The Accreditation Advantage

Methods

How NCQA Evaluated Accredited Versus Non-Accredited Plan Star Ratings Performance

› OVERVIEW

How Accreditation May Support Performance

NCQA Health Plan Accreditation includes operational requirements that may support stronger and more consistent quality performance. Accredited plans are required to maintain a structured quality improvement program, assess performance regularly, and identify and act on opportunities for improvement over time. NCQA also incorporates HEDIS® performance into Health Plan Accreditation scoring, reinforcing the connection between Accreditation and clinical quality improvement.

In addition, Accredited plans must demonstrate capabilities in areas such as population health management, access to care and performance monitoring, including processes related to network adequacy, appointment availability, complaints and appeals and member engagement. These requirements may support more consistent, repeatable execution of the measures that influence Centers for Medicare & Medicaid Services (CMS) Medicare Advantage (MA) Star Ratings performance.

› LIMITATIONS

This analysis reflects observed associations and does not imply causality. NCQA stewarded measures are a larger percentage of the overall measures and weight in future Star Ratings based on the most recent Final Rule 13. This may lead to findings that support further separation between Accredited plans and non-Accredited plans on Star Ratings.

Key limitations include:

  • Overall Star Ratings are influenced by multiple components beyond HEDIS measures.
  • Accreditation reflects multi-year performance and operational capabilities.
  • Differences in plan characteristics (e.g., size, geography, population) may influence results.
  • CAHPS measures show less consistent statistical significance.
  • Of 35 impact estimate inputs, there are caveated adopted rates and deliberately not quantified rates.
  • Impact estimate inputs for Care for Older Adults – Medication Review and Care for Older Adults – Pain Assessment could not be quantified defensibly through literature review. No distribution shift impact calculation was performed for these measures.
› SCOPE

This analysis evaluates the relationship between NCQA Health Plan Accreditation and performance in the CMS MA Star Ratings program. Accredited MA plans and non-Accredited MA plans are included in this analysis. NCQA evaluated 13 HEDIS measures, all of which are in the Star Ratings for MY 2024 (equivalent to CMS Stars 2026). The following measures were reviewed for the time period of MY 2022 to MY 2024:

  • Breast Cancer Screening (BSC-E)
  • Care for Older Adults – Medication Review (COA-MR)
  • Care for Older Adults – Pain Assessment (COA-PA)
  • Colorectal Cancer Screening (COL-E)
  • Controlling High Blood Pressure (CBP)
  • Diabetes Care – Blood Sugar Controlled (GSD)
  • Diabetes Care – Eye Exam (EED)
  • Follow-up after Emergency Department Visit for People with Multiple High-Risk Chronic Conditions (FMC)
  • Kidney Health Evaluation for Patients with Diabetes (KED)
  • Osteoporosis Management in Women who had a Fracture (OMW)
  • Plan All-Cause Readmissions (PCR)
  • Statin Therapy for Patients with Cardiovascular Disease (SPC)
  • Transitions of Care (TRC)

In addition, 7 Consumer Assessment of Healthcare Providers and Systems (CAHPS) measures, which are in Star Ratings for MY 2024, are included but Health Outcomes Survey (HOS) and administrative measures are excluded.

These measures were selected to reflect the clinical quality and patient experience components of the Star Ratings program and to focus the analysis on areas most directly associated with plan performance.

All performance comparisons are based on publicly reported CMS Star Ratings data and NCQA measure specifications. Impact estimate inputs are based on literature reviews and reflect a mix of sourced (verified primary, Medicare-65+) and caveated rates.

The analysis consists of two components. First, it examined Accredited plan performance compared to non-Accredited plan performance on individual measures and evaluated the statistical significance of observed differences. Second, the analysis calculated potential impact on care quality if non-Accredited plans were able to shift their performance distribution to match that of Accredited Medicare Advantage plans. This work estimates four impact metrics for each measure including rate per 100 eligible members, MA specific events averted, average MA unit costs per year and associated mortality differences.

› DATA SOURCES

This study used publicly reported Star Rating and performance rate data from CMS as well as Accreditation status and performance rate data from HEDIS stewarded by NCQA. These data were used to evaluate differences in quality performance by Health Plan Accreditation status over measurement years 2022 to 2024 within the MA product line. This was supplemented by findings from research studies related to measures in Star Ratings. All measure performance data reflects a general “higher is better” trend except for the Plan All-Cause Readmission measure.

