologic oncology: Identifying and addressing patient's social needs

OBJECTIVE

We aimed to identify social needs of gynecologic oncology patients using a self-administered social needs assessment tool (SNAT), compare the SNAT to a formal social work assessment performed by cancer care navigators (CCN), and provide SNAT-informed community resources.

METHODS

We analyzed prospectively collected data from a performance improvement initiative in a safety-net gynecologic oncology clinic between October 2021 and July 2022. We screened for eight social needs domains, health literacy, desire for social work, and presence of urgent needs. Clinicodemographic data were abstracted from the electronic medical record. Univariate descriptive statistics were used. Inter-rater reliability for social needs domains was assessed using percent agreement.

RESULTS

1010 unique patients were seen over this study period. 488 (48%) patients completed the SNAT, of which 265 (54%) screened positive for ≥1 social need. 83 (31%) patients were actively receiving cancer treatment, 140 (53%) were in post-treatment surveillance, and 42 (16%) had benign gynecologic diagnoses. Transportation (19% vs 25%), housing insecurity (18% vs 19%), and desire to speak with a social worker (16% vs 27%) were the 3 most common needs in both the entire cohort and among patients actively receiving cancer treatment. 78% patients in active treatment were seen by a CCN and received SNAT informed community resources. The percent agreement between the SNAT and formal CCN assessment ranged from 72%-94%.

CONCLUSIONS

The self-administered SNAT identified many unmet social needs among gynecologic oncology patients, corresponded well with the formal social work CCN assessment, and informed the provision of community resources.

A Systematic Review of Electronic Community Resource Referral Systems

INTRODUCTION

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

METHODS

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

RESULTS

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

DISCUSSION

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

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.

Evaluation of multiple data sources for predicting increased need for HIV prevention among cisgender women: understanding missed opportunities for Pre-exposure Prophylaxis (PrEP)

BACKGROUND

Ciswomen constitute a disproportionately low percentage of pre-exposure prophylaxis for HIV prevention (PrEP) users compared to men. Despite PrEP's effectiveness, women are 5.25 times less likely to take PrEP than men. Identifying women who have increased reasons for HIV prevention and educating and offering PrEP to these women is crucial to reducing HIV transmission and overall health equity. However, the best method of identifying women at highest risk of acquiring HIV remains unknown. This study aimed to identify common HIV risk factors and data sources for identifying these common factors (e.g., electronic medical record data, open source neighborhood data), as well as potential intervention points and missed opportunities for PrEP linkage.

METHODS

We conducted an evaluation of multiple data sources: semi-structured qualitative interviews, electronic medical record (EMR) chart abstraction, and open source data abstraction. We accessed EMRs for enrolled participants and all participants signed a standard release of medical information (ROI) form for all institutions at which they had received medical care for the five-year period preceding their HIV diagnosis. Data were abstracted using a standardized procedure. Both structured and unstructured fields (i.e., narrative text of free notes) within the EMR were examined and included for analysis. Finally, open data sources (e.g., STI cases, HIV prevalence) were examined by community area of Chicago. Open data sources were used to examine several factors contributing to the overall Economic Hardship Index (EHI) score. We used these calculated scores to assess the economic hardship within participants' neighborhoods.

RESULTS

A total of 18 cisgender women with HIV participated in our study. Participants were mostly Black/African American (55.6%) and young (median age of 34). Our analysis identified two main themes influencing HIV risk among participants: contextual factors and relationship factors. Further, potential pre-diagnosis intervention points and missed opportunities were identified during reproductive health/prenatal visits, behavioral/mental health visits, and routine STI testing. Our evaluation of multiple data sources included investigating the presence or absence of information in the EMR (STI history, HIV testing, substance use, etc.) as well as whether pertinent information could be gathered from open access sources.

CONCLUSION

Ciswomen recently diagnosed with HIV identified many shared experiences, including syndemic conditions like mental illness and substance abuse, sex with men who have sex with men, and frequent moving in areas with high HIV incidence prior to their diagnosis. It is imperative that providers ask patients about social history, information about partners, and other key variables, in addition to the standardized questions. Findings can be used to better recognize ciswomen most vulnerable to HIV and offer PrEP to them, reducing HIV transmission.

