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

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

Introduction

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

Methods

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

Results

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

Conclusions

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

A Community Resource Navigator Model: Utilizing Student Volunteers to Integrate Health and Social Care in a Community Health Center Setting

INTRODUCTION

While unmet social needs are major drivers of health outcomes, most health systems are not fully integrated with the social care sector to address them. In this case study, we describe the development and implementation of a model utilizing student volunteer community resource navigators to help patients connect with community-based organizations (CBOs). We then detail initial implementation outcomes and practical considerations for future work.

METHODS

We used the Ten Essential Public Health Services Framework to guide program planning of a student "Help Desk" model for a community health center. Planning included a literature review, observation of exemplar programs, development of a CBO directory, and evaluation of the center's patient population, clinical workflows, and data infrastructure. We piloted the model for two months. After pilot completion, we reviewed patient data to understand the feasibility of the student "Help Desk" model. We utilized planning and pilot execution materials, as well as pilot data, to develop and discuss practical considerations.

RESULTS

Design and implementation complemented ongoing social needs screening and referral to CBOs by center case managers. Patients were asked if they would accept telephone follow-up by volunteers two and four weeks after the clinic visit. Of 61 patients screened, 29 patients were referred for follow-up. Ninety percent were reached at least once during the follow-up period, and 48% of patients referred reported connecting to at least one CBO. Only 27% of patients required escalation back to case managers, and no emergency escalation was needed for any patients. Students, faculty advisors, and community health center frontline staff and leadership supported the scale up and continuation of the "Help Desk" model at the community health center.

DISCUSSION

Successful implementation required multi-sectoral collaboration, well-defined scope of practice, and data interoperability. Student volunteers are untapped resources to support integrated health and social care.

Health Status and Chronic Disease Burden of the Homeless Population: An Analysis of Two Decades of Multi-Institutional Electronic Medical Records

Using a multi-institutional EMR registry, we extracted housing status and evaluated the presence of several important comorbidities in order to describe the demographics and comorbidity burden of persons experiencing homelessness in northeast Ohio and compare this to non-homeless individuals of varying socioeconomic position. Of 1,974,766 patients in the EMR registry, we identified 15,920 (0.8%) as homeless, 351,279 (17.8%) as non-homeless and in the top quintile of area deprivation index (ADI), and 1,607,567 (81.4%) as non-homeless and in the lower four quintiles of area deprivation. The comorbidity burden was highest in the homeless population with depression (48.1%), anxiety (45.8%), hypertension (44.2%), cardiovascular disease (18.4%), and hepatitis (18.1%) among the most prevalent conditions. We conclude that it is possible to identify homeless individuals and document their comorbidity burden using a multi-institutional EMR registry, in order to guide future interventions to address the health of the homeless at the health-system and community level.

Social Determinants Predict Outcomes in Data From a Multi-Ethnic Cohort of 20,899 Patients Investigated for COVID-19

Importance: The COVID-19 pandemic exploits existing inequalities in social determinants of health (SDOH) in disease burden and access to healthcare. Few studies have examined these emerging disparities using indicators of SDOH.

Objective: To evaluate predictors of COVID-19 test positivity, morbidity, and mortality and their implications for inequalities in SDOH and for future policies and health care improvements.

Design, Setting, and Participants: A cross sectional analysis was performed on all patients tested for COVID-19 on the basis of symptoms with either a history of travel to at risk regions or close contact with a confirmed case, across the Mount Sinai Health System (MSHS) up until April 26th 2020.

Main Outcomes and Measures: Primary outcome was death from COVID-19 and secondary outcomes were test positivity, and morbidity (e.g., hospitalization and intubation caused by COVID-19).

Results: Of 20,899 tested patients, 8,928 tested positive, 1,701 were hospitalized, 684 were intubated, and 1,179 died from COVID-19. Age, sex, race/ethnicity, New York City borough (derived from first 3 digits of zip-code), and English as preferred language were significant predictors of test positivity, hospitalization, intubation and COVID-19 mortality following multivariable logistic regression analyses.

