Quality Dashboard That Disagrees With the Payor
A primary care network's internal dashboard reports 81% diabetes HbA1c control (<8%) for its 12,000 attributed patients. The largest payor's value-based contract reports 68% for the same population and threatens a payment withhold. The internal dashboard uses the most recent HbA1c in the prior 12 months and excludes patients with no lab. The payor specification counts patients with no HbA1c in the measurement year as not controlled and uses a 24-month lookback. The network also has 1,900 patients with diabetes who have no lab in the internal window. Leadership wants the dashboard changed to match the payor so the gap disappears.
1. Diagnose the discrepancy and explain why simply changing the dashboard to match the payor is the wrong fix.
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The gap is largely a specification difference, not a care difference. Internal: 12-month window, excludes no-lab patients (denominator inflation of control). Payor: 24-month lookback and counts no-lab as not controlled. The 1,900 patients without a recent lab are the crux - they are either truly uncontrolled or simply unmeasured, and the payor treats them as failures. Changing the dashboard to the payor spec would make the numbers agree but would hide the real clinical problem: a large cohort of patients with no recent testing. The right response is to reconcile specifications (denominator, lookback, attribution, exclusions) and then act on the unmeasured cohort. Report both a payor-aligned measure and a clinical measure, and label them clearly.
2. What analytic steps reconcile the two numbers, and what clinical action follows?
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Rebuild the measure under the payor specification and compare to the internal one to quantify how much of the 13-point gap is denominator, lookback, and no-lab handling. Then stratify the 1,900 no-lab patients: overdue for routine testing, recently transferred, deceased, or truly disengaged. Prioritize outreach to the engaged-but-overdue group. Track a care-gap closure rate as the operational metric. This converts a reporting dispute into a population-health worklist. Validate against a sample of charts to confirm attribution and lab matching are correct.
3. How do you communicate this to leadership and the payor?
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Present a reconciliation table showing each specification difference and its numeric effect, so leadership sees the gap is partly measurement and partly real. Propose a joint data-validation meeting with the payor to confirm attribution and lab feeds, and request correction if the payor's data is missing labs the network submitted. Commit to a measurable outreach plan for the unmeasured cohort with a review date. Avoid framing it as 'the payor is wrong'; frame it as 'here is what is measurable, here is what is missing, here is the plan.'