IMPACT-DM: Improving Management of Poorly Controlled Type 2 Diabetes (A1c ≥9%) Through Coordinated,Team-Based Care in Federally Qualified Health Centers

Abstract

Uncontrolled type 2 diabetes disproportionately affects underserved populations and contributes to substantial morbidity, mortality, and healthcare costs. In Federally Qualified Health Centers (FQHCs), infrequent follow-up and inconsistent use of real-time glycemic data limit timely treatment intensification. In a multi-site FQHC serving underserved communities, baseline data demonstrated persistently high rates of uncontrolled diabetes (A1c ≥9%) despite access to in-house point-of-care A1c testing. Provider-dependent workflows, inconsistent follow-up, and low patient engagement were identified as key barriers to sustained glycemic control.

Objective

To evaluate the impact of a monthly, interprofessional follow-up model incorporating medical assistant (MA)–led point-of-care A1c testing and clinical pharmacy integration on glycemic control and cardiometabolic outcomes in an FQHC setting.

Methods

A 12-month quality improvement initiative using the Plan–Do–Study–Act framework was implemented across multiple FQHC sites. Adults with type 2 diabetes and A1c ≥9% were enrolled. The primary outcome was the inverse diabetes quality measure (A1c ≥9%). Secondary outcomes included A1c distribution, provider-level A1c improvement, blood pressure control (<130/80 mmHg), and LDL cholesterol control (<100 mg/dL).

Results

An initial four-month pilot cohort of 138 patients with A1c ≥9%, managed by two primary care providers, demonstrated rapid glycemic improvement, with 81.8% achieving A1c <9%. These findings informed workflow refinement and organization-wide implementation over the subsequent year, during which the inverse diabetes quality measure improved from 24.1% to 18.6% (absolute reduction of 5.5 percentage points). Concurrent improvements were observed in blood pressure control (36.0% to 40.6%) and LDL cholesterol control (45.0% to 47.0%).

Conclusions

A monthly interprofessional follow-up model with MA-led in-house A1c testing and clinical pharmacy integration was associated with improved glycemic and cardiometabolic outcomes in an FQHC setting. This scalable approach supports value-based chronic disease management in underserved populations.

Keywords

community health Diabetes Mellitus disease management Federally Qualified Health Center (FQHC)medical assistant point-of-care testing rural health team-based care underserved populations value-based care

Introduction

Type 2 Diabetes mellitus remains one of the most prevalent and costly chronic conditions managed in primary care. Poor glycemic control is associated with increased microvascular and macrovascular complications, higher mortality, and substantial healthcare utilization, disproportionately affecting underserved populations receiving care in Federally Qualified Health Centers (FQHCs).¹

Hemoglobin A1c (A1c) is a cornerstone biomarker for assessing long-term glycemic control, reflecting a weighted average of blood glucose exposure over approximately three months. Traditionally, A1c is reassessed every 3–6 months; however, delayed availability of results and infrequent reassessment may limit timely treatment adjustment for patients with markedly elevated A1c levels. Importantly, A1c is not a simple average: glycemia in the most recent 30 days contributes approximately 50% of the measured value, with progressively smaller contributions from earlier periods, supporting the potential value of monthly A1c assessment in patients with poor glycemic control.^2,3

Standard diabetes follow-up intervals of three to six months may be inadequate for patients with A1c ≥9%. Evidence supports that more frequent follow-up, particularly when paired with rapid A1c availability, improves clinical decision-making and glycemic outcomes in this high-risk population.^4-7

Team-based care models that integrate medical assistants and clinical pharmacists into chronic disease management have demonstrated improved diabetes outcomes and workflow efficiency in primary care settings.^8-15 Medical assistants play an expanding role in population health and protocol-driven testing, while clinical pharmacists contribute specialized expertise in medication management, titration, and patient education.

Patients with poorly controlled type 2 diabetes frequently report willingness to engage in more frequent visits and therapy adjustments and express frustration when treatment escalation is delayed.¹⁶ These findings suggest that structured, frequent follow-up aligns with patient preferences and may enhance engagement.