Conclusions and Relevance: People residing in poorer boroughs were more likely to be burdened by and die from COVID-19. Our results highlight the importance of integrating comprehensive SDOH data into healthcare efforts with at-risk patient populations.

Racial-ethnic differences in prevalence of social determinants of health and social risks among middle-aged and older adults in a Northern California health plan

BACKGROUND

Social determinants of health (SDoHs) and social risks (SRs) have been associated with adverse health and healthcare utilization and racial/ethnic disparities. However, there is limited information about the prevalence of SRs in non-"safety net" adult populations and how SRs differ by race/ethnicity, age, education, and income.

METHODS

We analyzed weighted survey data for 16,247 White, 1861 Black, 2895 Latino, 1554 Filipino, and 1289 Chinese adults aged 35 to 79 who responded to the 2011 or 2014/2015 Kaiser Permanente Northern California Member Health Survey. We compared age-standardized prevalence estimates of SDoHs (education, household income, marital status) and SRs (financial worry, cost-related reduced medication use and fruit/vegetable consumption, chronic stress, harassment/discrimination, health-related beliefs) across racial/ethnic groups for ages 35 to 64 and 65 to 79.

RESULTS

SDoHs and SRs differed by race/ethnicity and age group, and SRs differed by levels of education and income. In both age groups, Blacks, Latinos, and Filipinos were more likely than Whites to be in the lower income category and be worried about their financial situation. Compared to Whites, cost-related reduced medication use was higher among Blacks, and cost-related reduced fruit/vegetable consumption was higher among Blacks and Latinos. Younger adults were more likely than older adults to experience chronic stress and financial worry. Racial/ethnic disparities in income were observed within similar levels of education. Differences in prevalence of SRs by levels of education and income were wider within than across racial/ethnic groups.

CONCLUSIONS

In this non-"safety net" adult health plan population, Blacks, Latinos, and Filipinos had a higher prevalence of social risks than Whites and Chinese, and prevalence of social risks differed by age group. Our results support the assessment and EHR documentation of SDoHs and social risks and use of this information to understand and address drivers of racial/ethnic disparities in health and healthcare use.

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

IMPORTANCE

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

OBJECTIVE

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

DESIGN, SETTING, AND PARTICIPANTS

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

EXPOSURES

Social risk screening documented in the electronic health record.

MAIN OUTCOMES AND MEASURES

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

RESULTS

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

CONCLUSIONS AND RELEVANCE

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

The terminology of social emergency medicine: Measuring social determinants of health, social risk, and social need

Emergency medicine has increasingly focused on addressing social determinants of health (SDoH) in emergency medicine. However, efforts to standardize and evaluate measurement tools and compare results across studies have been limited by the plethora of terms (eg, SDoH, health-related social needs, social risk) and a lack of consensus regarding definitions. Specifically, the social risks of an individual may not align with the social needs of an individual, and this has ramifications for policy, research, risk stratification, and payment and for the measurement of health care quality. With the rise of social emergency medicine (SEM) as a field, there is a need for a simplified and consistent set of definitions. These definitions are important for clinicians screening in the emergency department, for health systems to understand service needs, for epidemiological tracking, and for research data sharing and harmonization. In this article, we propose a conceptual model for considering SDoH measurement and provide clear, actionable, definitions of key terms to increase consistency among clinicians, researchers, and policy makers.

Coordination of services for people with serious mental illness and general medical conditions: Perspectives from rural northeastern United States

OBJECTIVE

The objective of the study was to investigate providers' perspectives on how medical, mental health, and social services are coordinated for people with serious mental illnesses and general medical conditions in 2 predominantly rural states.

METHOD

To achieve multiple perspectives on service coordination, this study includes perspectives from providers employed in community mental health centers, social service agencies, and primary care settings in 2 northern rural New England states with contrasting approaches to financing and organizing services. We conducted 29 individual semistructured interviews and 1 focus group, which included administrative leaders, team leaders, primary care providers, social workers, and case managers who provide services for people with serious mental illness. Data were analyzed using qualitative thematic content analysis.