Uses of Social Determinants of Health Data to Address Cardiovascular Disease and Health Equity: A Scoping Review

Background Cardiovascular disease is the leading cause of morbidity and mortality worldwide. Prior research suggests that social determinants of health have a compounding effect on health and are associated with cardiovascular disease. This scoping review explores what and how social determinants of health data are being used to address cardiovascular disease and improve health equity. Methods and Results After removing duplicate citations, the initial search yielded 4110 articles for screening, and 50 studies were identified for data extraction. Most studies relied on similar data sources for social determinants of health, including geocoded electronic health record data, national survey responses, and census data, and largely focused on health care access and quality, and the neighborhood and built environment. Most focused on developing interventions to improve health care access and quality or characterizing neighborhood risk and individual risk. Conclusions Given that few interventions addressed economic stability, education access and quality, or community context and social risk, the potential for harnessing social determinants of health data to reduce the burden of cardiovascular disease remains unrealized.

Association Between Adverse Social Determinants of Health and Suicide Death

OBJECTIVE

The aim of this study was to identify adverse social determinants of health (SDoH) International Statistical Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code prevalence among individuals who died by suicide and to examine associations between documented adverse SDoH and suicide.

RESEARCH DESIGN

A case-control study using linked medical record, insurance claim, and mortality data from 2000 to 2015 obtained from 9 Mental Health Research Network-affiliated health systems. We included 3330 individuals who died by suicide and 333,000 randomly selected controls matched on index year and health system location. All individuals in the study (cases and controls) had at least 10 months of enrollment before the study index date. The index date for the study for each case and their matched controls was the suicide date for that given case.

RESULTS

Adverse SDoH documentation was low; only 6.6% of cases had ≥1 documented adverse SDoH in the year before suicide. Any documented SDoH and several specific adverse SDoH categories were more frequent among cases than controls. Any documented adverse SDoH was associated with higher suicide odds [adjusted odds ratio (aOR)=2.76; 95% CI: 2.38-3.20], as was family alcoholism/drug addiction (aOR=18.23; 95% CI: 8.54-38.92), being an abuse victim/perpetrator (aOR=2.53; 95% CI: 1.99-3.21), other primary support group problems (aOR=1.91; 95% CI: 1.32-2.75), employment/occupational maladjustment problems (aOR=8.83; 95% CI: 5.62-13.87), housing/economic problems (aOR: 6.41; 95% CI: 4.47-9.19), legal problems (aOR=27.30; 95% CI: 12.35-60.33), and other psychosocial problems (aOR=2.58; 95% CI: 1.98-3.36).

CONCLUSIONS

Although documented SDoH prevalence was low, several adverse SDoH were associated with increased suicide odds, supporting calls to increase SDoH documentation in medical records. This will improve understanding of SDoH prevalence and assist in identification and intervention among individuals at high suicide risk.

The impact of patients' social backgrounds assessment on nursing care: Qualitative research

Background

Although nurses are expected to address the social determinants of health (SDH) in clinical settings, the perspectives of front-line nurses on the integration of SDH into their clinical practice remain unclear. Understanding the dynamism of this integration and its outcomes can yield crucial insights into effective nursing care. This study aims to elucidate the integration and adoption of tool-based SDH assessment nursing programs and their impacts on daily nursing care.

Methods

We conducted qualitative research at a small community-based hospital in Japan, where a tool-based program characterized by social background interviews and documentation was implemented. Nurses at the hospital were recruited via purposive and snowball sampling. After hypothesis generation, semi-constructed in-depth online interviews were conducted. Each interview lasted between 30 and 50 min. The data were analyzed via thematic analysis using the framework approach.

Results

A total of 16 nurses participated. Participants' incorporation of the novel SDH assessment program was bolstered by prior learning and their recognition of its practical value. Institutional support and collaborative teamwork further facilitated the adoption of this innovation. Enhanced knowledge about the social contexts of their patients contributed to increased respect, empathy, and self-affirmation among participants, consequently enhancing the quality of nursing care.

Conclusion

Through team-based learning, reflection, and support, nurses can integrate a tool-based SDH assessment program into their daily nursing practice. This program has the potential to empower nurses to deliver more holistic care and redefine their professional identity. Further research is warranted to assess patient-reported outcomes.

A Qualitative Study of Perceptions and Preferences Regarding Social and Behavioral Risk Screening Among Primary Care Patients

BACKGROUND

Despite its relevance for healthcare settings, social and behavioral risk screening is not systematically performed by clinicians or healthcare systems.

OBJECTIVE

To address clinician concerns, such as social and behavioral risk screening disrupting the clinician-patient relationship and lack of resources to respond, we interviewed primary care patients at an academic medical center regarding their perceptions and preferences on social and behavioral risk screening.

