Overview
Measure Overview
The goal of this measure is to improve patient outcomes by providing patients, physicians, hospitals, and policymakers with information about hospital-level, risk-standardized mortality rates following hospitalization for AMI. Measurement of patient outcomes allows for a broad view of quality of care that encompasses more than what can be captured by individual process-of-care measures. Complex and critical aspects of care, such as communication between providers, prevention of and response to complications, patient safety, and coordinated transitions to the outpatient environment, all contribute to patient outcomes, but are difficult to measure by individual process measures. The goal of outcomes measurement is to risk adjust for patient conditions at the time of hospital admission then evaluate patient outcomes. This measure was developed to identify institutions whose performance is better or worse than what would be expected based on their patient case mix and therefore promote hospital quality improvement and better inform consumers about care quality.
Measurement of patient outcomes related to acute myocardial infarction (AMI) risk-standardized mortality rates permits an overall view of care provided by individual hospitals as compared to like facilities with similar patient populations. This process can assist patients and caregivers in evaluating outcomes for specific providers in relation to care and services for AMI. This opportunity provides patients with the opportunity to choose a provider based on their needs and provides hospitals with quality improvement opportunities.
Measure currently used in a Medicare program, but the measure is undergoing substantive change.
Initial endorsement in 2007; endorsed during maintenance review in 2020.
Integration of Medicare Advantage (MA) beneficiaries into the cohort and modifying performance period from 3 years to 2 years.
Measure Specification
The outcome for this measure is 30-day all-cause mortality. We define mortality as death from any cause within 30 days of the start of the index admission.
N/A
Not applicable
The cohort includes admissions for patients that meet all of the following inclusion criteria:
- Discharged from the hospital with a principal discharge diagnosis of AMI;
- Enrolled in Medicare FFS Part A and Part B or MA for the first 12 months prior to the date of admission and enrolled in Part A or MA during the index admission. [For VA beneficiaries hospitalized in VA hospitals, there are no Medicare FFS or MA enrollment requirements. For VA beneficiaries hospitalized in non-VA hospitals, they must be concurrently enrolled in Medicare FFS Part A or MA at the time of the index admission, to be eligible for cohort inclusion, but the 12-month Part A and B or MA enrollment prior to admission is not required.];
- Aged 65 or over;
- Not transferred from another acute care facility.
N/A
This measure excludes index admissions for patients that meet any of the following exclusion criteria:
Discharged alive on the day of admission or the following day and not transferred to another acute care facility;
Inconsistent or unknown vital status or other unreliable demographic data (e.g., age and sex);
Enrolled in the Medicare hospice program (or used VA hospice services) any time in the 12 months prior to the index admission, including the first day of the index admission; or
Discharged against medical advice.
For patients with more than one eligible AMI admission in the reporting period, only one index admission per year is randomly selected for inclusion in the cohort. Additional admissions within that time period are excluded.
Meaningfulness
Importance
As referenced in the empirical data summary, mortality rates after an AMI hospital admission are high throughout hospitals in the United States and create significant economic burden on the health care system. Evidence from peer-reviewed studies shows that while risk-standardized mortality rates for AMI have decreased over the last 20 years, variation remains, suggesting continued opportunities for improvement. This measure clearly aligns with national health priorities and demonstrates potential for improving outcomes, such as use of appropriate medications, timely percutaneous coronary interventions, and prevention of complications to decrease risk of mortality within 30 days of hospital admission.
The evidence supporting the importance of this measure was found to be sufficient during CBE endorsement review in 2020.
Conformance
This measure intends to estimate the risk-standardized mortality rate (RSMR) of adults discharged from the hospital with an AMI diagnosis within 30 days of discharge. The measure numerator, denominator, and exclusions are clearly defined and directly support the intent of the measure. Specifically, the numerator is 30-day all-cause mortality. The denominator includes patients discharged from the hospital with a principle diagnosis of AMI, are enrolled in Medicare FFS Part A and Part B for the first 12 months prior to the date of hospital admission and are enrolled in Medicare FFS or MA during the index admission, are 65 or older, and were not transferred from another acute care facility. The denominator has been updated to include MA patients, which doubles the cohort size and more accurately reflects the quality of care for FFS and MA beneficiaries.
