IN BRIEF Diabetes continues to represent a substantial individual and societal burden for those affected by the disease and its complications in the United States, and especially for racial/ethnic minorities, the socioeconomically disadvantaged, and the underinsured. Although tools and strategies are now available to manage the condition and its associated comorbidities at the patient level, we continue to struggle to gain control of this health burden at the population health level. Most patients are not achieving desired clinical goals and thus continue to be exposed to preventable risks and complications. As the U.S. health system moves toward a more value-based system of reimbursement, there are opportunities to rethink our approaches to patient and population health management and to harness the available tools and technologies to better understand the disease burden, stratify our patient populations by risk, redirect finite resources to high-impact initiatives, and facilitate better diabetes care management for patients and providers alike.
One in 10 adults in the United States has diabetes (1), and the prevalence is projected to double or even triple by 2050 (2). The total estimated cost of diagnosed diabetes is $327 billion, representing a steep 26% increase from 2012 (3). It is well known that the optimization of cardiometabolic control, as demonstrated by improvements in A1C, lipids, and blood pressure, can achieve significant reductions in morbidity and mortality (4–6). However, despite significant advances in treatment during the past decade, clinical risk factor control remains suboptimal in diabetes (7). According to the National Health and Nutrition Examination Survey, approximately half (53%) of adults ≥20 years of age with diabetes had an A1C <7%, and only 19% met all targets for A1C, lipids, and blood pressure (7). The 2017 National Diabetes Statistics Report estimated that 15.6% of adults with diabetes in the United States have an A1C >9% (1), which is associated with a significant risk of developing vascular complications (8). Furthermore, significant diabetes disparities are experienced by socioeconomically disadvantaged racial/ethnic minority populations in the United States. In particular, African Americans and Hispanics with diabetes generally exhibit poorer glycemic control and outcomes relative to non-Hispanic whites (7,9–11).
Current diabetes statistics are certain to overwhelm health care systems and diabetes care workforces that already struggle to keep pace with growing demand. Innovative models are needed to overcome care delivery deficiencies such as providing care at scale, rushed practitioners challenged to follow evidence-based practices, a lack of follow-up and care coordination, and patients who are inadequately trained to manage their condition(s) (12).
Overcoming these limitations will require nothing less than a transformation of health care, from a reactive system to one that is proactive, integrated, and focused on keeping people with diabetes as healthy as possible (12,13). Part of the transformation in health care will have to come from the ongoing transition from a fee-for-service model to a value-based system of compensation to address the Quadruple Aim of improved outcomes, lower cost, and improved patient and provider (workforce) experience (14). In a value-based health system, the “health outcomes of a group of individuals, including the distribution of such outcomes” (i.e., population health) will need to drive policies and interventions and resource allocations (15).
The Chronic Care Model (CCM) is a framework underpinning the Quadruple Aim that can be used to systematically guide this transformation and improve care for chronic conditions. Four CCM elements address practice strategies (organizational support, clinical information systems, delivery system design, and decision support), whereas two elements are patient-focused (self-management support and community resources) to highlight the significant portion of chronic care management that takes place outside of the clinic. Research examining the CCM and demonstrating improvements in clinical, behavioral, and cost outcomes in diabetes has been reviewed (16), including among underserved patients (17,18).
This article highlights several completed or in-progress population-based, care management approaches designed to operationalize CCM elements and improve outcomes in a high-risk group, namely low-socioeconomic-status, racial/ethnic minority individuals with diabetes. The team-based approaches reviewed here integrate a variety of strategies to achieve this end, including optimization of treatment regimen and adherence, electronic medical record (EMR)-driven risk stratification and panel management, and health coaching. This review concludes with examples of innovative professional education formats that can be used to introduce and maintain the integrity of these approaches in real-world health systems.