PARTICIPANTS

Between September and December 2020, we recruited a convenience sample of 14 English-speaking primary care patients 18 years + from three clinics affiliated with an academic medical center.

APPROACH

Using a semi-structured interview guide, we asked about the importance of social and behavioral risk screening, whether or not and how to share social and behavioral risk factors, and how social and behavioral risk factors are addressed. We used a multi-step analytic process to identify the range and commonality of participants' responses thematically.

KEY RESULTS

Participants recognized that social and behavioral risk factor domains were relevant to primary care and important for treating the patient as a whole person. Participants preferred a conversation regarding social and behavioral risk factor with their primary care providers (PCPs), and suggested that, if surveys are used, they be followed with an open-ended, in-person discussion. Participants also suggested framing the discussion as something that is done routinely with all patients so that patients do not feel judged. Participants felt comfortable sharing social and behavioral risk factors when they trusted their PCPs, and felt that discussing social and behavioral risk factors with their PCPs built trust. Participants recognized that resources exist outside of the clinic, and suggested that PCPs distribute lists of relevant community resources to patients.

CONCLUSION

In our study of primary care patients on perceptions and preferences about screening and addressing social and behavioral risk factors, we found that patients were willing to share social and behavioral risk factors with their PCP, preferred an in-person discussions with or without a survey, and wanted a list of community resources to address their needs.

Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations

Objective

Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU).

Methods

Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency.

Results

We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings.

Conclusion

We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.

Social Determinants of Health Documentation in Structured and Unstructured Clinical Data of Patients With Diabetes: Comparative Analysis

Background

Electronic health records (EHRs) have yet to fully capture social determinants of health (SDOH) due to challenges such as nonexistent or inconsistent data capture tools across clinics, lack of time, and the burden of extra steps for the clinician. However, patient clinical notes (unstructured data) may be a better source of patient-related SDOH information.

Objective

It is unclear how accurately EHR data reflect patients' lived experience of SDOH. The manual process of retrieving SDOH information from clinical notes is time-consuming and not feasible. We leveraged two high-throughput tools to identify SDOH mappings to structured and unstructured patient data: PatientExploreR and Electronic Medical Record Search Engine (EMERSE).

Methods

We included adult patients (≥18 years of age) receiving primary care for their diabetes at the University of California, San Francisco (UCSF), from January 1, 2018, to December 31, 2019. We used expert raters to develop a corpus using SDOH in the compendium as a knowledge base as targets for the natural language processing (NLP) text string mapping to find string stems, roots, and syntactic similarities in the clinical notes of patients with diabetes. We applied advanced built-in EMERSE NLP query parsers implemented with JavaCC.

Results

We included 4283 adult patients receiving primary care for diabetes at UCSF. Our study revealed that SDOH may be more significant in the lives of patients with diabetes than is evident from structured data recorded on EHRs. With the application of EMERSE NLP rules, we uncovered additional information from patient clinical notes on problems related to social connectionsisolation, employment, financial insecurity, housing insecurity, food insecurity, education, and stress.

Conclusions

We discovered more patient information related to SDOH in unstructured data than in structured data. The application of this technique and further investment in similar user-friendly tools and infrastructure to extract SDOH information from unstructured data may help to identify the range of social conditions that influence patients' disease experiences and inform clinical decision-making.

Awareness, Adjustment, Assistance, Alignment, and Advocacy: Operationalizing Social Determinants of Health Topics in Undergraduate Medical Education Curricula

Social and economic factors, such as those related to food, housing, and transportation, are major drivers of health and health inequities. Multiple national professional organizations have articulated roles for physicians in identifying and addressing social determinants of health (SDOH) and the need to include SDOH in all stages of physician education. Despite encouragement from these professional organizations, medical schools still do not routinely offer SDOH education alongside basic and clinical sciences curricula. A recent national expert consensus process identified priority SDOH knowledge domains and professional skills for medical students but lacked an organizing schema and specific pedagogical examples to help translate prioritized skills into routine pedagogical practice. One such schema is the 5As framework developed by the National Academies of Sciences, Engineering, and Medicine, which elaborates on 5 strategies to strengthen social care: awareness, adjustment, assistance, alignment, and advocacy. In this article, the authors highlight and provide examples of how mapping SDOH skills to the 5As framework can help educators meaningfully operationalize SDOH topics into specific curricular activities during the preclinical and clinical stages of undergraduate medical education. As a foundational first step in this direction, medical schools should conduct an internal curricular review of social care content (ideally mapped to the 5As framework) and identify opportunities to integrate these topics into existing courses when relevant (e.g., in social medicine, population health, and health systems science courses). Given that health and social care integration is highly context dependent, each medical school will likely need to tailor curricular changes based on their own institutional needs, mission, patient populations, and ties to the community. To increase interinstitutional alignment, medical schools might consider using or adapting peer-reviewed materials and assessments curated and centralized by the National Collaborative for Education to Address the Social Determinants of Health.