This measure aligns with the Hospital IQR Program objectives to improve the quality of care that hospitals provide and to distribute clearly defined and objective data about hospital performance, and the HVBP program objective to eliminate or reduce adverse events.
Feasibility
No, not an eCQM
Data elements for this measure are routinely collected in electronic health records and claims data. The measure can be implemented without significant workflow changes. No major technical barriers are associated with this measure; however, rural hospitals or hospitals without EHR access may experience burden when reporting on this measure.
The feasibility of this measure was found sufficiently demonstrated during CBE endorsement review in 2020.
Validity
Empiric Validity
Facility
Yes
The developer assessed the validity of the measure by comparing the correlations between the AMI Mortality measure and components of the Overall Hospital Quality Star Rating, including the Summary Score (with and without the Mortality Group) and the Mortality Group Score (with and without the AMI Mortality Measure[1]). Because the AMI Mortality measure follows a lower-is-better scale while Star Rating measures follow a higher-is-better scale, the developer hypothesized a weak-to-moderate negative correlation between the AMI Mortality measure and Star Rating-related measures.
The AMI Mortality measure showed negative correlations with the Star Rating Overall Summary Scores (r=-0.304, p < .0001), the Star Ratings Adjusted summary score excluding the Mortality Group (r=-0.128, p < .0001), the Star Rating Mortality Group Scores (r=-0.451, p < .0001), and the Star Rating Adjusted Mortality Group Scores excluding the AMI Mortality Measure (r=-0.376, p < .0001). These findings are consistent with expectations, reinforcing the relationship between lower AMI Mortality scores and higher Star Ratings as indicators of better quality of care.
The developer does not recommend the measure for stratification.
Testing in populations representative of the CMS program population supports the external validity of the measure.
The validity of this measure was found sufficiently demonstrated during CBE endorsement review in 2020.
[1] All Mortality Measures used in Star Ratings are FFS only.
The measure is risk adjusted for patient functional status (frailty indicator), patient-level demographics (age), and patient-level health status and clinical conditions (case-mix adjustment, comorbidities, and severity of illness). The developer performed model discrimination using the c-statistic and predictive ability and assessed model calibration using the risk decile plot. During measure development, the sample of 228,680 persons produced a c-statistic of 0.86, with predictive ability ranging from 1.00% in the lowest decile to 61.96% in the highest decile. The validation sample of 227,985 persons produced the same c-statistic of 0.86, with predictive ability ranging from 1.07% to 62.82%. The risk decile plot showed that deciles with a higher predicted risk of AMI mortality were associated with higher observed AMI mortality rates. These results suggest that the model effectively differentiates AMI mortality risk levels and adequately adjusts for differences in patient characteristics.
Reliability
Random Split-half Correlation [MUC Entry/Review Information Tool (MERIT) Submission Form]
Facility
The developer conducted permutation resampling to calculate the intraclass correlation coefficient (ICC) at the accountable-entity level. The overall ICC for hospitals with at least 25 admissions was 0.772. Reliability results used 2 years of data (2022-2023) from 2,127 facilities with at least 25 admissions. During collaboration on this PA, the developer provided the minimum, maximum, median, and 25th and 75th percentiles. Among hospitals with at least 25 admissions, the minimum reliability was 0.739, the median reliability was 0.771 (IQR: 0.765-0.779), and the maximum reliability was 0.788. All facilities exceeded the recommended minimum reliability threshold of 0.6.
During CBE endorsement review in 2020, the committee found the reliability of this measure to be sufficiently demonstrated.
No additional analyses were performed.
Usability
Yes, the submission materials briefly discuss the measure’s usability within relevant programs.
The measure is currently used in the HVBP program and has demonstrated actionable insights for providers in hospitals. The developer did not identify unintended consequences but will continue to monitor the measure’s use and assess potential unintended consequences over time, such as the inappropriate shifting of care, increased patient morbidity and mortality, and other negative unintended consequences for patients.