Optimization of Treatment Regimen and Adherence
An exemplar approach for treatment optimization was developed at Parkland Health & Hospital System, an integrated health care system that provides care for the underserved and uninsured residents of Dallas County, TX. Parkland is one of the largest public hospital systems in our nation and is the primary teaching hospital for the University of Texas Southwestern (UTSW) Medical Center. Parkland owns and operates a new, state-of-the-art, 2.8-million-square-foot, 870-bed hospital on its main campus; in 2017, Parkland registered a daily average of 669 emergency department visits, more than 205 hospital discharges, and more than 34 infant deliveries (19). Parkland also runs 20 community-based clinics, which include primary care, geriatric and women’s clinics, and 12 school-based clinics.
Twelve community-oriented primary care (COPC) clinics with official medical home designations are strategically located in underserved communities throughout Dallas County to facilitate access to high-quality, affordable care. The health centers provide care for patients of all ages, offering pediatric, adolescent, adult, and senior care services. Pharmacy services are available on location in some COPC clinics. Financial counseling services are provided to patients to link them to appropriate federal and state-funded insurance programs; Parkland provides charity care to economically disadvantaged individuals and families. To ensure cultural sensitivity in the provision of care, Parkland staffs each clinic with health professionals whose demographic backgrounds match the neighborhood communities served. Approximately 60% of physicians are African American, Hispanic, or Asian; 54% are female; and more than half speak both English and Spanish.
In 2017, a total of 1,046,806 outpatient visits were completed at Parkland, of which 345,681 were seen in specialty care and 452,412 were seen in COPC clinics. Parkland oversees an extensive mobile health care program that treats Dallas County’s homeless population and has responsibility for health care provision to Dallas County Jail (average daily census 6,500) for both adults and juveniles (19).
The payer mix at Parkland reflects the nature of a large, public hospital in a large, urban city. Specifically, 33% of patients receive charity care, 31% have Medicaid, 16% are on Medicare, 12% self-pay, and 8% have commercial insurance. In fiscal year 2017, Parkland provided about $880 million in uncompensated care (19).
The Global Diabetes Program (GDP) at Parkland was created in 2014 and funded through Medicaid Section 1115 waiver funds that targeted several diabetes-specific metrics. The goal of the GDP is to organize, coordinate, and standardize approaches in diabetes management and education across the health system, including the acute inpatient setting, the diabetes specialty clinics, and the network of primary care clinics. The GDP’s clinical service engine is based on an advanced practice provider (APP) model that is overseen and supported by the program’s faculty (UTSW Medical Center endocrinologists), as well as a multidisciplinary team of inpatient and outpatient nurse and dietitian certified diabetes educators (CDEs), a licensed clinical social worker, and a clinical pharmacist. The APPs staff the inpatient diabetes consultation service, which functions in coordination with the endocrine fellow consultation service, as well as the diabetes specialty clinics. A weekly, half-day fellow clinic staffed by UTSW faculty is held within the diabetes clinic. The clinic accepts referrals for patients with type 1 diabetes, gestational diabetes requiring insulin, uncontrolled type 2 diabetes (A1C >9%) despite insulin therapy, advanced chronic complications of the disease (including active foot ulcers or infections), post-transplant diabetes, and those needing rapid perioperative control or with diagnostic dilemmas.
Parkland’s diabetes registry includes more than 36,000 “active” patients (those having an office visit within the system in the past 12 months), with 95% classified as having type 2 diabetes. The majority of patients identify as Hispanic (56%) or African American (29%), and the rest are white or Asian; a similar distribution is seen for those diagnosed with type 1 diabetes (Table 1).
Patients With a Diagnosis of Diabetes Within the Parkland Health & Hospital System in 2018*
| Type 1 Diabetes | Type 2 Diabetes | |
|---|---|---|
| Active patients in diabetes registry, n | 1,860 | 34,946 |
| Females, % | 56 | 59 |
| Race/ethnicity | ||
| Hispanic | 51 | 56 |
| African American | 31 | 29 |
| Non-Hispanic, non–African American | 18 | 15 |
| Age distribution | ||
| ≤18 years | 4 | <1 |
| 19–45 years | 45 | 19 |
| 46–65 years | 41 | 59 |
| >65 years | 10 | 22 |
| A1C range | ||
| ≤7% | 27 | 39 |
| 7.1–9.0% | 34 | 35 |
| >9% | 39 | 27 |
| Type 1 Diabetes | Type 2 Diabetes | |
|---|---|---|
| Active patients in diabetes registry, n | 1,860 | 34,946 |
| Females, % | 56 | 59 |
| Race/ethnicity | ||
| Hispanic | 51 | 56 |
| African American | 31 | 29 |
| Non-Hispanic, non–African American | 18 | 15 |
| Age distribution | ||
| ≤18 years | 4 | <1 |
| 19–45 years | 45 | 19 |
| 46–65 years | 41 | 59 |
| >65 years | 10 | 22 |
| A1C range | ||
| ≤7% | 27 | 39 |
| 7.1–9.0% | 34 | 35 |
| >9% | 39 | 27 |
Data are % unless otherwise indicated.
