Integrating Technology and Human Capital to Address Social Needs: Lessons to Promote Health Equity in Diabetes Care

Summary

This report highlights implementation experiences of three Bridging the Gap: Reducing Disparities in Diabetes Care sites. Their clinical care teams use technology to identify at-risk patients with diabetes and engage them with services that address medical and social complexity. Staff identified lessons that facilitated intervention success and promoted health equity.

Background

Structural and social inequities (e.g., poverty, residential segregation, insurance access, discrimination) contribute substantially to poor clinical outcomes in chronic diseases among often marginalized populations (e.g., racial/ethnic minority groups, under-resourced communities).^1–5 Technological advances in health care, despite their ability to improve population health and well-being, have the potential to exacerbate inequities by leaving some patients behind in the technological divide.^6–8 Technology alone will not address long-standing health inequities, and will need to be accompanied by strategies that also engage human capital. As a result, health care organizations must consider how technology can be used with care teams to address the increased medical and social needs of at-risk patients and improve equity in health outcomes, particularly for patients with chronic diseases such as diabetes.⁹

Population health management (PHM) efforts (e.g., quality indicator evaluation, risk stratification) are nearly ubiquitous in the U.S. health care system, and routinely include technological platforms. Despite the growing recognition of social risks as underlying drivers of health, social needs screening and referral are not widely implemented as part of PHM efforts.^10–12 The use of technology to integrate medical, behavioral, and social data can better enable health care organizations to holistically address population health and improve health equity.^13,14 It is critical to evaluate how organizations wield technology as a tool to deliver equitable care.^15–17 This paper describes the implementation experiences and early evaluation of three health care organizations that use technology and diverse care teams to identify high-risk patients with diabetes and connect them with interventions that address their complex medical and social needs.

Organizational Contexts and Populations Served

Each health care organization examined is part of Bridging the Gap: Reducing Disparities in Diabetes Care, a five-year initiative that aims to improve diabetes care and reduce health disparities for vulnerable populations with type 2 diabetes. The initiative funds eight sites, three of which are highlighted in this paper. Each site consists of a health care organization that collaborates with other health care organizations and non-medical organizations to address the medical and social needs of often marginalized populations with diabetes.^18,19 These sites were selected to showcase diversity in technology, patient populations and associated social needs, and types of health care organizations.

La Clínica del Pueblo (LCDP), a federally qualified health center (FQHC), serves predominantly Central American and other Latinx immigrants in the Washington, D.C. Metropolitan area. Many patients at LCDP have unrecognized immigration status, limited English proficiency, low healthy literacy, and are unfamiliar with the U.S. health care system. These circumstances affect how they access health insurance, health care, and community-based services. A nurse leads a broad team of care coordinators, health educators, and others to support diabetes patients (Figure 1). La Clínica del Pueblo collaborates with many community-based organizations, including formal partnerships with legal services and food distribution organizations (Figure 1).

Trenton Health Team (THT), is a non-profit community health improvement collaborative in Trenton, New Jersey, comprising two local hospitals, a federally qualified health center (FQHC), and the Department of Health and Human Services. Trenton Health Team leads the Capital City Diabetes Collaborative (CCDC), a diverse set of stakeholders who improve diabetes outcomes by changing health care, social, and environmental factors (e.g., food access). In support of the CCDC, THT implements interventions for people with diabetes who live in predominantly African American and Latinx communities (Figure 2). Trenton Health Team operates the Trenton Health Information Exchange (HIE) and established a Community-wide Clinical Care Coordination Team that guides clinical and community interventions and supports care management efforts.

Figure 1.

Figure 2.

Providence Health & Services (Providence) is an integrated delivery system that serves patients in seven states, predominantly in the West. As part of the overall initiative, three Portland-area family medicine clinics in Oregon are also part of the Diabetes Collective Impact Initiative (DCII), a local program to improve diabetes care among predominantly Medicaid and uninsured patients. In partnership with a local social services agency, Providence established clinic-based Community Resource Desks (CRDs) to address unmet social needs through referrals to community-based organizations (e.g., transportation agencies, housing assistance) (Figure 3).

Figure 3.

Risk Stratification: Using Technology to Identify Patients with Medical and Social Needs

Risk stratification identifies patients who need extra support with the medical management of their diabetes (Table 1). Interventions that solely rely on medical risk stratification may not account for the individual social factors that affect poor diabetes outcomes, nor directly address social needs themselves. Thus, incorporating data on social needs is key to supporting care management activities and engaging at-risk populations. All three sites rely on social needs screenings to guide the care provided, even if social needs data are not incorporated directly into the risk stratification models (See Figure 4). Addressing social needs barriers (e.g., unstable housing, food insecurity) comprise an integral part of the sites' interventions and facilitate equitable care. The sites' tailored use of technology guide risk stratification processes and streamline the outreach to at-risk patients with diabetes (Table 2). Hence, the technology (i.e., electronic medical records [EMR], HIE) provides the information, but it is people who act on the data that enable organizations to reach their goal of delivering equitable care.