During CBE endorsement review in 2020, the committee found the use/usability of this measure to be sufficiently demonstrated.
Appropriateness of Scale
Overview
Hybrid Hospital-Wide All-Cause Risk Standardized Mortality Measure (HWM) is a related measure in the HVBP program.
Hospital 30-Day, All-Cause, Risk-Standardized Readmission Rate (RSRR) Following Acute Myocardial Infarction (AMI) Hospitalization is a related measure in the Hospital Readmissions Reduction Program.
This measure is an updated version of the Hospital 30-Day, All-Cause, Risk-Standardized Mortality Rate (RSMR) Following Acute Myocardial Infarction (AMI) Hospitalization in HVBP program. MA patients were added to the cohort, which increases the cohort size, improves measure reliability, and allows measure results to more accurately reflect the quality of care for FFS and MA beneficiaries. The developer’s analysis does not indicate the potential for differential benefit or harm to specific subgroups of participating entities or their patient populations and included a robust risk adjustment model that may improve distribution of benefit through reducing the influence of factors outside of the facility’s control.
Considerations for the committee: Based on clinical and professional experience, the committee should consider the potential burden and value of the measure for small or rural hospitals as well as hospitals serving high-risk or underserved populations.
Time to Value Realization
Overview
None specified
Incorporating MA data will extend the measure to a broader group of Medicare beneficiaries, thereby improving representativeness and reducing potential selection bias. In the near term, some hospitals may experience modest changes in scores as MA beneficiaries are incorporated. Over the longer term, a single, comprehensive Medicare cohort will provide more consistent tracking of performance trends, enhance comparability across hospitals and regions, and better reflect true differences in hospital quality.
Considerations for the committee:
- What are the potential near- and long-term impacts of this measure on measured entities as well as the Hospital IQR and HVBP programs and patient populations?
- Will benefits and burdens associated with this measure be realized within an appropriate implementation time frame?
- How will this measure mature through revisions in the future if added to the Hospital IQR and HVBP programs measure sets?
Public Comments
MUC2025-036
The Kansas Hospital Associaton has concerns regarding the substantive changes identified in this measure.
The inclusion of Medicare Advantage (MA) beneficiaries raises concern, as hospitals have limited ability to account for or influence MA plan utilization management policies, including prior authorization requirements, network limitations, plan directed discharges and post-acute care restrictions. These plan-level factors may affect utilization outcomes in ways that are unrelated to hospital performance and could introduce variability that is outside the hospital’s control.
We recommend reconsideration of the inclusion of MA beneficiaries in the cohort utilization calculations to ensure the measure more accurately reflects hospital performance.
AMI Mortality
Agree with addition population expansion to include Medicare Advantage and reducing time for data inclusion from 3 to 2 years.
RSMR Following Acute Myocardial Infarction (AMI) Hospitalization
The American Medical Association (AMA) believes that additional analyses are needed before revised measures are implemented. Like our concerns raised on the readmission and excess days in acute care measures, it is not clear to what degree is the reported association of lower readmissions with higher mortality found over longer or shorter time periods such as, one year or one week, as compared to the first 30-days post discharge. Gupta and co-authors report that the inverse association was still evident at one year.2 To what degree are any positive or negative correlations related to all-cause mortality and/or readmissions versus the condition-specific outcome? It is also worth examining whether trends exist based on unadjusted data and adjusted data. Most of the studies identified through our search of the literature, including Dharmarajan, et al.[1], used risk-adjusted data. Most individual patient care decisions are not made with risk-adjustment in mind. To better understand the outliers (those who died), there is a need to investigate and determine whether there is small, but important associations between patient mortality and readmissions rates. Therefore, are we masking the issue by only examining the adjusted rates? Examination of unadjusted and risk-adjusted rates could help address this concern. We also believe that the timeframe of the mortality measures and whether the post discharge period is appropriate must be reexamined.