Active patients in the diabetes registry are defined as those having a completed office visit in 2018.
The first step for any health system that needs to understand how to affect the health of its population is to understand the inherent needs of those it serves by collecting data from sources such as EMRs, pharmacy and insurance claims databases, or other data repositories (20,21) and risk-stratifying its population to identify and prioritize care gaps and drive appropriate intervention.
In 2014, Parkland partnered with the American Diabetes Association and iMD (intelligent Medical Decisions) in a quality improvement initiative called Diabetes INSIDE (INspiring System Improvement through Data-driven Excellence) to address poor glycemic control within the primary care setting in the health system (22). We queried the diabetes registry for baseline data and analyzed the findings, which identified that 82% of patients with an A1C >9% were not prescribed insulin therapy. Next, we coordinated and organized a Diabetes Outpatient Quality Improvement (DOQ-In) Committee with multidisciplinary representation from all COPC sites. The committee reviewed the findings and came to a consensus regarding the focus of the initiative and potential interventions (22).
After open debate and discussion, we agreed to focus on therapeutic inertia surrounding insulin initiation and intensification by using a shared medical appointment (SMA) approach (23–25). We held several workshops to increase provider and multidisciplinary team knowledge, problem-solving skills, and comfort levels with initiating and intensifying insulin therapy in patients with type 2 diabetes. We also trained sites on how to properly conduct an SMA, created workflows and processes to support these efforts (e.g., templated smart notes and referral order sets), and developed an SMA facilitator guide.
Of the 12 primary care sites included in DOQ-In, eight clinics achieved an insulin initiation or intensification rollout, with one clinic not adopting any of the SMA processes or formats developed by the group. Between January 2015 and March 2016, the COPCs conducted 240 SMA sessions for 899 unique patients (Figure 1). Starting in early 2016, we observed an increase in the proportion of patients with an A1C >9% who were prescribed insulin therapy, with parallel improvements in mean A1C and a reduction in the percentage of patients with poor glycemic control (A1C >9%) (26). All of these changes became statistically significant over time (Figure 2) (27). We believe it was the focus on insulin initiation and associated educational activities, more than the SMA approach itself, that moved the needle with regard to primary care providers (PCPs) starting insulin treatment in appropriate patients.
Clinical outcomes after insulin initiation and intensification quality improvement project (control charts) (27).
Clinical outcomes after insulin initiation and intensification quality improvement project (control charts) (27).
Medication adherence was another huge gap in care that Parkland identified early on in this initiative (28–31). Very few systems or practices are able to objectively assess, within their workflows, adherence to prescribed medication for treatment of chronic conditions. Evidence shows that many patients discontinue prescribed medications without informing their health care provider and that this lack of adherence translates into worsening disease outcomes and increased costs of care (31–34). Another potential consequence of undisclosed non-adherence is overprescribing of medication (either in dose or number of medications), as providers intensify treatment without ascertaining that patients are actually taking their prescribed therapy.
We therefore developed a tool based on the concept of proportion of days covered (PDC) (35) to indirectly assess medication adherence for our patients. The tool, known as P-SAM (Parkland Score for Adherence to Medication), queries both internal pharmacy data and data provided by Surescripts on outside pharmacy fills to calculate a PDC percentage, which is displayed in the EMR so providers and other health care staff can quickly identify medication nonadherence.