La Clínica del Pueblo

La Clínica del Pueblo developed a risk stratification model that identifies three risk levels and refers the subset of high-risk patients to a nurse-led care management program. La Clínica del Pueblo uses EMR extraction tools to identify patients based on medical risk factors and develop a registry (Figure 4A). The risk stratification process routes patients to interventions based on risk level, including health literacy, self-management needs, and patient understanding of diabetes. La Clínica del Pueblo captures social needs data within their EMR. Though not directly integrated into their stratification model, LCDP uses social risk scoring to understand social needs severity before addressing clinical gaps in care. A patient snapshot, including social

Table 1.

TECHNOLOGY AND TARGET POPULATION

Table 2.

DEMOGRAPHIC CHARACTERISTICS OF AT-RISK INDIVIDUALS WITH DIABETES ELIGIBLE FOR INTERVENTIONS AFTER RISK STRATIFICATION

Figure 4A.

Risk Stratification Parameters Linked to Intervention Model: La Clínica del Pueblo

Note

PHQ—Patient Health Questionnaire; SBP—Systolic Blood Pressure; DBP—Diastolic Blood Pressure; LDL—Low-density Lipoproteins; HDL—High-density Lipoproteins; LCDP—La Clínica del Pueblo; DSME—Diabetes Self-Management Education

LCDP stratified patients based on a total score where individual parameter scores are reflected by a numeral. A patient with a PHQ-9 score of 13 (2), current tobacco use (1), and A1c of 8.7% (6) would have a total score of 9 and be considered high risk.

Figure 4B.

Risk Stratification Parameters Linked to Intervention Model: Trenton Health Team

Note

DSME—Diabetes Self-Management Education; ED—Emergency Department; PCP—Primary Care Physician; ESRD—End Stage Renal Disease

Figure 4C.

Risk Stratification Parameters Linked to Intervention Model: Providence Health & Services

Note

PHQ—Patient Health Questionnaire; SBIRT—Screening, Brief Intervention and Referral to Treatment; CRD—Community Resource Desk; ED—Emergency Department; CHF—Chronic Heart Failure; COPD—Chronic obstructive pulmonary disease.

¹Care gaps are considered poor health outcomes that reflect missed opportunities for clinical management of diabetes or 'clinical care gaps'.

needs score and the name of their care manager, is shared in a regional HIE which is accessible to other providers in the area.

Trenton Health Team

Trenton Health Team adapted a risk stratification model using data from the Trenton HIE to identify patients for care management and other interventions in the CCDC.²⁰ Trenton Health Team integrates Medicaid claims data from their designation as a Regional Health Hub and recruits local health care providers to submit data to the HIE to improve overall data quality. Trenton Health Team stratifies patients into six different risk levels; the depth and breadth of the interventions intensify with increasing risk level (Figure 4B). Trenton Health Team established community-wide access to a social needs screening and bidirectional referral platform (NowPow) where social needs screening data are integrated into the HIE. Health information exchange users (e.g., providers, social workers, case managers, care coordinators) from both the community and the clinical partner organization are able to view screening and referral data in the same dashboard as other clinical reports (e.g., primary care encounters, emergency department visits).

Providence Health & Services

Providence implements a two-part risk stratification model to identify high-risk patients. Providence initially uses their EMR to develop patient registries based on medical risk factors, poor health outcomes that reflect missed opportunities for clinical management (e.g., poor HbA1c control), and clinical care gaps (e.g., missing retinal exams) (Figure 4C). These data, along with social risk factors, are integrated into dashboards for clinical care. Patients are further stratified for clinical case management services, based on utilization (e.g., ED visits) and complex comorbidities (Figure 4C). Front office staff or medical assistants complete social needs screening with patients identified for interventions; these data are integrated into the EMR, and EMR-based referrals are sent to CRD staff to address social needs.

Addressing the Complex Medical and Social Needs of High-Risk Patients

Marginalized patients with diabetes have higher burdens of chronic disease (e.g., worse control, more complications, more comorbid conditions) and more complicated social needs (e.g., higher rates of poverty, insurance barriers). Systemic structural inequities drive individual social needs and increase the risk of poor health outcomes. The health care teams combine human capital and technology in two important areas for high-risk patients: addressing additional medical needs (e.g., care coordination) and social needs (i.e., social needs referrals) for complex patients with diabetes.

While some health care roles are similar across the sites, each site has unique roles that support holistic care delivery (Table 3). The staffing models across the three organizations vary due to organizational size and structure, health care financing and reimbursement options and limitations, and the roles each organization prioritizes for their patient populations and interventions to address medical and social needs. For example, LCDP relies on internal medical interpreters to support patients with services at neighboring health systems. Members of the Community-wide Clinical Care Coordination Team provide guidance to the THT interventions and the THT care management team provides support for complex patients by addressing social needs. Providence's clinical case managers coordinate care for complex patients and collaborate with care team members including physicians, population health outreach, and CRD staff to address clinical and social gaps in care.

La Clínica del Pueblo

Patients at LCDP are engaged by the care team to address clinical goals and underlying social needs (Table 3). Care coordinators and health educators collaborate with patients to organize their care, focus on diabetes management, identify treatment goals, and address social needs. Staff use social needs screening results to navigate patients to other interventions and community resources (e.g., housing, legal services, food).

