Diabetes Care Analytics Software

Analytics in a Diabetes Care Practice

Diabetes care produces two kinds of numbers that leaders need side by side. One is clinical: how many patients have a recent A1c or GMI, how many sit above 9.0, and how many had kidney testing this year. The other is financial: what each service line earns, which payers deny training units, and how much CGM work goes unbilled. Analytics in Unlimited Systems draws both from the same billing and clinical data. A leader can see that the share of patients above 9.0 rose at one site, then open the list and find that many have no result this year at all. A billing manager can see that training denials rose with one payer, and trace them to referrals that lapsed. Trends run by month, site, clinician and payer. Peer benchmarks show where the practice stands on collections and denial rates. The point is to find the cause while it can still be fixed.

Why Is Analytics Different in Diabetes Care?

Glycemic numbers in diabetes care can move for reasons that have little to do with how patients are doing. Under MIPS measure 001, a patient with no A1c or GMI result in the year counts the same as a patient above 9.0. A rise in poor control may simply mean that tests were not done or not recorded. Analytics that splits missing results from high results shows whether the fix is clinical outreach or better data capture.

Device data adds a newer source of results. Both MIPS and HEDIS now accept a glucose management indicator from CGM data alongside a lab A1c. A practice with many patients on CGM may have more recent glycemic results than its lab feed shows. Diabetes care analytics has to count both, with the date each result applies to, or the panel will look worse than it is.

The financial side is spread across many small service lines. Training, nutrition therapy, CGM studies, monthly reads, in-office lab tests and visits each have their own payment rules and denial patterns. A total collections figure hides which of these is slipping. Leaders need to see each line, and to drill into the payer and the reason code when one moves. The diabetes reporting tools then deliver the same figures on a schedule.

What Diabetes Care Analytics Software Handles

Glycemic bands under MIPS 001

MIPS 001 sorts each patient's most recent A1c or GMI into bands: below 7.0, 7.0 to below 8.0, 8.0 to 9.0, above 9.0, or missing. The panel view shows each band, so a move between bands is easy to spot. (CMS QPP: 2026 MIPS CQM Quality ID #001, Diabetes: Glycemic Status Assessment Greater Than 9%)

Missing results count as poor control

Under MIPS 001, a patient with no A1c or GMI result in the period counts toward the poor-control rate. Analytics splits missing from high results, so outreach goes to patients who simply need a test. (CMS QPP: 2026 MIPS CQM Quality ID #001, Diabetes: Glycemic Status Assessment Greater Than 9%)

HEDIS glycemic status (GSD)

The HEDIS GSD measure reports patients 18 to 75 with a most recent A1c or GMI below 8.0 and above 9.0. Health plan HEDIS scores can be checked against the practice's own panel using the same bands. (NCQA: Glycemic Status Assessment for Patients With Diabetes (GSD))

GMI dates from CGM data

Health plan guidance says a GMI must be documented with the CGM date range it came from, and the last date in that range is the result date. Analytics uses that date, so a GMI counts in the right year. (Point32Health: HEDIS Tip Sheet, Glycemic Status Assessment for Patients with Diabetes (GSD))

Kidney health evaluation (KED)

The HEDIS KED measure looks for both an eGFR and a urine albumin-creatinine ratio in the year, for patients 18 to 85. Patients with one test but not the other are shown as a separate group. (NCQA: Kidney Health Evaluation for Patients With Diabetes (KED))

Education hours used

Medicare covers up to ten hours of initial training and two hours of follow-up each year after. Analytics shows how many eligible patients have unused hours, which points to referral or scheduling gaps. (Noridian JF Part B: Diabetic, Diabetes Self-Management Training (DSMT) and Medical Nutrition Therapy (MNT) (updated 2025-07-11))

How Does Diabetes Care Analytics Software Work?

1. 1. Bring clinical and billing data together

   Lab results, CGM-derived GMI values, charges and remittances are drawn into one data layer. Each result keeps its date and source.

2. 2. Set the measures and bands

   Glycemic bands, kidney and eye testing, education hours and service line revenue are defined once. Every view uses the same definitions.

3. 3. Watch trends by site and clinician

   Each measure runs by month, site, clinician and payer. A change at one site stands out against the others.

4. 4. Drill into the cause

   From any figure, users open the list of patients, claims or denial reason codes behind it. The cause is visible without a new report.

5. 5. Share what matters

   Leaders see the figures on diabetes dashboards, and the same numbers go out in scheduled reports. Everyone works from one version.

Diabetes Care Analytics Software vs. Generic Software

Who Uses It in a Diabetes Care Practice

• Medical director: Panel results by band and clinician, with missing results split from high ones, so outreach and care changes go to the right patients.
• Billing manager: Denials and collections by service line, payer and reason code, with drill-down to the claims behind each trend.
• Practice owner: A view of clinical and financial performance by site, with peer benchmarks on collections and denials to show where the practice stands.