RESULTS

We identified key themes at 3 levels: (a) provider-level coordination: bridging across services; managing interprofessional communications; and contrasting perspectives on the locus of responsibility for coordination; (b) individual-level coordination: support for self-management and care navigation; trusting and continuous relationships; and the right to individual choice and autonomy; (c) system-level coordination: linking appropriate residential and care provision services, funding, recruiting and retaining staff, policy enablers, and integration solutions.

CONCLUSIONS

Three levels of provider-reported coordination themes are described for the 2 states, reflecting efforts to coordinate and integrate service delivery across medical, mental health, and social services.

IMPLICATIONS

Improvements in patient outcomes will need additional actions that target key social determinants of health. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Social Determinants of Health: What's a Healthcare System to Do?

EXECUTIVE SUMMARY

A growing literature regarding the health consequences of social risks, such as substandard housing and food insecurity, combined with increased adoption of risk-based payment models have contributed to a wave of healthcare sector initiatives focused on the social determinants of health. Yet decisions about how and when to address adverse social conditions are frequently guided by limited information about potential interventions and a lack of data on their effectiveness. We describe four complementary strategies that healthcare leaders can pursue to intervene on social adversity, split between patient care and community-level approaches. Patient care strategies rely on data about patients' social risks to adapt medical care or improve patients' social circumstances directly. Community-level strategies focus on improving the broader health and well-being of the local population through a mix of direct investments in communities and collaboration through multisector partnerships. Each approach presents unique incentives and challenges, and healthcare systems wanting to address social adversity may adopt one or more. Understanding the range of potential choices may help healthcare leaders make more informed choices in response to patient needs and changing payment and policy initiatives.

The prevalence of social care in US health care settings depends on how and whom you ask

BACKGROUND

Despite unprecedented enthusiasm for integrating social risk screening and related interventions into US health care settings, we know relatively little about the extent to which these activities occur. We reviewed results from multiple national surveys that reported on the prevalence of social care activities.

METHODS

We used snowball sampling to solicit input from 29 expert informants who were asked to share any knowledge about survey instruments that included questions on the prevalence of social care-related activities conducted in health care settings. We subsequently ran web searches on recommended surveys to identify those fielded with a national sample and conducted between Jan 1, 2007 and May 31, 2019. Finally, we analyzed and compared results across surveys.

RESULTS

We reviewed 23 total survey events (19 individual surveys and 4 that had been re-administered) that included questions on the extent of social care activities across health care disciplines and settings. Samples included a wide range of health care stakeholders (including payers, health care executives, providers, and patients.) Sample sizes ranged across the types of respondents: 95-120 respondents in surveys of payers; 44-757 in surveys of health care delivery leaders; 484-2333 in surveys of clinicians; and 500-7002 in surveys of patients. In eight cases, survey reports did not include response rates; another four reports described response rates under 25%. Fifteen of the 23 surveys incorporated questions on the prevalence of social risk screening; 17 included questions on social care intervention activities. Responses about the prevalence of both screening and interventions varied widely: between 15 and 100% of respondents reported their organization conducts screening for at least one social risk; 18-100% of respondents reported providing social care interventions. Between 3 and 22% of surveyed patients reported being screened or assisted with a social risk. In the four surveys that were administered in different years, we found no significant differences in results between survey administrations.

CONCLUSIONS

Findings suggest that caution is warranted in interpreting survey findings from any single survey since existing surveys report a wide range of prevalence estimates for social risk screening and interventions.

Social Determinants of Health Improve Predictive Accuracy of Clinical Risk Models for Cardiovascular Hospitalization, Annual Cost, and Death

BACKGROUND

Risk models in the private insurance setting may systematically underpredict in the socially disadvantaged. In this study, we sought to determine whether US minority Medicare beneficiaries had disproportionately low costs compared with their clinical outcomes and whether adding social determinants of health (SDOH) into risk prediction models improves prediction accuracy.