Trenton Health Team

Appropriate patients identified by the risk stratification model are referred for care management services at THT to address identified medical and social needs, peer mentoring to empower behavior change, and community-based peer-led diabetes education. Trenton Health Team also coordinates with local providers and health care staff to strengthen patient-provider relationships, improve diabetic process measures, standardize patient education materials, and support diabetes care across health systems.

Providence Health & Services

When clinical care gaps occur due to lapses in health care services, a Providence population health specialist re-engages the patient in care. Care team members navigate patients to clinical interventions (e.g., retinopathy screening, behavioral health) and identify social needs. Patients with unmet social needs are referred to the Community Resource Desks, located in the clinics, for referral support to community resources.

Table 3.

INTERVENTION CARE TEAM ROLES FOR ADDRESSING MEDICAL AND SOCIAL NEEDS

Evaluating Technology-enhanced Efforts to Provide Equitable Diabetes Care

Providence, LCDP, and THT employ different technological approaches to bridge medical care with social needs interventions for patients with diabetes (Table 1). We used three components of the RE-AIM framework to evaluate the use of risk stratification and the connection to medical and social needs interventions. The RE-AIM framework was developed to evaluate the effect of public health promotion programs and recently has been used to evaluate interventions to address health inequities.^21,22

Reach and Adoption

We used the Reach and Adoption components of RE-AIM to evaluate risk stratification processes and the medical/social needs interventions. Each site reached all of their patients with diabetes with their medical risk stratification tool. Each site was able to identify at-risk individuals with diabetes as well as individuals eligible for interventions based on level of risk (Table 1). Technology supported care teams with proactively reaching at-risk patients who may need additional support from medical/social needs interventions (Tables 1, 2). Eighteen percent of high-risk LCDP patients were engaged in nurse-led care management, and all of these patients were screened for social needs. For THT, 14% of eligible patients were engaged in interventions and all of these patients were screened for social needs. For Providence, 38% of DCII-eligible patients were engaged in interventions and 30% were screened. Technology supported intervention adoption and social needs screening by integrating information into accessible formats for care teams to engage patients in interventions, according to risk level. For example, EMR-based data management tools and medical risk scoring systems (LCDP), integrated records (Trenton HIE), and EMR-driven registries and clinical dashboards (Providence) supported care teams with integrating patient-level information to provide interventions that address medical and social complexities.

Implementation

We used the Implementation component of RE-AIM to contextualize ways in which staff insights validated the data used in risk stratification models and guided implementation of medical/social needs interventions. For example, THT iteratively improves opportunities to capture populations with diabetes and data in the HIE that enable attribution to various levels of the risk stratification model. These iterations include improving staff workflow (e.g., data entry and documentation), improving data integration (e.g., point-of-care to EMR to HIE), and engaging independent providers to include their lab data in the HIE. As the HIE increases the volume of clinical data, patients can more easily, and more accurately, be stratified into different levels. This improves the alignment of risk stratification and the receipt of medical/social needs interventions.

Implementation experiences at each site also revealed gaps between the volume of eligible patients reached and at-risk patients who may benefit from engaging with medical/social needs interventions (Table 1). Part of this gap is due to the iterative, learning nature of quality improvement projects. For example, before enrolling patients, LCDP decided to focus efforts on 50 total patients and use this cohort as an opportunity to determine proof of concept and improve future implementation efforts. In addition, it is important to note that the capacity for in-person care teams is lower than the capacity for outsourcing referrals (e.g., food resources, shelter). The health care teams require significant staff time, which limits the number of patients that can be served at a single point in time. As sites implement and refine their efforts, it will be essential to identify ways to scale staffing models to address the needs of the patient population.

Implications

Organizations preparing to implement risk stratification models and address medical and social factors should evaluate their internal human capital, technology, and processes. The Bridging the Gap: Reducing Disparities in Diabetes Care organizations provide lessons learned for delivering equitable care (Table 4). Each organization discussed the capacity of current staffing models to provide interventions to patients at the highest level of risk. They relied on multidisciplinary care teams with dedicated roles to support both medical and social needs of patients that align with their levels of risk.

Each organization adopted different risk stratification models (e.g., patient-tailored risk scoring, evidence-based, hybrid model) that were dependent on internal priorities and available technology (e.g., EMR, HIE, dashboards). For LCDP, existing patient relationships guided strategies to engage patients. Trenton Health Team engaged patients at community listening dinners to gauge their experiences inside and outside of the clinical setting to improve their diabetes care. Providence staff used health insurance status as a proxy for income, and also reviewed race, ethnicity, and preferred language. Each approach enabled care teams to use technology to address medical and social needs and improve equity in health outcomes.

Conclusions

The health care organizations highlighted in this paper have shown that information technology, in combination with team-based holistic care, can address patients' complex medical and social needs, and prepare organizations to deliver equitable care among the most marginalized populations in the U.S. While many health care organizations have the technology in place,²³ what we lack is the leadership and political will to prioritize social justice and equity in our health systems on a national scale.^24–28 Technology is merely a tool, available to those willing to use it.