National guidance from the American Diabetes Association (ADA) emphasizes regular reassessment of therapy, timely treatment modification, and team-based care for individuals not meeting glycemic targets. The ADA Standards of Care in Diabetes—2025 recommend avoiding delays in treatment intensification and reassessing treatment effectiveness and adherence at regular intervals, underscoring the need for care models that enable frequent follow-up and real-time data access in primary care settings. In addition, ADA emphasizes that effective diabetes care relies on collaboration among a multidisciplinary team, which may include primary care clinicians, pharmacists, nurses, dietitians, behavioral health professionals, and other specialists. Such coordinated care facilitates individualized treatment planning, improves communication, and enhances patient engagement, ultimately supporting better clinical outcomes and quality of life.¹⁷

This quality improvement initiative implemented a monthly interprofessional diabetes follow-up model incorporating medical assistant–led point-of-care A1c testing, clinical pharmacy integration, structured appointment management, and team-based coordination to improve outcomes in an FQHC setting.^18,19

Objectives

Primary Objective

To improve glycemic control among adults with type 2 diabetes and A1c ≥9% by replacing standard 3–6-month follow-up with a monthly interprofessional follow-up model incorporating MA-led point-of-care A1c testing and clinical pharmacy integration.

Secondary Objectives

1. Improve population-level diabetes quality metrics

2. Enhance concurrent management of cardiometabolic comorbidities (blood pressure and LDL cholesterol)

3. Improve patient engagement and follow-up adherence through team-based care

Methods

Setting and Context

This initiative was implemented across multiple Federally Qualified Health Center (FQHC) sites in Maricopa and Pinal Counties, Arizona, including rural communities in Pinal County. The clinics serve underserved populations, with approximately 75% of patients living below 200% of the federal poverty level and 62% identifying as racial and/or ethnic minority patients. At baseline, 15% of the patient population had a diagnosis of type 2 diabetes, and 19% had hypertension.

Design and Population

Using the Plan–Do–Study–Act (PDSA) framework, primary care teams conducted a 12-month phased implementation that began with a small pilot cohort managed by two primary care providers, followed by organization-wide adoption. Adults (≥18 years) with type 2 diabetes and A1c ≥9% in the prior year were identified using the Azara DRVS population health platform and electronic health record reports.

Eligible participants included nonpregnant adults aged ≥18 years with type 2 diabetes who were actively engaged in care, defined as at least one completed visit in the prior 12 months. Patients established with endocrinology who wished to continue specialty care, as well as those receiving hospice or long-term care services, were excluded.

Intervention Components

Monthly Follow-Up Visits

Eligible patients were scheduled for monthly visits with their primary care provider and/or clinical pharmacist until A1c improved below 9% (Figure 1).

Figure 1.

Monthly interprofessional diabetes follow-up intervention

MA-Led In-House A1c Testing

Medical assistants underwent standardized training on diabetes workflows, point-of-care A1c testing, eligibility criteria (A1c ≥9%), documentation, and escalation pathways prior to implementation. Under standing orders, MAs performed in-house point-of-care A1c testing during rooming for eligible patients, eliminating provider-initiated ordering.^9,10 Protocol adherence was reinforced through written workflows, competency validation, and clinical supervision. MAs identified eligible patients, ensured real-time result availability, and flagged patients due for monthly follow-up. Adherence was monitored through chart review and team huddles, with workflow refinements to maintain cross-site consistency (Figure 1).

Clinical Pharmacy Integration

Under Arizona Senate Bill 1112 and medical director procedure approval, clinical pharmacists were authorized to initiate, adjust, or discontinue diabetes medications (including insulin), order laboratory tests, and manage therapy in accordance with ADA Standards of Care in Diabetes—2025 and manufacturer guidance.¹⁷ Pharmacists addressed adherence, cost and formulary barriers, ordered laboratory monitoring, and provided medication education and titration. Care was delivered through in-person and telehealth encounters, including shared or separate visits with providers.^12,15,20

Team Coordination

Daily team huddles and monthly care reviews supported pre-visit planning, alignment of care plans, and task delegation across the interprofessional team.¹⁴

Appointment Management

Missed visits were proactively rescheduled through targeted outreach to support continuity and engagement.²¹

Measures

• Primary Outcome: Inverse diabetes quality measure (A1c ≥9%)

• Secondary Outcomes: A1c distribution, provider-level A1c improvement, blood pressure control (<130/80 mmHg), and LDL cholesterol control (<100 mg/dL)

• Analyses were descriptive in nature and based on pre–post comparisons over the 12-month period. No inferential statistical testing was performed.

Ethical Considerations

This project was reviewed as a quality improvement initiative and did not require institutional review board approval. No ethical concerns or publication misconduct were identified. The conclusions are supported by the data. The report follows SQUIRE 2.0 guidelines for QI studie.

