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.
- social determinants of health
- primary health care
- community factors
- residence characteristics
- electronic health records
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- social determinants of health
- primary health care
- community factors
- residence characteristics
- electronic health records
Neighbourhood and community factors have significant impacts on health.1–3 For example, neighbourhood deprivation, poverty, racial segregation, poor air quality and poor food availability have been associated with mortality,4 poor physical and mental health,5–8 poor healthcare access9 10 and less healthcare utilisation.11 Research has also mapped and highlighted geographical clustering of health, health behaviours and environmental issues by neighbourhood.12 The main goals of Healthy People 2020 are to reduce health disparities and achieve health equity by addressing the ‘social, economic, and physical’ barriers to health,13 and public health interventions and policies have aimed to improve neighbourhood conditions, and to develop food and physical activity environments that improve health.14 Despite this, most primary healthcare teams have not fully integrated new data technologies into their practice to increase and apply knowledge about neighbourhood and community factors.
The idea of integrating knowledge about a patient’s environment and community to provide comprehensive care is not new15–17; however, little is known about whether primary care teams have access to this information or if they are using it routinely. Recent advancements in health information technology, especially electronic health records (EHRs) and the availability of electronic data, have increased the potential for integrating and meaningfully using these data in primary care. Currently, there are a large amount of geocoded, community-level data publicly available in the USA from a variety of sources such as the US Census Bureau, American Community Survey and the Centers for Disease Control and Prevention.18 Examples include: neighbourhood economic conditions, racial/ethnic segregation, and environmental exposures. Thus, there have been improvements in the data and the systems that enable primary care to use a patient’s community context (ie, community-level factors) and to address community needs.
Several national organisations recommend tracking and addressing the community context of patients to improve equity and reduce health and healthcare disparities, including: the National Academy of Medicine (NAM; previously the Institute of Medicine), the American Academy of Pediatrics and the American Academy of Nursing.19–21 For example, one of the recommendations of the NAM is to collect and geocode patient addresses to understand the ‘role of built and social environment on health behaviors and on disease onset and progression’.19
Given this renewed enthusiasm for using these data, it is valuable to reflect on what has been published about using community-level factors with EHR data in primary care settings. Here, we focus on the USA because community contextual data availability, social conditions, and the social and healthcare safety net are very different from other countries. Specifically, we comment on: (1) recommendations around combining community-level factors in EHRs and using this information in the primary care setting; (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.
What are the recommendations for adding a patient’s community-level factors to the EHR to inform primary care?
There are many ideas about the potential uses of linked publicly available community-level factors and EHR clinical data in primary care settings. Primary care clinicians and researchers suggest these data can be used to improve patient care by identifying where to target interventions,22–25 tracking population health26 27 and informing clinical care.18 19 23 24 27–29 Bazemore et al recommend the addition of community-level data, which they call ‘community vital signs’, to the EHR for clinicians to inform clinical decision-making, similar to the availability and use of biological vital signs like blood pressure and heart rate. In addition to using these data for point-of-care clinical decision-making, they suggest using community vital signs for panel management and assessing population health.18 They also describe the technical aspects of how their team plans to connect these community vital signs to EHR data within a clinical data research network setting.18 They appended information on the built environment, environmental exposures and neighbourhood sociodemographics to a large network of geocoded patient addresses.