Identifying low adherence allows clinicians to query patients on barriers to nonadherence, which can include medication cost, missed refill appointments, inability to get to the pharmacy, lack of information about a medication’s purpose, side effects, and patient forgetfulness (36). When specific barriers to medication adherence are identified, potential solutions and resources can be offered (37). We also plan to use information on medication adherence to identify patients who may need treatment intensification or deintensification (Figure 3).
The medication adherence score is displayed in a number of interfaces in the EMR, including the Diabetes Overview snapshot, which is a single page that dynamically collects essential data needed to assess a patient’s diabetes status (Figure 4). Instead of having to navigate the EMR for information needed during a clinic visit, a patient’s vitals and metabolic values (current and historical), medications, foot and retinal screening dates and results, depression screening, past and upcoming visits within the health system, and overdue or upcoming health maintenance issues (e.g., flu vaccination, colonoscopy, and pap smears) are organized in a user-friendly display. The information on the Diabetes Overview snapshot is updated in real time and considerably improves a health professional’s efficiency in gathering data needed for a diabetes assessment and planning intervention (Figure 4).
EMR-Driven Risk Stratification and Panel Management
Evidence suggests that tailoring interventions according to individuals’ clinical risk represents a cost-effective way to provide care at scale for the ever-growing numbers of individuals with chronic disease (38–40). The improving functionality of EMR systems offers an increasingly efficient way to identify and stratify at-risk people in a health care setting (38). A project underway at Scripps Health is examining the clinical and financial benefits of embedding an integrated care team to improve clinical control among patients with diabetes in an EMR-equipped primary care environment. Scripps Health is a large health system in San Diego, CA, that comprises four hospitals on five campuses, with nearly 70,000 admissions each year. Two large partnering medical groups use a single EMR (Epic) and deliver primary and specialty care services to more than 700,000 patients per year.
The Cardiometabolic Care Team Intervention at Scripps Health includes a registered nurse (RN) CDE care manager, a medical assistant (MA) health coach, and an RN depression care manager working at top of scope alongside PCPs to optimize treatment and provide self-management support to moderate- and high-risk patients with diabetes, stratified according to A1C, blood pressure, and LDL cholesterol data from the EMR. These strata are used to filter and prioritize higher-risk patients on a clinical data dashboard for more immediate and frequent outreach. Patients with all metabolic parameters at target are seen quarterly, whereas others are followed more frequently by phone by the MA health coach and in-clinic by the RN/CDE care manager. Decision-support tools guide therapy for glucose, blood pressure, LDL cholesterol, and depression. When medication changes are indicated, the RN/CDE care manager issues an EMR “task” to the PCP and, if approved, proceeds with the modification. Patients who screen positive for depressive symptomatology are referred to the RN depression care manager for triage and treatment, as indicated. An in-progress evaluation will examine the clinical, behavioral, and cost-effectiveness of this model, as well as feasibility and acceptability by patients and the primary care team.
Health Coaching
Bodenheimer and Smith (41) propose that the burgeoning primary care demand-capacity imbalance can be reduced by training nonlicensed clinic personnel to serve as panel managers and health coaches (41). Team-based care, or empowering nonclinicians to “share the care,” is posited as a foundational element that improves chronic disease care processes and outcomes. Indeed, many U.S. settings are shifting responsibilities to MAs, one of the fastest growing and widely available allied health professions, and implementing higher MA/physician ratios. Although health coaching interventions vary, the most common elements are goal-setting, individualized care, and frequent follow-up (42).
Recent reviews have summarized the effectiveness of health coaching for chronic disease outcomes overall (43) and for diabetes specifically (44), including among underserved groups (45,46). Research that involved specifically training nonclinicians (e.g., MAs) to function as health coaches has found this approach to improve A1C by 1.1% over 6 months among low-income patients (47) and identified an association between higher health coaching frequency and greater blood pressure reductions in predominantly ethnic minority patients (48). A recent, larger trial demonstrated that in-clinic MA health coaching improved medication adherence, A1C, and cholesterol in low-income, predominantly racial/ethnic minority patients (49,50), with clinical improvements sustained at 1 year beyond the coaching intervention (51). Qualitative studies have highlighted methods for enhancing the success of MA health coaches (52) and have also provided evidence of PCPs’ satisfaction with (and acceptance of) delegating health coaching responsibilities to nonclinician staff (53).