Results

Pilot Cohort Outcomes

A total of 138 patients with poorly controlled diabetes (A1c ≥9%) were enrolled in the initial pilot cohort managed by two primary care providers. Progressive improvement in glycemic control was observed over four months. By the end of month four, 81.8% of patients (113 of 138) achieved A1c values below 9%, demonstrating a marked shift toward improved glycemic control across the cohort (Figure 2).

Figure 2.

Impact of monthly follow-up on poorly controlled DMT2 (A1c ≥9%) in an initial 4-month pilot cohort

Population-Level Outcomes

Following scale-up across the organization, population-level improvements were observed over 12 months. The inverse diabetes quality measure improved from 24.1% at baseline to 18.6%, representing a 5.5 percentage-point absolute reduction (Figure 3). Blood pressure control (<130/80 mmHg) improved from 36.0% to 40.6%, and LDL cholesterol control (<100 mg/dL) improved from 45.0% to 47.0% (Table 2).

Figure 3.

Provider-level percentage improvement in patients with A1c ≥9% over 12 months

A1c Distribution

Shifts in A1c category distribution demonstrated improvement across the glycemic spectrum. The proportion of patients achieving controlled diabetes (A1c <6.9%) increased from 45.1% to 51.1%, representing a 6.0 percentage-point absolute improvement. Intermediate A1c categories remained stable (Table 1).

Table 1.

Distribution of A1c Categories at Baseline and 12 Months

A1c category	Baseline (n)	Baseline (%)	12 months (n)	12 months (%)
A1c ≥9%	1,677	24.9%	1,205	19.2%
8.0–8.9%	704	10.4%	650	10.3%
7.0–7.9%	1,317	19.5%	1,220	19.4%
<6.9%	3,044	45.1%	3,214	51.1%

The combined category (A1c ≥9%) reflects the inverse diabetes quality measure used in UDS and other value-based reporting frameworks; lower values indicate improved performance. The total denominators were baseline (N = 6,744) and 12 months (N = 6,289).

Provider-Level Outcomes

All eligible family medicine panels demonstrated improvement in glycemic control over 12 months, with percentage improvement in mean panel A1c ranging from 0.2% to 14.9% (Figure 3).

Discussion

This quality improvement initiative demonstrates that a monthly, interprofessional diabetes follow-up model integrating medical assistant–led point-of-care A1c testing, clinical pharmacy support, structured appointment management, and team-based coordination was associated with meaningful improvements in glycemic control and cardiometabolic outcomes in an FQHC setting.

The pilot cohort demonstrated rapid improvement, with most patients achieving A1c <9% within four months, highlighting the impact of frequent reassessment and timely treatment adjustment in high-risk populations. Patients in this cohort also showed higher follow-up visit completion rates, suggesting improved engagement associated with the high-frequency, team-based care model. Findings from the pilot informed workflow refinement prior to expansion across additional provider teams and likely contributed to the population-level improvements observed during broader implementation.

The observed 5.5 percentage-point improvement in the inverse diabetes quality measure reflects both improved follow-up completion and a reduced proportion of patients with very poorly controlled diabetes. Importantly, the 6 percentage-point increase in the proportion of patients achieving A1c <6.9% suggests a population-level shift toward improved glycemic control rather than isolated improvement among lower-risk patients.

Monthly follow-up created repeated opportunities for reassessment and action, while real-time A1c availability supported timely clinical decision-making during visits. Clinical pharmacy integration under expanded scope of practice enabled rapid titration of insulin and oral agents, laboratory monitoring, and focused patient education. Pharmacist-led care was delivered through both independent and shared visits, supporting continuity of care and operational feasibility within the FQHC setting.

The expanding role of medical assistants was also central to the feasibility of this intervention. Prior workforce and care transformation studies demonstrate that medical assistants can effectively support chronic disease management when roles are clearly defined and supported by standardized training and protocol-driven workflows.^11,12 In this initiative, MA-led point-of-care A1c testing ensured consistent access to real-time glycemic data during visits, reduced workflow bottlenecks, and enabled timely treatment intensification by the interprofessional care team.

Improvement across all eligible family medicine panels suggests that the observed gains reflected a system-wide effect rather than reliance on individual champions. Variability in the magnitude of improvement likely reflects differences in provider adoption and acceptance of clinical pharmacy integration into chronic care management, underscoring the importance of implementation strategies that strengthen change management, role clarity, and team-based workflows.