Hughes et al add that mapping ‘hot’ (high-odds) and ‘cold’ (low-odds) spots for different health conditions and issues can help primary care providers and clinics create targeted interventions.23 NAM also suggests these data can be used for diagnosis and treatment, and population health measurement. For example, NAM asserts that clinicians could use contextual information to tailor treatment plans.19 Remington and Wadland describe a bidirectional benefit of combining community-level factors and EHRs: (1) adding community-level factors to EHRs can be helpful for patient care, and (2) adding clinical data from EHRs to community-level data could enhance public health surveillance systems.27
Simpson and Novak posit that the connection between community-level factors and EHR data could assist providers with understanding patient context and delivering tailored care at all times, but that it could be particularly important when a natural disaster occurs. They interviewed patients and found many had a difficult time with self-care for chronic disease when their management routines were disrupted during a flood. Thus, alerts and reminders to patients living in an area affected by natural disaster with a diagnosis of diabetes could help them manage their chronic disease during this difficult time.30 In addition, there are several examples of using community-level factors with EHR data to identify where patients live and seek care in order to locate or relocate healthcare services.22 31–33
Others suggest that linked community-level and EHR data can be used in primary care to promote community referrals and partnerships, and clinician advocacy for their patient population.20 For example, community-level factors could be used in primary care to refer patients for focused outreach, including: case management, transportation support and community resources.24 Use of these data to understand challenges in patients’ community context can help clinicians and systems identify barriers to health and recovery, and to address these ‘upstream’ determinants of health in the community.34 In addition, the Institute of Health Equity identified actions for healthcare providers to use a patient’s community contextual information to: see the patient in a broader perspective, work in partnership with community organisations and advocate for health equity.35 36 The Council on Community Pediatrics also recommends advocacy as a way for clinicians to improve children’s health equity and decrease child health disparities. Clinicians can use their influence to advocate for healthy environments, health equity initiatives and policies that support positive parenting practices.20
Efforts to combine community-level factors and EHR clinical data also have the potential to catalyse partnerships between primary care and community organisations. For example, Henize at al 37 developed a conceptual road map for how primary care providers and clinics can help patients through community partnerships with the following steps. First, primary care providers and clinics should assess the needs of their patients and prioritise them in order of importance. Second, they should build collaborations with community partners to develop a plan of action based on the needs assessment. Lastly, the partnership should be operationalised within the healthcare setting through training around community determinants of health, development of EHR tools to track data and partnerships, and strategies to allow patients to connect with community partners from within the clinic.
Another potential use for connecting community-level data to EHRs is to risk-adjust quality indicators so that clinicians are not financially penalised for providing healthcare to patients living in neighbourhoods with high deprivation scores.38 39 Nelson et al found that mortality was higher for those living in neighbourhoods with low socioeconomic status, independent of individual characteristics and pointed out the need to risk adjust health outcome payments by neighbourhood context in order to adequately compensate providers serving people from disadvantaged neighbourhoods.4
Overall, access to patients’ community-level factors within EHRs has the potential to have a positive impact on health at the patient, community, provider, health planning and system levels. With targeted interventions, population health measurement and tailored treatment plans, contextual information about patients could help narrow disparities and improve health equity.
How have community-level factors been successfully linked to EHR data and used in primary care?
Community-level factors and clinical EHR data were combined to identify areas with high uninsurance rates to identify where an outreach programme could be implemented to increase health insurance coverage for patients seen in a practice-based research network of safety net clinics.40 Geraghty et al, demonstrated how community-level and EHR data can be used together to understand and improve chronic disease management for primary care clinics.41 Specifically, they mapped a registry of patients with chronic disease to identify the geographical location of their patients with diabetes. They also assessed the association of community-level socioeconomic status with glucose and lipid control and found that low-income neighbourhoods had less controlled diabetes than high-income neighbourhoods, but found no association between lipid control and neighbourhood socioeconomic status. Xie et al, described a use case for identifying hot and cold spots for asthma exacerbations with the goal of providing clinicians with information to assist with development of targeted interventions.42 Buckingham describes using community-level factors together with EHR data for prevalence identification of areas with a higher percentage of patients with high body mass index, diabetes and/or other issues to develop clinic-led interventions to target those issues in certain neighbourhoods.22
Connecting community-level factors and EHR data helps researchers and providers gain a better understanding of both the clinical and non-clinical factors that contribute to disease. Without the combination of these data, one does not get the whole picture of patients, patient populations and the factors contributing to or impeding their health. For example, researchers used EHR data to compute cardiovascular health scores for patients and link them to census-tract level per capita expenditures on various food groups and sociodemographic data. In this study, they found high community-level fruit and vegetable expenditures and high income were associated with high cardiovascular health scores.43 In another study, body mass index was derived from EHR data and linked to zip-code level population characteristics. Here, researchers found lower odds of obesity were associated with zip codes that had high numbers of farmers’ markets, grocery stores and college graduates.44 Drewnowski et al 45 used EHR data to identify patients with diabetes and linked these data with socioeconomic measures from the 2000 census. They found areas with high levels of college education, property values and income were negatively associated with having diabetes. Several studies also used geocoded patient and census data to understand healthcare need by mapping federal medical services areas with actual access and utilisation.32 33 46
These publications highlight the work that has been done to date, yet none of them assessed the actual impact of the targeted interventions.