HEDIS

HEDIS performance rates were obtained for MA plans at the measure and submission level while Accreditation data was obtained at the contract level. Standard HEDIS requirements for reportability were applied.

Transformation of some measure rate data to align with CMS measure specifications occurred on two occasions:

  • For the “Diabetes Care – Blood Sugar Controlled” measure, CMS defines higher performance as better, while HEDIS defines lower performance as better; the HEDIS rate was inverted to align with CMS.
  • For the “Transitions of Care” measure, CMS reflects the average of all four HEDIS TRC indicators; the average of the four HEDIS indicators was calculated for this analysis.

CMS Public Use Files (PUF)

CMS publicly reported Star Ratings performance and Star assignment data were obtained for Medicare Advantage at the measure level and are reported at the contract level1. CMS contract identifiers were matched to corresponding HEDIS CMS contract identification variables to assign Accreditation status for analytic purposes.

CAHPS

CAHPS performance data included in this report were derived exclusively from CMS publicly reported CAHPS rates. HEDIS CAHPS performance data were not used due to differences in case-mix adjustment methodologies, which limit comparability.

› ANALYTICAL APPROACH

Plan-Level Analysis

Performance was analyzed at the contract (plan) level and represents member or event level data.

Measurement Year Alignment

  • Results are based on MY 2024 unless otherwise noted.
  • MY 2024 reflects care delivered in the prior year.
  • Multi-year trends (MY 2022–2024) were used to assess consistency over time.

Star Ratings Framework

Star Ratings positioning was assessed using:

  • Measure-level performance.
  • Alignment with CMS thresholds (cut points).
  • Plan-level aggregation and standard distribution where applicable.

Overall Star Ratings are derived from multiple components, the analysis focuses on individual underlying measure performance and positioning relative to CMS thresholds.

CMS establishes performance thresholds (“cut points”) that determine whether a plan achieves a 1-5 Star Rating for each measure. Cut points reflect the minimum score a plan must achieve to earn the next higher Star Rating for a given measure. Even minor differences in performance can determine whether a plan crosses a threshold, which is why performance near cut points is especially important.

This analysis evaluates:

  • Plan performance relative to these thresholds.
  • The proportion of plans above key thresholds (e.g., 4-Star level).
  • The impact of performance distribution-shift near thresholds.

Evaluating performance relative to cut points provides insight into how clinical quality translates into Star Ratings outcomes, particularly for plans operating near key thresholds.

Analysis Method 1: Accredited Plan Performance Versus Non-Accredited Plan Performance

A linear regression analysis was conducted at the measure level to examine the effect of Accreditation status, measurement year, and their interaction on performance rates.

  • The overall model test (F-test) assessed whether Accreditation status, year and their interaction jointly explained variation in rates beyond the null model.
  • Regression coefficient p-values were used to evaluate statistical significance of individual model parameters relative to the specified reference categories.
  • Year-specific differences between Accreditation groups were evaluated using Bonferroni-adjusted post-hoc pairwise comparisons within year. All analyses were conducted in R using the lm() function with statistical significance evaluated at α=0.05.

Analyses were conducted in R using the lm() function, with statistical significance evaluated at α = 0.05.

Analysis Method 2: Impact and Net Present Value Calculations

This analysis looks at the distribution shift impact if non-Accredited plans were able to shift their quality performance to match the Accredited plans distribution for a given HEDIS measure that is used in Star Ratings.

For this analysis each input is either sourced (verified primary, Medicare-65+) or a caveated rate. The caveats provides information on how to use the research. Of 35 tracked inputs to the analysis, 20 are sourced, 13 are caveated adopted rates and 2 are deliberately not quantified.