METHODS AND RESULTS

Retrospective observational cohort study of 2016 to 2017 Medicare Current Beneficiary Survey data (n=3614) linked to Medicare fee-for-service claims. Logistic and linear regressions were used to determine the relationship between race/ethnicity and annual costs of care, all-cause hospitalization, cardiovascular hospitalization, and death. We calculated the observed-to-expected (O:E) ratios for all outcomes under 4 risk models: (1) age+sex, (2) model 1+clinical comorbidity adjustment, (3) model 2+SDOH, and (4) SDOH alone. Our sample was 44% male and 11% black or Hispanic. Among minorities, adverse clinical outcomes were inversely related to cost. After multivariable adjustment, blacks/Hispanics had higher rates of cardiovascular hospitalization (incidence rate ratio, 1.78; P=0.012) but similar annual costs ($-336, P=0.77) compared with whites. Among whites, models 1 to 4 all showed similar O:E ratios, suggesting high accuracy in risk prediction using current models. Among minorities, adjustment for age, sex, and comorbidities underpredicted all-cause hospitalization by 20% (O:E, 1.20) and cardiovascular hospitalization by 70% (O:E, 1.70) and overpredicted death by 21% (O:E, 0.79); adding SDOH brought O:E near 1 for all outcomes. Among both groups, the SDOH risk model alone performed with equal or superior accuracy to the model based on clinical comorbidities.

CONCLUSIONS

A paradoxical relationship was observed between clinical outcomes and costs among racial and ethnic minorities. Because of systematic differences in access to care, cost may not be an appropriate surrogate for predicting clinical risk among vulnerable populations. Adjustment for SDOH improves the accuracy of risk models among racial and ethnic minorities and could guide use of prevention strategies.

Association Between Social and Economic Needs With Future Healthcare Utilization

INTRODUCTION

Unmet social and economic needs are associated with poor health outcomes, but little is known about how these needs are predictive of future healthcare utilization. This study examined the association of social and economic needs identified during medical visits with future hospitalizations and emergency department visits.

METHODS

Individuals with electronic health record-coded social and economic needs during a primary care, emergency department, or urgent care visit at Kaiser Permanente Northwest from October 1, 2016 to November 31, 2017 (case patients) were identified, as well as individuals who had visits during that time period but had no electronic health record-coded needs (control patients). The 2 groups were compared on sociodemographic characteristics, comorbidities, and healthcare utilization in the prior year. Finally, logistic regression assessed the relationship between documented needs and hospitalizations and emergency department visits in the 12 months following the index visit, controlling for sociodemographic characteristics, comorbidities, and prior healthcare utilization. Statistical analysis was completed in April 2019.

RESULTS

Case patients differed significantly from control patients on sociodemographic characteristics and had higher rates of comorbidities and prior healthcare utilization. Social and economic needs documented during the index visit were associated with significantly higher rates of hospitalization and emergency department visits in the 12 months following the visit, controlling for sociodemographic characteristics, comorbidities, and prior utilization.

CONCLUSIONS

These results demonstrate that documented social and economic needs are a powerful predictor of future hospitalization and emergency department use and suggest the need for research into whether interventions to address these needs can influence healthcare utilization.

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

INTRODUCTION

This paper describes the adoption of an electronic health record-based social determinants of health screening tool in a national network of more than 100 community health centers.

METHODS

In 2016, a screening tool with questions on 7 social determinants of health domains was developed and deployed in the electronic health record, with technical instructions on how to use the tool and suggested clinical workflows. To understand adoption patterns, the study team extracted electronic health record data for any patient with a community health center visit between June 2016 and May 2018. Patients were considered "screened" if a response to at least 1 social determinants of health domain was documented in the electronic health record tool.

RESULTS

A total of 31,549 patients (2% of those with a visit in the study period) had a documented social determinants of health screening. The number of screenings increased over time, time; 71 community health centers (67%) conducted at least one screening, but almost 50% took place in only 4 community health centers. Over half (55%) of screenings only included responses for only 1 domain. Screening was most likely to occur during an office visit with an established patient and documented in the electronic health record by a medical assistant.

CONCLUSIONS

Screening documentation patterns varied widely across the network of community health centers. Despite the growing national emphasis on the importance of screening for social determinants of health, these findings suggest that simply activating electronic health record tools for social determinants of health screening does not lead to widespread adoption. Potential barriers to screening adoption and implementation should be explored further.