Improvements in glycemic control were accompanied by gains in blood pressure and lipid outcomes. These findings are consistent with robust evidence demonstrating that pharmacist-integrated care models are among the most effective team-based strategies for blood pressure control, largely by addressing clinical inertia through guideline-driven medication titration and close follow-up (Table 2). Recent meta-analytic evidence shows that pharmacist-led interventions achieve greater blood pressure reductions than physician- or nurse-led models, supporting the role of clinical pharmacy integration in improving cardiometabolic outcomes alongside glycemic control.²² Within chronic care management models, clinical pharmacists are also positioned to adjust antihypertensive therapy and recommend initiation or intensification of statin therapy for eligible patients in accordance with American Diabetes Association guideline recommendations, enabling more comprehensive cardiometabolic risk reduction during high-frequency follow-up.¹⁷

Table 2.

Secondary Cardiometabolic Outcomes at Baseline and 12 Months

Secondary outcome	Baseline (n/N)	Baseline (%)	12 months (n/N)	12 months (%)
Blood pressure <130/80 mmHg	2,425/6,744	36.0%	2,554/6,289	40.6%
LDL cholesterol <100 mg/dL	3,047/6,744	45.0%	2,933/6,289	47.0%

While monthly follow-up may increase demands on patients, including time, transportation, and potential financial burden, these factors were actively addressed within the care model. Care teams were instructed to identify social determinants of health that could impact participation, and alternative visit options, including telehealth and telephone follow-up, were offered to mitigate barriers such as transportation, scheduling, or copay concerns. Patients generally expressed satisfaction with the intervention, suggesting that the perceived benefits of more frequent, team-based care outweighed the added burden. As part of ongoing Plan–Do–Study–Act (PDSA) cycles, a formal patient survey is being developed to systematically assess patient experience and inform continued practice improvement.

Finally, the structure of this intervention aligns with ADA guidance emphasizing regular reassessment and timely modification of therapy for individuals not meeting glycemic targets.¹⁷ These findings demonstrate that ADA recommendations can be effectively operationalized through monthly follow-up, protocol-driven testing, and interprofessional collaboration within routine FQHC workflows.

Limitations

This initiative was observational and conducted within a single FQHC system, which may limit generalizability. The frequency of encounters with primary care providers versus clinical pharmacists varied based on scheduling and availability; although the intended model emphasized monthly pharmacist follow-up with quarterly primary care visits, this cadence was not consistently achieved. Variability in PVP was driven by medical assistant staffing constraints, including shortages and cross-coverage needs. Similarly, clinical pharmacy visit variability reflected site-level resource differences, as some clinics lacked full-time pharmacist coverage and required cross-site support. Outcomes were assessed during active participation in the monthly protocol, and the durability of glycemic control after transition to routine follow-up could not be evaluated. The decrease in the denominator from 6,744 to 6,289 reflects changes in the active patient population over 12 months, defined as having at least one completed visit within the prior year. Patients without recent encounters were classified as inactive and excluded by the EHR. Initial review suggests these patients were more likely to have less severe baseline glycemic abnormalities; therefore, this shift is unlikely to overestimate the observed improvements and may represent a conservative estimate of the intervention’s effect. Patient-reported outcomes and long-term sustainability were not formally assessed. In addition, although current ADA guidelines recommend an LDL cholesterol goal of <70 mg/dL for high-risk patients, the Azara DRVS platform was not configured to capture this threshold during the study period, limiting precision in lipid outcome assessment.

Conclusion

A high-frequency, team-based diabetes follow-up model incorporating MA-led point-of-care A1c testing and clinical pharmacy integration was associated with meaningful improvements in glycemic control and concurrent cardiometabolic outcomes in a high-risk population served by an FQHC. By replacing provider-dependent testing with protocol-driven workflows and leveraging interprofessional team members at the top of their scope, this approach enabled timely treatment intensification using real-time data. The model proved feasible and scalable across multiple clinic sites and demonstrates how guideline-concordant, value-based diabetes care can be operationalized within routine primary care workflows in resource-constrained settings.

Footnotes

Acknowledgments

The authors acknowledge the medical assistants, clinical pharmacists, primary care providers, and population health teams at Sun Life Health for their contributions to the implementation of this initiative.

ORCID iD

Jose Rapanan

Ethical Considerations

This project was reviewed and determined to be a quality improvement initiative and did not require institutional review board approval.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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