Summary of Inputs – Sourced, Caveated and Not Quantified

This analysis uses distribution matching where each non-Accredited plan is matched to the Accredited performance distribution at its rank. This looks like:

Distribution matching aligns percentiles across groups Each non-Accredited plan is matched to the Accredited plan at the same percentile rank rather than applying one average shift. Orange arrows connect matching percentiles from the non-Accredited distribution up to the Accredited distribution. A dashed line marks an example target or four-star cut point. Distribution matching aligns percentiles across groups Same percentile rank is matched across distributions, rather than applying one average shift. Example target / 4-Star cut point Accredited plans Non-accredited plans 10th 25th 50th 75th 90th 10th 25th 50th 75th 90th 40 50 60 70 80 90 100 Performance Score Same percentile rank matched across distributions Example target threshold

Once the distribution is matched the number of care gaps closed are calculated by measure and deduplicated and then the impact of the literature is implemented. This estimates and describes the potential for additional people and costs impacted if non-Accredited plans matched the Accredited distribution. In addition, this analysis calculates the data for:

  • Additional people impacted.
  • Percentage of plans at or above 4 Stars.
  • Rate per 100 eligible members.
  • Medicare specific events averted using average Medicare unit costs.
  • Annual costs deduplicated.
  • Associated mortality.

All costs are initially calculated annually and these avoided costs would be possible to save with members each year as it does not recount the same person’s health event. The 13 measures are then added with one rule: where two measures would count the same event (e.g., statins and blood pressure control both prevent cardiovascular events; transitions of care and ED follow-up both prevent readmissions which are already counted under all-cause readmissions) it is counted once. Diabetes complications that also appear under kidney and cardiovascular measures are likewise removed once. 

The annual avoided-spend run-rate is constant in real 2026 dollars. When looking at a cumulative avoided spend over 5 or 10 years, the dollars are adjusted with a net present value discount rate of 3%. This is avoided medical spend if performance is sustained, it is not a sustained CMS Star Ratings bonus. (The bonus lift can be transient; the clinical/cost benefit recurs only while performance is maintained).

Summary of Calculations and Results by Measure

Total — 13 measures Additional reaching target: 1,558,800 Lives Impacted / Year: 1,995–4,611 (3 measures w/ multipliers)
› APPENDIX A – DATA TABLES

HEDIS Measures Included in the Analysis

The table below summarizes the HEDIS measures included in this analysis.

Measure TypeMeasure NameAccredited Plan AverageNon-Accredited Plan AverageDifference
HEDISCare For Older Adults - Medication Review96.70%93.51%3.19%
HEDISCare For Older Adults - Pain Assessment96.30%93.05%3.25%
HEDISGlycemic Assessment87.51%81.82%5.69%
HEDISStatin Therapy87.33%86.10%1.23%
HEDISControlling Blood Pressure80.55%77.12%3.43%
HEDISEye Exam80.38%74.52%5.86%
HEDISBreast Cancer Screening76.73%71.70%5.04%
HEDISColorectal Screening73.95%68.33%5.63%
HEDISTransitions of Care66.88%61.86%5.01%
HEDISKidney Health Evaluation64.45%58.57%5.88%
HEDISFollow-Up after ED Visit61.96%60.18%1.78%
HEDISOsteoporosis Management48.79%41.70%7.09%
HEDISPlan All Cause Readmissions9.91%10.23%-0.32%
HEDISCare For Older Adults - Medication Review95.94%91.90%4.04%
HEDISCare For Older Adults - Pain Assessment95.75%91.73%4.02%
HEDISGlycemic Assessment86.39%80.07%6.32%
HEDISStatin Therapy86.90%85.41%1.49%
HEDISControlling Blood Pressure77.67%74.62%3.05%
HEDISEye Exam76.94%72.00%4.94%
HEDISBreast Cancer Screening76.22%70.26%5.96%
HEDISColorectal Screening69.89%63.72%6.17%
HEDISTransitions of Care63.84%57.27%6.58%
HEDISKidney Health Evaluation58.50%50.12%8.38%
HEDISFollow-Up after ED Visit58.83%56.19%2.64%
HEDISOsteoporosis Management46.81%39.56%7.25%
HEDISPlan All Cause Readmissions11.01%11.37%-0.36%
HEDISCare For Older Adults - Medication Review94.73%90.28%4.45%
HEDISCare For Older Adults - Pain Assessment93.58%90.14%3.44%
HEDISGlycemic Assessment84.07%76.66%7.41%
HEDISStatin Therapy85.90%84.81%1.10%
HEDISControlling Blood Pressure75.97%71.22%4.75%
HEDISEye Exam76.13%69.78%6.35%
HEDISBreast Cancer Screening75.61%70.74%4.87%
HEDISColorectal Screening69.14%62.47%6.67%
HEDISTransitions of Care57.08%49.81%7.28%
HEDISKidney Health Evaluation53.47%44.35%9.12%
HEDISFollow-Up after ED Visit58.09%54.75%3.34%
HEDISOsteoporosis Management47.52%40.99%6.54%
HEDISPlan All Cause Readmissions10.94%11.25%-0.31%