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.

Pediatric primary care provider and staff perspectives on the implementation of electronic health record-based social needs interventions: A mixed-methods study

Introduction

Interventions to address social needs in clinical settings can improve child and family health outcomes. Electronic health record (EHR) tools are available to support these interventions but are infrequently used. This mixed-methods study sought to identify approaches for implementing social needs interventions using an existing EHR module in pediatric primary care.

Methods

We conducted focus groups and interviews with providers and staff (n = 30) and workflow assessments (n = 48) at four pediatric clinics. Providers and staff completed measures assessing the acceptability, appropriateness, and feasibility of social needs interventions. The Consolidated Framework for Implementation Research guided the study. A hybrid deductive-inductive approach was used to analyze qualitative data.

Results

Median scores (range 1-5) for acceptability (4.9) and appropriateness (5.0) were higher than feasibility (3.9). Perceived barriers to implementation related to duplicative processes, parent disclosure, and staffing limitations. Facilitators included the relative advantage of the EHR module compared to existing documentation practices, importance of addressing social needs, and compatibility with clinic culture and workflow. Self-administered screening was seen as inappropriate for sensitive topics. Strategies identified included providing resource lists, integrating social needs assessments with existing screening questionnaires, and reducing duplicative documentation.

Conclusions

This study offers insight into the implementation of EHR-based social needs interventions and identifies strategies to promote intervention uptake. Findings highlight the need to design interventions that are feasible to implement in real-world settings. Future work should focus on integrating multiple stakeholder perspectives to inform the development of EHR tools and clinical workflows to support social needs interventions.

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.

Partner-developed electronic health record tools to facilitate social risk-informed care planning

OBJECTIVE

Increased social risk data collection in health care settings presents new opportunities to apply this information to improve patient outcomes. Clinical decision support (CDS) tools can support these applications. We conducted a participatory engagement process to develop electronic health record (EHR)-based CDS tools to facilitate social risk-informed care plan adjustments in community health centers (CHCs).

MATERIALS AND METHODS

We identified potential care plan adaptations through systematic reviews of hypertension and diabetes clinical guidelines. The results were used to inform an engagement process in which CHC staff and patients provided feedback on potential adjustments identified in the guideline reviews and on tool form and functions that could help CHC teams implement these suggested adjustments for patients with social risks.

RESULTS

Partners universally prioritized tools for social risk screening and documentation. Additional high-priority content included adjusting medication costs and changing follow-up plans based on reported social risks. Most content recommendations reflected partners' interests in encouraging provider-patient dialogue about care plan adaptations specific to patients' social needs. Partners recommended CDS tool functions such as alerts and shortcuts to facilitate and efficiently document social risk-informed care plan adjustments.

DISCUSSION AND CONCLUSION

CDS tools were designed to support CHC providers and staff to more consistently tailor care based on information about patients' social context and thereby enhance patients' ability to adhere to care plans. While such adjustments occur on an ad hoc basis in many care settings, these are among the first tools designed both to systematize and document these activities.

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.

Implementation of a Biopsychosocial History and Physical Exam Template in the Electronic Health Record: Mixed Methods Study

BACKGROUND

Patients' perspectives and social contexts are critical for prevention of hospital readmissions; however, neither is routinely assessed using the traditional history and physical (H&P) examination nor commonly documented in the electronic health record (EHR). The H&P 360 is a revised H&P template that integrates routine assessment of patient perspectives and goals, mental health, and an expanded social history (behavioral health, social support, living environment and resources, function). Although the H&P 360 has shown promise in increasing psychosocial documentation in focused teaching contexts, its uptake and impact in routine clinical settings are unknown.

OBJECTIVE

The aim of this study was to assess the feasibility, acceptability, and impact on care planning of implementing an inpatient H&P 360 template in the EHR for use by fourth-year medical students.