SUPPLEMENT INFORMATION

This article is part of a supplement entitled Identifying and Intervening on Social Needs in Clinical Settings: Evidence and Evidence Gaps, which is sponsored by the Agency for Healthcare Research and Quality of the U.S. Department of Health and Human Services, Kaiser Permanente, and the Robert Wood Johnson Foundation.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Evaluation of a Novel Financial Navigator Pilot to Address Patient Concerns about Medical Care Costs

CONTEXT

Interventions are required that address patients' medically related financial needs.

OBJECTIVE

To evaluate a Financial Navigator pilot addressing patients' concerns/needs regarding medical care costs in an integrated health care system.

METHODS

Adults (aged ≥ 18 years) enrolled at Kaiser Permanente Northwest, who had a concern/need about medical care costs and received care in 1 of 3 clinical departments at the intervention or comparison clinic were recruited between August 1, 2016, and October 31, 2016. Baseline and 30-day follow-up participant surveys were administered to assess medical and nonmedical socioeconomic needs, satisfaction with medical care, and satisfaction with assistance with cost concerns. Physicians at both clinics were invited to complete a survey on medical care costs. We assessed participant characteristics and survey responses using descriptive statistics and 30-day change in satisfaction measures using multivariable linear regression models.

RESULTS

Eighty-five intervention and 51 comparison participants completed the baseline survey. At baseline, intervention participants reported transportation (52.9%), housing (38.2%), and social isolation (32.4%) needs; comparison participants identified employment (33.3%), food (33.3%), and housing (33.3%) needs. Intervention participants reported higher satisfaction with care (p = 0.01) and higher satisfaction with cost concerns assistance (p = 0.01) vs comparison participants at 30-day follow-up, controlling for baseline responses. Although most physicians (80%) reported discussing medical care costs with their patients, only 18% reported knowing about their patients' financial well-being.

CONCLUSION

We demonstrated the promise of a novel Financial Navigator pilot intervention to address medical care cost concerns and needs, and underscored the prevalence of nonmedical social needs in an economically vulnerable population.

Addressing Health-Related Social Needs: Value-Based Care or Values-Based Care?

Health-related social needs, such as food insecurity, housing instability, and lack of transportation, are associated with worse health outcomes, and are increasingly the focus of health-related social needs interventions within healthcare. Adoption of health-related social needs interventions is often justified by the potential to reduce healthcare costs. However, this can present a conundrum to clinicians. Physicians are often more accustomed to justifying clinical innovation based on improvements in health, in accord with the fundamental values of the medical profession, which include using our knowledge, skills, and the resources at our disposal to improve both individual and public health. In cases where health-related social needs interventions improve health but are not cost-saving, these two types of justifications can conflict. We provide a framework for considering these issues, and an agenda for scholarly work on this topic. Ultimately, if promoting patient and public health are key values for our profession, then understanding when to emphasize values-based care, rather than simply value-based care, is crucial to fulfilling our professional duty.

Accounting for accountable care: Value-based population health management

Accountable Care Organizations (ACOs) are exemplars of so-called value-based care in the US. In this model, healthcare providers bear the financial risk of their patients' health outcomes: ACOs are rewarded for meeting specific quality and cost-efficiency benchmarks, or penalized if improvements are not demonstrated. While the aim is to make providers more accountable to payers and patients, this is a sea-change in payment and delivery systems, requiring new infrastructures and practices. To manage risk, ACOs employ data-intensive sourcing and big data analytics to identify individuals within their populations and sort them using novel categories, which are then utilized to tailor interventions. The article uses an STS lens to analyze the assemblage involved in the enactment of population health management through practices of data collection, the creation of new metrics and tools for analysis, and novel ways of sorting individuals within populations. The processes and practices of implementing accountability technologies thus produce particular kinds of knowledge and reshape concepts of accountability and care. In the process, account-giving becomes as much a procedural ritual of verification as an accounting for health outcomes.

Translating Research into Agile Development (TRIAD): Development of Electronic Health Record Tools for Primary Care Settings

OBJECTIVES

This article describes a method for developing electronic health record (EHR) tools for use in primary care settings.