Notably, MA health coaching has been shown to enhance patients’ perceptions of CCM elements, with the largest improvements reported among Spanish-speaking (versus English-speaking) patients, perhaps because bilingual MA health coaches served to reduce language and cultural barriers often encountered by non-English speakers in health care settings. Indeed, it has been proposed that MA health coaches serve as cultural brokers or liaisons between patient and provider, enhancing the relevance and benefit of this model for underserved populations. Importantly, a large, pragmatic trial (National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Disease grant 5R18DK104250-04, principal investigators Athena Philis-Tsimikas and Linda C. Gallo) is currently underway that integrates EMR-driven panel management and risk stratification with MA health coaching for diabetes in two very distinct primary care settings, including a federally qualified health center that serves a predominantly Hispanic, low-income patient population. This study will provide valuable data on the feasibility, acceptability, and clinical and cost-effectiveness of MA health coaching for diabetes in a real-world, underserved health setting without the research constraints and influence inherent in a traditional randomized controlled trial.
Professional Education
Professional education and management updates are essential to effectively translate evidence into practice, although traditional continuing education programs have had little impact (54). We sought to understand primary care practice gaps and respond to them with targeted education by piloting e-consultations (55,56) combined with biweekly, 30-minute webinars (loosely styled after the ECHO model [57]). This activity provides specific support and advice in a timely manner to PCPs for patients who would otherwise have received delayed consultations. COPCs identify provider and support staff champions who, along with anyone else at their sites who has an interest, participate in these webinars. The webinar content is based on specific issues identified from referring physicians’ e-consultation requests; this provides the opportunity for discussing relevant topics and specific solutions, including the appropriate use of newer treatments in high-risk patients. These periodic, targeted educational interactions are succeeding in catalyzing practice change as providers and support staff are becoming more facile at appropriately prescribing glucagon-like peptide 1 receptor agonists and sodium–glucose cotransporter 2 inhibitors and educating their patients on their administration and expected outcomes.
Summary
Health care delivery is undergoing major change to address identified needs for improved, outcome-driven, value-based care that focuses on efficiencies, effectiveness, and patient and care team satisfaction. Initiatives, including EMR-driven risk stratification and panel management, treatment regimen optimization and adherence, and culturally centered health coaching, all have an integrated goal of achieving value-based outcomes and use strategies to best stratify patient, care team, and health systems based on disease acuity/severity, health risks, and other variables that might affect care and care delivery.
The examples given above demonstrate that combining initiatives achieves higher success in goal achievement. An important component in these initiatives relates to ongoing workforce training and leveraging, to ensure integrated, team-based, person-centered care delivery. The individualized needs of a diverse population can make the transition of care to effective population health challenging, requiring targeted and culturally appropriate engagement, communication, and intervention approaches. Having an information technology infrastructure to capture and connect required data, both within and external to the EMR, to drive efficiencies and outcomes requires major consideration. However, success requires navigating these challenges to identify creative and alternative models that can direct care to when and where it is needed to best meet patient and system needs. Collaborative partnerships to promote standardized approaches to registry development, data collection, and EMR reporting are crucial to evaluate internal progress as well as benchmark comparisons with other systems.
More research is needed to demonstrate the longer-term impact of population health strategies on patient morbidity, mortality, and quality of life, as well as important system benefits in terms of efficiency, productivity, satisfaction, and cost.
Acknowledgment
Dr. Uma Gunasekaran, lead physician for the diabetes clinic at Parkland, has been an integral part of the success of the Global Diabetes Program.
Duality of Interest
No potential conflicts of interest relevant to this article were reported.
Author Contributions
L.F.M., A.L.F., and T.L.C. reviewed the literature and wrote the article. K.R. contributed to editing the manuscript and providing additional content. L.F.M and A.L.F are the guarantors of this work, had full access to all the data reported, and take responsibility for the integrity of the content of this review.
