Why are our A1C control rates dropping?

Declining A1C control rates — the percentage of diabetic patients with A1C below 8% or 9% — usually indicate one of three issues: a shift in patient panel composition toward higher-acuity new patients, medication compliance degradation (often correlated with formulary changes or cost increases), or lab follow-up gaps where patients are screened but not retested at the guideline-recommended 3-month interval. Approximately 23% of "uncontrolled" patients are actually control-unknown due to testing timing alone.

Panel composition shift is the most commonly misdiagnosed cause of declining A1C control rates. When a practice grows by adding new diabetic patients — through panel expansion, new provider hiring, or absorption of another practice — the incoming patients typically have worse glycemic control than the established panel. The denominator grows faster than the controlled numerator, depressing the control percentage even if every existing patient is being managed well. A practice that adds 200 new diabetic patients with an average A1C of 9.2% to an established panel of 800 patients with 62% control will see its control rate fall to approximately 55% before any patient receives new treatment — a purely mathematical effect of panel growth that looks identical to clinical failure in a simple control rate report.

Medication compliance degradation has become a more significant driver since 2023 due to GLP-1 agonist and SGLT2 inhibitor formulary changes. Cost increases for these medications have driven formulary-based switches for Medicaid and high-deductible commercial patients that temporarily destabilize glycemic control during the transition period. Practices with high Medicaid panels experience this most acutely because Medicaid formulary changes occur without direct patient notification in many states, meaning patients may discover at pharmacy pickup that their prescription is no longer covered — and simply not fill it rather than calling the office for an alternative.

A1C control rate as a single number conceals three distinct sub-populations that require different interventions. Genuinely uncontrolled patients who need intensified treatment represent one group — these patients need medication adjustment, not reminders. Patients with testing gaps who may actually be controlled represent a second group — the HEDIS HbA1c control measure requires testing within the measurement year, and a patient tested in December of year one who is not retested until February of year two shows as a gap in both years. The third group is new patients in the onboarding period whose control has not yet responded to treatment initiation.

The 23% "control-unknown" finding — that nearly a quarter of patients classified as uncontrolled are actually missing a current test rather than confirmed uncontrolled — changes the intervention math significantly. A practice that pursues intensified pharmacological treatment for all patients in the "uncontrolled" category is treating test-timing gaps with medication changes, which generates unnecessary clinical risk and cost. Separating the uncontrolled-confirmed from the uncontrolled-untested sub-population before designing interventions is the single most important analytical step in A1C control rate improvement.

Implement a panel composition-adjusted control rate that reports separately for established patients (enrolled 12+ months) and new patients (enrolled less than 12 months). This allows comparison of the established panel control rate against prior periods — the metric that reflects actual clinical performance — while tracking new patient onboarding progress as a separate operational metric. Most quality reporting platforms do not produce this split automatically; it requires a custom panel tenure calculation applied to the existing A1C control denominator.

Build a 90-day retest reminder workflow that identifies diabetic patients with a last A1C test more than 85 days ago and generates a lab order reminder for the patient and provider simultaneously. This workflow addresses the testing gap sub-population without clinical intervention. For the medication compliance sub-population, implement a refill pattern flag that identifies patients who have not refilled a key diabetes medication (GLP-1, metformin, SGLT2 inhibitor, basal insulin) within the expected refill window — this catches formulary-driven non-adherence at the point of non-fill rather than at the next A1C test three months later.