METHODS

The "Translating Research into Agile Development" (TRIAD) method relies on the close collaboration of researchers, end users, and development teams. This five-step method for designing a tailored EHR tool includes (1) assessment, observation, and documentation; (2) structured engagement for collaboration and iterative data collection; (3) data distillation; (4) developmental feedback from clinical team members on high-priority EHR needs and input on design prototypes and EHR functionality; and (5) agile scrum sprint cycles for prototype development.

RESULTS

The TRIAD method was used to modify an existing EHR for behavioral health clinicians (BHCs) embedded with primary care teams, called the BH e-Suite. The structured engagement processes stimulated discussions on how best to automate BHC screening tools and provide goal tracking functionality over time. Data distillation procedures rendered technical documents, with information on workflow steps, tasks, and associated challenges. In the developmental feedback phase, BHCs gave input on screening assessments, scoring needs, and other functionality to inform prototype feature development. Six 2-week sprint cycles were conducted to address three domains of prototype development: assessment and documentation needs, information retrieval, and monitoring and tracking. The BH e-Suite tool resulted with eight new EHR features to accommodate BHCs' needs.

CONCLUSION

The TRIAD method can be used to develop EHR functionality to address the evolving needs of health professionals in primary care and other settings. The BH e-Suite was developed through TRIAD and was found to be acceptable, easy to use, and improved care delivery during pilot testing. The BH e-Suite was later adopted by OCHIN Inc., which provided the tool to its 640 community health centers. This suggests that the TRIAD method is a promising research and development approach.

Documenting social determinants of health-related clinical activities using standardized medical vocabularies

OBJECTIVES

Growing recognition that health is shaped by social and economic circumstances has resulted in a rapidly expanding set of clinical activities related to identifying, diagnosing, and intervening around patients' social risks in the context of health care delivery. The objective of this exploratory analysis was to identify existing documentation tools in common US medical coding systems reflecting these emerging clinical practices to improve patients' social health.

MATERIALS AND METHODS

We identified 20 social determinants of health (SDH)-related domains used in 6 published social health assessment tools. We then used medical vocabulary search engines to conduct three independent searches for codes related to these 20 domains included in common medical coding systems (LOINC, SNOMED CT, ICD-10-CM, and CPT). Each of the 3 searches focused on one of three clinical activities: Screening, Assessment/Diagnosis, and Treatment/Intervention.

RESULTS

We found at least 1 social Screening code for 18 of the 20 SDH domains, 686 social risk Assessment/Diagnosis codes, and 243 Treatment/Intervention codes. Fourteen SDH domains (70%) had codes across all 3 clinical activity areas.

DISCUSSION

Our exploratory analysis revealed 1095 existing codes in common medical coding vocabularies that can facilitate documentation of social health-related clinical activities. Despite a large absolute number of codes, there are addressable gaps in the capacity of current medical vocabularies to document specific social risk factor screening, diagnosis, and interventions activities.

CONCLUSIONS

Findings from this analysis should help inform efforts both to develop a comprehensive set of SDH codes and ultimately to improve documentation of SDH-related activities in clinical settings.

Aligning Programs and Policies to Support Food Security and Public Health Goals in the United States

Food insecurity affects 1 in 8 US households and has clear implications for population health disparities. We present a person-centered, multilevel framework for understanding how individuals living in food-insecure households cope with inadequate access to food themselves and within their households, communities, and broader food system. Many of these coping strategies can have an adverse impact on health, particularly when the coping strategies are sustained over time; others may be salutary for health. There exist multiple opportunities for aligning programs and policies so that they simultaneously support food security and improved diet quality in the interest of supporting improved health outcomes. Improved access to these programs and policies may reduce the need to rely on individual- and household-level strategies that may have negative implications for health across the life course.

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

BACKGROUND

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

METHODS

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

DISCUSSION

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

TRIAL REGISTRATION

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

Progress towards using community context with clinical data in primary care

Community-level factors have significant impacts on health. There is renewed enthusiasm for integrating these data with electronic health record (EHR) data for use in primary care to improve health equity in the USA. Thus, it is valuable to reflect on what has been published to date. Specifically, we comment on: (1) recommendations about combining community-level factors in EHRs for use in primary care; (2) examples of how these data have been combined and used; and (3) the impact of using combined data on healthcare, patient health and health equity. We found publications discussing the potential of combined data to inform clinical care, target interventions, track population health and spark community partnerships with the goal of reducing health disparities and improving health equity. Although there is great enthusiasm and potential for using these data to inform primary care, there is little evidence of improved healthcare, patient health or health equity.