Detailed Results By Measure: CAHPS

The table below summarizes the results for the case mix adjusted CAHPS measures analyzed for this report. The average score for each measure was calculated based on data from MY 2022 to 2024.

Measure TypeMeasure NameAccredited Plan AverageNon-Accredited Plan AverageDifference
CAHPSFlu Vaccine69.23%65.75%3.48%
CAHPSGetting Care81.49%81.25%0.24%
CAHPSGetting Appointments83.88%83.83%0.05%
CAHPSCustomer Service90.53%90.23%0.30%
CAHPSHealth Care Quality87.18%86.63%0.55%
CAHPSHealth Plan Rating87.54%86.72%0.82%
CAHPSCare Coordination87.24%87.28%-0.04%
CAHPSFlu Vaccine71.46%68.67%2.79%
CAHPSGetting Care80.63%80.60%2.93%
CAHPSGetting Appointments83.39%83.56%-17.19%
CAHPSCustomer Service90.65%90.25%39.67%
CAHPSHealth Care Quality86.90%86.53%37.45%
CAHPSHealth Plan Rating87.75%86.86%88.55%
CAHPSCare Coordination86.75%86.56%18.65%
CAHPSFlu Vaccine73.94%71.00%2.95%
CAHPSGetting Care80.53%80.11%41.48%
CAHPSGetting Appointments77.66%77.17%48.44%
CAHPSCustomer Service90.55%90.12%42.86%
CAHPSHealth Care Quality86.47%85.92%54.57%
CAHPSHealth Plan Rating87.38%86.60%77.86%
CAHPSCare Coordination85.94%85.63%31.58%
› APPENDIX B – REFERENCES

Data Source References

Centers for Medicare & Medicaid Services. Part C and Part D Performance Data. https://www.cms.gov/medicare/health-drug-plans/part-c-d-performance-data

Impact Estimate Input References

The published evidence behind every clinical-yield and cost coefficient, by measure. Each was independently checked for existence and for supporting the coefficient it is cited for.

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R3  Bardach, N. S., Doupnik, S. K., Rodean, J., Zima, B. T., Gay, J. C., Nash, C., Tanguturi, Y., & Coker, T. R. (2020). ED visits and readmissions after follow-up for mental health hospitalization. Pediatrics, 145(6), e20192872. https://doi.org/10.1542/peds.2019-2872 PMID 32404433

R4  Bergen, G., Stevens, M. R., & Burns, E. R. (2016). Falls and fall injuries among adults aged ≥65 years – United States, 2014. MMWR. Morbidity and Mortality Weekly Report, 65(37), 993–998. https://doi.org/10.15585/mmwr.mm6537a2 PMID 27656914

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R7  Blumen, H., Fitch, K., & Polkus, V. (2016). Comparison of treatment costs for breast cancer, by tumor stage and type of service. American Health & Drug Benefits, 9(1), 23–32. PMID 27066193

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R15  Centers for Medicare & Medicaid Services. (2024). Medicare Advantage Part C and Part D star ratings and measure cut points, MY2024 [Data file].

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R17  Chongvoranond, P., Thewjitcharoen, Y., Chatchomchaun, W., Wanothayaroj, E., Butadej, S., Nakasatien, S., Krittiyawong, S., & Himathongkam, T. (2025). Sodium-glucose cotransporter-2 inhibitor (SGLT2i) prescription rates amongst diabetologists for type 2 diabetes patients with albuminuric diabetic kidney disease: A real-world study at a diabetes center in Bangkok. Journal of the ASEAN Federation of Endocrine Societies, 40(2), 69–77. https://doi.org/10.15605/jafes.040.02.22 PMID 41357057