METHODS

A mixed methods study design was used. Fourth-year medical students on internal medicine subinternship (subI) services were given a brief training on the H&P 360 and access to EHR-based H&P 360 templates. Students not working in the intensive care unit (ICU) were asked to use the templates at least once per call cycle, whereas use by ICU students was elective. An EHR query was used to identify all H&P 360 and traditional H&P admission notes authored by non-ICU students at University of Chicago (UC) Medicine. Of these notes, all H&P 360 notes and a sample of traditional H&P notes were reviewed by two researchers for the presence of H&P 360 domains and impact on patient care. A postcourse survey was administered to query all students for their perspectives on the H&P 360.

RESULTS

Of the 13 non-ICU subIs at UC Medicine, 6 (46%) used the H&P 360 templates at least once, which accounted for 14%-92% of their authored admission notes (median 56%). Content analysis was performed with 45 H&P 360 notes and 54 traditional H&P notes. Psychosocial documentation across all H&P 360 domains (patient perspectives and goals, mental health, expanded social history elements) was more common in H&P 360 compared with traditional notes. Related to impact on patient care, H&P 360 notes more commonly identified needs (20% H&P 360; 9% H&P) and described interdisciplinary coordination (78% H&P 360; 41% H&P). Of the 11 subIs completing surveys, the vast majority (n=10, 91%) felt the H&P 360 helped them understand patient goals and improved the patient-provider relationship. Most students (n=8, 73%) felt the H&P 360 took an appropriate amount of time.

CONCLUSIONS

Students who applied the H&P 360 using templated notes in the EHR found it feasible and helpful. These students wrote notes reflecting enhanced assessment of goals and perspectives for patient-engaged care and contextual factors important to preventing rehospitalization. Reasons some students did not use the templated H&P 360 should be examined in future studies. Uptake may be enhanced through earlier and repeated exposure and greater engagement by residents and attendings. Larger-scale implementation studies can help further elucidate the complexities of implementing nonbiomedical information within EHRs.

Demand for and Occurrence of Medication Cost Conversations: A Narrative Review

High medication prices can create a financial burden for patients and reduce medication initiation. To improve decision making, public policy is supporting development of tools to provide real-time prescription drug prices. We reviewed the literature on medication cost conversations to characterize the context in which these tools may be used. Our review included 42 articles: a median of 84% of patients across four clinical specialties reported a desire for cost conversations (n = 7 articles) but only 23% reported having held a cost conversation across six specialties (n = 16 articles). Non-White and older patients were less likely to report having held a cost conversation than White and younger patients in 9 of 13 and 5 of 9 articles, respectively, examining these associations. Our review indicates that tools providing price information may not result in improved decision making without complementary interventions that increase the frequency of cost conversations with a focus on protected groups.

Addressing social determinants of health through customization: Quality improvement, telemedicine, and care coordination to serve immigrant families

PURPOSE

The purpose of this project was to establish a pathway for electronic medical record (EMR) customization, utilizing quality improvement methodology, to both identify and address adverse social determinants of health (SDOH) among a diverse spina bifida (SB) population.

METHODS

Starting in September 2020, the four fundamental steps were to (1) facilitate an advisory committee to safeguard the standard clinical protocols, (2) characterize barriers to implementation, (3) evaluate workflow to sustain data entry capture, and (4) manage the technology platform for seamless integration. The SB clinic was the first clinic within the enterprise to rollout the use of an adverse SDOH mitigation activity. A Spanish-speaking interpreter was scheduled for all clinics, as many families were limited in English proficiency.

RESULTS

The customization of the EMR to support an efficient workflow to address SDOH was feasible in a large and diverse urban medical center. Of the 758 patients served in the clinic, a myelomeningocele diagnosis was present in 86% of individuals. While 52% of participants were female, ethnically 52% of individuals served were Latino. Many of these individuals disclosed being recent immigrants to the United States. Often immigration and asylum related issues were at the forefront of the SDOH issues addressed.

CONCLUSION

Given the occurrence of adverse SDOH among individuals with SB, many of whom are new Latin-American immigrants, meaningful clinical efforts are needed to both identify and address the causes of the observed disparities. EMR customization is feasible and can identify and, through social prescriptions, address SDOH to support the provision of safe, high quality, and equitable care for vulnerable and medically complex populations at home and potentially abroad.