Public and Population Health Informatics: The Bridging of Big Data to Benefit Communities

Objective:  To summarize the recent public and population health informatics literature with a focus on the synergistic “bridging” of electronic data to benefit communities and other populations.

Methods:  The review was primarily driven by a search of the literature from July 1, 2016 to September 30, 2017. The search included articles indexed in PubMed using subject headings with (MeSH) keywords “public health informatics” and “social determinants of health”. The “social determinants of health” search was refined to include articles that contained the keywords “public health”, “population health” or “surveillance”.

Results:  Several categories were observed in the review focusing on public health's socio-technical infrastructure: evaluation of surveillance practices, surveillance methods, interoperable health information infrastructure, mobile health, social media, and population health. Common trends discussing socio-technical infrastructure included big data platforms, social determinants of health, geographical information systems, novel data sources, and new visualization techniques. A common thread connected these categories of workforce, governance, and sustainability: using clinical resources and data to bridge public and population health.

Conclusions:  Both medical care providers and public health agencies are increasingly using informatics and big data tools to create and share digital information. The intent of this “bridging” is to proactively identify, monitor, and improve a range of medical, environmental, and social factors relevant to the health of communities. These efforts show a significant growth in a range of population health-centric information exchange and analytics activities.

The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification

OBJECTIVES

To examine the value of unstructured electronic health record (EHR) data (free-text notes) in identifying a set of geriatric syndromes.

DESIGN

Retrospective analysis of unstructured EHR notes using a natural language processing (NLP) algorithm.

SETTING

Large multispecialty group.

PARTICIPANTS

Older adults (N=18,341; average age 75.9, 58.9% female).

MEASUREMENTS

We compared the number of geriatric syndrome cases identified using structured claims and structured and unstructured EHR data. We also calculated these rates using a population-level claims database as a reference and identified comparable epidemiological rates in peer-reviewed literature as a benchmark.

RESULTS

Using insurance claims data resulted in a geriatric syndrome prevalence ranging from 0.03% for lack of social support to 8.3% for walking difficulty. Using structured EHR data resulted in similar prevalence rates, ranging from 0.03% for malnutrition to 7.85% for walking difficulty. Incorporating unstructured EHR notes, enabled by applying the NLP algorithm, identified considerably higher rates of geriatric syndromes: absence of fecal control (2.1%, 2.3 times as much as structured claims and EHR data combined), decubitus ulcer (1.4%, 1.7 times as much), dementia (6.7%, 1.5 times as much), falls (23.6%, 3.2 times as much), malnutrition (2.5%, 18.0 times as much), lack of social support (29.8%, 455.9 times as much), urinary retention (4.2%, 3.9 times as much), vision impairment (6.2%, 7.4 times as much), weight loss (19.2%, 2.9 as much), and walking difficulty (36.34%, 3.4 as much). The geriatric syndrome rates extracted from structured data were substantially lower than published epidemiological rates, although adding the NLP results considerably closed this gap.

CONCLUSION

Claims and structured EHR data give an incomplete picture of burden related to geriatric syndromes. Geriatric syndromes are likely to be missed if unstructured data are not analyzed. Pragmatic NLP algorithms can assist with identifying individuals at high risk of experiencing geriatric syndromes and improving coordination of care for older adults.

Technology Innovations for Better Fall Risk Management in Home Care

Achieving better fall risk management is an integral component of quality home care. The current qualitative study uncovers the challenges and opportunities of home health agencies (HHAs) in achieving better fall risk management. A secondary document analysis was adopted to learn from rich contextual information in fall incident reports recorded in a HHA. Poor engagement of patients and caregivers was a contributing factor in many fall incidents. Patients often fell as a result of not understanding or accepting their physical limitations. For better fall risk management, many incidents highlighted a need for providing complete and thorough care, better coordination of care, higher levels of sociocultural awareness, patient engagement, and caregiver involvement. The results provide evidence regarding the challenges and opportunities for improving fall risk management in home care along with insight about how information technology solutions can support improvement initiatives. [Journal of Gerontological Nursing, 44(7), 15-20.].