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R20  Donzé, J., John, G., Genné, D., Mancinetti, M., Gouveia, A., Méan, M., Bütikofer, L., Aujesky, D., & Schnipper, J. (2023). Effects of a multimodal transitional care intervention in patients at high risk of readmission: The TARGET-READ randomized clinical trial. JAMA Internal Medicine, 183(7), 658–668. https://doi.org/10.1001/jamainternmed.2023.0791 PMID 37126338

R21  Ettehad, D., Emdin, C. A., Kiran, A., Anderson, S. G., Callender, T., Emberson, J., Chalmers, J., Rodgers, A., & Rahimi, K. (2016). Blood pressure lowering for prevention of cardiovascular disease and death: A systematic review and meta-analysis. The Lancet, 387(10022), 957–967. https://doi.org/10.1016/S0140-6736(15)01225-8 PMID 26724178

R22  Feltner, C., Jones, C. D., Cené, C. W., Zheng, Z. J., Sueta, C. A., Coker-Schwimmer, E. J., Arvanitis, M., Lohr, K. N., Middleton, J. C., & Jonas, D. E. (2014). Transitional care interventions to prevent readmissions for persons with heart failure: A systematic review and meta-analysis. Annals of Internal Medicine, 160(11), 774–784. https://doi.org/10.7326/M14-0083 PMID 24862840

R23  Field, T. S., Gilman, B. H., Subramanian, S., Fuller, J. C., Bates, D. W., & Gurwitz, J. H. (2005). The costs associated with adverse drug events among older adults in the ambulatory setting. Medical Care, 43(12), 1171–1176. https://doi.org/10.1097/01.mlr.0000185690.10336.70 PMID 16299427

R24  Florence, C. S., Bergen, G., Atherly, A., Burns, E., Stevens, J., & Drake, C. (2018). Medical costs of fatal and nonfatal falls in older adults. Journal of the American Geriatrics Society, 66(4), 693–698. https://doi.org/10.1111/jgs.15304 PMID 29512120

R25  Gonçalves-Bradley, D. C., Lannin, N. A., Clemson, L. M., Cameron, I. D., & Shepperd, S. (2016). Discharge planning from hospital. Cochrane Database of Systematic Reviews, 2016(1), CD000313. https://doi.org/10.1002/14651858.CD000313.pub5 PMID 26816297

R26  Green, R. K., Nieser, K. J., Jacobsohn, G. C., Cochran, A. L., Caprio, T. V., Cushman, J. T., Kind, A. J. H., Lohmeier, M., & Shah, M. N. (2023). Differential effects of an emergency department-to-home care transitions intervention in an older adult population: A latent class analysis. Medical Care, 61(6), 400–408. https://doi.org/10.1097/MLR.0000000000001848 PMID 37167559

R27  Gurwitz, J. H., Field, T. S., Harrold, L. R., Rothschild, J., Debellis, K., Seger, A. C., Cadoret, C., Fish, L. S., Garber, L., Kelleher, M., & Bates, D. W. (2003). Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA, 289(9), 1107–1116. https://doi.org/10.1001/jama.289.9.1107 PMID 12622580

R28  Heart Protection Study Collaborative Group. (2002). MRC/BHF heart protection study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: A randomised placebo-controlled trial. The Lancet, 360(9326), 7–22. https://doi.org/10.1016/S0140-6736(02)09327-3 PMID 12114036

R29  Heerspink, H. J. L., Stefánsson, B. V., Correa-Rotter, R., Chertow, G. M., Greene, T., Hou, F. F., Mann, J. F. E., McMurray, J. J. V., Lindberg, M., Rossing, P., Sjöström, C. D., Toto, R. D., Langkilde, A. M., Wheeler, D. C., & DAPA-CKD Trial Committees and Investigators. (2020). Dapagliflozin in patients with chronic kidney disease. The New England Journal of Medicine, 383(15), 1436–1446. https://doi.org/10.1056/NEJMoa2024816 PMID 32970396

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R34  Javitt, J. C., Aiello, L. P., Chiang, Y., Ferris, F. L., 3rd., Canner, J. K., & Greenfield, S. (1994). Preventive eye care in people with diabetes is cost-saving to the federal government. Implications for health-care reform. Diabetes Care, 17(8), 909–917. https://doi.org/10.2337/diacare.17.8.909 PMID 7956643

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