Engaging Stakeholders in Social Determinants of Health Quality Improvement Efforts

Background Social determinants of health (SDOH) affect around 70% of health outcomes. However, it is not clear how to integrate SDOH into clinical practice and health care policy. This quality improvement project engaged stakeholders to identify SDOH factors relevant in an Alaska Native/American Indian health system and how to integrate SDOH data into electronic health records (EHRs). Methods The authors utilized an internal steering committee of clinical leadership; conducted focus groups with patients, practitioners, administrative staff, and clinical leaders; developed programmatic workgroups to engage with the health system; and coordinated with allied health systems. Results The Steering Committee members prioritized uses of SDOH data. Focus groups grounded work in local community values and refined SDOH subdomains. Workgroups developed data visualizations, such as EHR dashboards, to automate data collection for reporting and assess performance metrics. External stakeholders helped innovate ways to utilize SDOH data through community partnerships and advocacy work. Stakeholders liked how the holistic approach of SDOH looks at whole-person wellness and how it can improve patient-practitioner relationships and reduce health disparities. They were concerned about outdated SDOH data and how some sensitive SDOH could lead to unanticipated harms. Leaders emphasized developing an actionable, strengths-based SDOH framework. Conclusions Many initiatives call for integrating SDOH into health care and EHRs. Engaging diverse audiences helps guide the work. This engagement may be particularly helpful for minority-serving health systems. SDOH data collection can be stigmatizing for patients. Stakeholder engagement can mitigate that by identifying which SDOH data elements to prioritize, and how to utilize them.

Neighborhood deprivation and severe maternal morbidity in a medicaid-Insured population in Georgia

BACKGROUND

Despite growing acceptance of the role of context in shaping perinatal risk, data on how neighborhood factors may identify high-risk obstetric patients is limited. In this study, we evaluated the effect of neighborhood deprivation and neighborhood racial composition on severe maternal morbidity (SMM) among persons delivered in a large public health system in Atlanta, Georgia.

METHODS

We conducted a population cohort study using electronic medical record data on all deliveries at Grady Memorial Hospital during 2011-2020. Using residential zip codes, we calculated neighborhood deprivation index based on data from the US Census. We used log-binomial regression with generalized estimating equations to estimate crude and adjusted relative risks (aRR) and 95% confidence intervals (CI) for the association between tertile of neighborhood deprivation and SMM, adjusting for demographic, clinical, and neighborhood-level (racial composition, food desert, and transit access) covariates.

RESULTS

Among 25,257 deliveries, 6.2% (1566) experienced SMM. Approximately 24.0%, 32.0%, and 44.0% of women lived in the lowest, middle, and highest tertile of neighborhood deprivation, respectively and 64.9% lived in a neighborhood with majority non-Hispanic Black residents. After adjustment, there was no association between neighborhood deprivation and SMM (aRR: 1.0 (0.8, 1.1)) or residence in a majority Black neighborhood and SMM (aRR:1.0 (0.9, 1.2)).

CONCLUSION

In this safety-net hospital, residence in a high deprivation or majority Black neighborhood did not predict SMM at or following delivery. Individual-level social determinants may better explain variation in risk, particularly in high-burden populations.

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

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

Striving Toward Equity in Health Care for People With Communication Disabilities

PURPOSE

Approximately 10% of the U.S. adult population has a speech, language, and/or voice disability, collectively referred to as communication disabilities. An increasing number of studies demonstrate that persons with communication disabilities have worse health and health care outcomes as compared to those without communication disabilities. Understanding the state of the science, including potential contributing factors is critical to begin to address the disparities.

METHOD

Applying a historical lens and integrating multiple models of disability provide a comprehensive perspective of the health and health care outcomes of persons with communication disabilities.

RESULTS

Three phases for addressing health care disparities exist: detecting, understanding, and reducing. Results from a 2012 National Health Interview Survey provide compelling population-level results of the health and health care disparities experienced by persons with communication disabilities. To understand the disparities, factors within the health care system, such as availability of communication aids and services, as well as provider and staff biases, assumptions, and lack of knowledge need to be considered. To date, few interventions exist to address disparities in care for persons with communication disabilities. Consequently, researchers need to engage with stakeholders in innovative study designs and methods to facilitate the rapid development, implementation, and dissemination of interventions that address the disparities.

CONCLUSION

To ensure equity for the large and growing population of persons with communication disabilities, researchers, policy makers, patients, and health care systems need to collaborate in identifying and addressing the factors contributing to health and health care disparities. Presentation Video: https://doi.org/10.23641/asha.21215804.

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.