Integrating Data On Social Determinants Of Health Into Electronic Health Records

As population health becomes more of a focus of health care, providers are realizing that data outside of traditional clinical findings can provide a broader perspective on potential drivers of a patient's health status and can identify approaches to improving the effectiveness of care. However, many challenges remain before data related to the social determinants of health, such as environmental conditions and education levels, are as readily accessible and actionable as medical data are. Key challenges are a lack of consensus on standards for capturing or representing social determinants of health in electronic health records and insufficient evidence that once information on them has been collected, social determinants can be effectively addressed through referrals or other action tools. To address these challenges and effectively use social determinants in health care settings, we recommend creating national standards for representing data related to social determinants of health in electronic health records, incentivizing the collection of the data through financial or quality measures, and expanding the body of research that measures the impact of acting on the information collected.

Added Value from Secondary Use of Person Generated Health Data in Consumer Health Informatics

Introduction: Various health-related data, subsequently called Person Generated Health Data (PGHD), is being collected by patients or presumably healthy individuals as well as about them as much as they become available as measurable properties in their work, home, and other environments. Despite that such data was originally just collected and used for dedicated predefined purposes, more recently it is regarded as untapped resources that call for secondary use. Method: Since the secondary use of PGHD is still at its early evolving stage, we have chosen, in this paper, to produce an outline of best practices, as opposed to a systematic review. To this end, we identified key directions of secondary use and invited protagonists of each of these directions to present their takes on the primary and secondary use of PGHD in their sub-fields. We then put secondary use in a wider perspective of overarching themes such as privacy, interpretability, interoperability, utility, and ethics. Results: We present the primary and secondary use of PGHD in four focus areas: (1) making sense of PGHD in augmented Shared Care Plans for care coordination across multiple conditions; (2) making sense of PGHD from patient-held sensors to inform cancer care; (3) fitting situational use of PGHD to evaluate personal informatics tools in adaptive concurrent trials; (4) making sense of environment risk exposure data in an integrated context with clinical and omics-data for biomedical research. Discussion: Fast technological progress in all the four focus areas calls for a societal debate and decision-making process on a multitude of challenges: how emerging or foreseeable results transform privacy; how new data modalities can be interpreted in light of clinical data and vice versa; how the sheer mass and partially abstract mathematical properties of the achieved insights can be interpreted to a broad public and can consequently facilitate the development of patient-centered services; and how the remaining risks and uncertainties can be evaluated against new benefits. This paper is an initial summary of the status quo of the challenges and proposals that address these issues. The opportunities and barriers identified can serve as action items individuals can bring to their organizations when facing challenges to add value from the secondary use of patient-generated health data.

Secondary Use and Analysis of Big Data Collected for Patient Care

Objectives: To identify common methodological challenges and review relevant initiatives related to the re-use of patient data collected in routine clinical care, as well as to analyze the economic benefits derived from the secondary use of this data. Through the use of several examples, this article aims to provide a glimpse into the different areas of application, namely clinical research, genomic research, study of environmental factors, and population and health services research. This paper describes some of the informatics methods and Big Data resources developed in this context, such as electronic phenotyping, clinical research networks, biorepositories, screening data banks, and wide association studies. Lastly, some of the potential limitations of these approaches are discussed, focusing on confounding factors and data quality. Methods: A series of literature searches in main bibliographic databases have been conducted in order to assess the extent to which existing patient data has been repurposed for research. This contribution from the IMIA working group on "Data mining and Big Data analytics" focuses on the literature published during the last two years, covering the timeframe since the working group's last survey. Results and Conclusions: Although most of the examples of secondary use of patient data lie in the arena of clinical and health services research, we have started to witness other important applications, particularly in the area of genomic research and the study of health effects of environmental factors. Further research is needed to characterize the economic impact of secondary use across the broad spectrum of translational research.