Does linking community-level factors to EHR data in the primary care setting have an impact on healthcare services, patient health and health equity?
Lindau et al 47 describe a unique community–primary care partnership that enabled infrastructure for providers to ‘e-prescribe’ community resources from infrastructure linked to their EHR systems. In this example, an inventory of health-promoting resources and community social service providers was developed, integrated and made available to the care team at the point of care where a prescription to social services tailored to an individual patient’s needs and location could be generated. This study found collecting community resource information, creating and implementing a system within the EHR for prescribing these resources, and providing this information to patients was possible and useful. Specifically, the project provided prescriptions to 113 295 patients; from those surveyed (n=458), 19% reported they followed through on the information received. Thus, the authors concluded that the partnership and tools provided ‘a powerful tool for communicating health-promoting information to a targeted population’.47
Miranda et al 25 describe a geographical health information system which provided public health and healthcare providers a common window into relevant data to improve health for patients and communities. One of the case studies presented in this article created a map that modelled areas with high lead exposure risk based on clinic blood lead screening data and census data. The health department used this information to target lead screening and reported an increase in capturing elevated levels without additional cost and allowed for monitoring progress in elimination of lead exposure.
These early examples demonstrate the feasibility of using community-level factors and EHR data and establishing partnerships between healthcare and community organisations; however, they did not quantify the impact of these data on improved healthcare, patient health or health equity. In our extensive review, we did not find any publications that documented the effectiveness of these types of interventions. Therefore, there are many opportunities for future research in this nascent area of study. It is also important to note that we did not find any publications reporting that the use of community-level factors and EHR data have negative impacts or do not improve on healthcare and health outcomes.
Linkages between publicly available community-level factors and EHR data have the potential to help primary care inform clinical care, target interventions, track population health and spark community partnerships with the goal of reducing health disparities and improving health equity. Community-level factors can provide additional contextualisation for EHR data, and the unique addition of non-clinical and clinical data can provide information on geographical clustering of disease that neither type of data can provide on its own. 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. Given our growing understanding of the multiple determinants of health, and the importance of neighbourhoods in promoting or challenging health and contributing to health disparities, we encourage primary care providers to integrate community context into their EHR data systems and use the information for patient care, population health and advocacy. DeVoe et al 24 outlined the following steps to move the use of community-level factors in primary care forward: systematic collection and organisation of community-level data, presentation of these data to primary care providers in the healthcare setting and automated EHR actions to help meaningfully use the data. Importantly, they recommend research to determine whether these data are improving health outcomes. Indeed, we also identified an exciting nascent area of inquiry where more research is needed to quantify the benefits of incorporating and using community-level data in primary care settings to improve healthcare, health outcomes and health equity for patients and communities.
There is great enthusiasm and potential for using linked publicly available community-level factors and EHR data to inform primary care practice. Community-level factors can provide additional context to patient care. The unique addition of non-clinical to clinical data can inform care, target interventions, track population health and spark community partnerships with the goal of reducing health disparities and improving health equity. Currently, there is little evidence that combining non-clinical and clinical data improves healthcare, patient health or health equity. Thus, there remains an exciting nascent area of inquiry where more research is needed to quantify the benefits of incorporating and using community-level data in primary care settings to improve healthcare, health outcomes and health equity for patients and communities.
Contributors HA was responsible for conceptualisation, methodology, investigation, writing, original draft preparation and project administration. EAJ was responsible for conceptualisation, writing, review and editing, and supervision. NH was responsible for conceptualisation, writing, review and editing. SL-A was responsible for writing, review and editing. SR was responsible for writing, review and editing. JEDV was responsible for conceptualisation, resources, supervision, writing, review and editing, and funding acquisition.
Funding This study was funded by Pisacano Leadership Foundation.
Competing interests None declared.
Patient consent Not required.
Provenance and peer review Commissioned; internally peer reviewed.
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