In addition, this measure includes some of the same changes that CMS has made to the readmission measures in Hospital Readmissions Reduction Program (HRRP); specifically, the risk adjustment model was updated to use individual ICD-10 codes rather than the CMS Hierarchical Condition Category (HCC) model; the measure now includes Medicare Advantage (MA) beneficiaries; and the data collection timeframe is two years. How each of these changes impact the reliability and validity of the measure has not been provided in this submission and we are concerned that without a phased approach, it will be extremely difficult for hospitals to determine their impact (e.g., what is the effect of the expansion to MA beneficiaries as compared to the reduction in the number of years of data used to calculate the measure). We also recommend CMS provide data on how hospitals’ performance shift since the potential impact on each hospital is critical to ensure that the results can be used to drive further improvement in patient care.
Lastly, we also question the lack of socio-economic factors in the risk adjustment due to evidence that hospitals with larger populations of poor patients perform poorly on the measures. We recognize that some of the measures have been tested to consider economic related variables; however, we do not believe the appropriate risk models were tested. The traditional approach of risk adjusting at the patient level may not be appropriate for measures where the measurement period includes care that is outside of the control of the hospital and a 30-day post-acute phase where the availability of community supports, and other resources directly impact a patient’s care. We believe that there may be community-level variables that affect the risk of mortality during the days following hospital admission but are not currently addressed. Measures that extend beyond the hospital stay or outside the locus of control of the measured entity should continue to have socio-economic adjustments addressed and analyzed at different levels (e.g., patient, hospital, and community).
Due to these concerns and unanswered questions, the AMA does not support inclusion of this measure in the Hospital Inpatient Quality Reporting Program or the Hospital Value-Based Purchasing Program.
[1] Dharmarajan, Wang, Lin, et al. Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge. JAMA. 2017;318:270-278.
RSMR AMI
As the agency did in last year’s PRMR process, CMS included this updated measure on the MUC list because it intends to include Medicare Advantage beneficiaries in the denominator. We appreciate the general effects this change would have to improve the accuracy and timeliness of this measure, namely doubling the patient cohort and allowing CMS to shorten the performance period. However, it is unclear from the supporting documentation whether any analysis has been done to determine the implications of including these beneficiaries for the outcomes of the HVBP program, and testing has only been conducted on the existing (rather than updated) measure using one year of data from 2024. While MA beneficiaries are increasing as a proportion of total Medicare enrollees nationally, MA market penetration and specifics of enrollee makeup still varies widely. Because HVBP payment adjustments are calculated based on the distribution of all participating hospitals’ performance on measures within the program, the inclusion of MA beneficiaries in this calculation could mean significant changes in the distribution of scores, and as a result, in the payments hospitals experience under the HVBP program. We hope that the measure developer can provide additional insight into how this significant change to the measure’s specifications would impact the HVBP program and the hospitals that participate in it.
MUC2025-036
On behalf of the American Heart Association, including the American Stroke Association (Association) and 35 million volunteers and supporters, we appreciate the opportunity to submit comments on MUC2025-036. The Association supports the updated specifications expanding measurement to MA beneficiaries to the cohort. The Association also supports the removal of transfer patients.
Excess Days in Acute Care (EDAC) after Hospitalization
The American Medical Association (AMA) is concerned of the potential unintended negative consequences to patients that may result from measures that include readmissions as they may not capture the appropriate patient population due to their structure and timeframe.[1],[2] For example, the literature is beginning to show that the 30-day readmission measures based on administrative claims in the Hospital Readmissions Reduction Program (HRRP) may lead to increased mortality.2
The AMA believes that additional analyses are needed before revised measures are implemented. For example, it remains unclear to what degree is the reported association of lower readmissions with higher mortality found over longer or shorter time periods such as, one year or one week, as compared to the first 30-days post discharge. Gupta and co-authors report that the inverse association was still evident at one year.2 To what degree are any positive or negative correlations related to all-cause mortality and/or readmissions versus the condition-specific outcome? It is also worth examining whether trends exist based on unadjusted data and adjusted data. Most of the studies identified through our search of the literature, including Dharmarajan, et al.[3], used risk-adjusted data. Most individual patient care decisions are not made with risk-adjustment in mind. To better understand the outliers (those who are readmitted), there is a need to investigate and determine whether there is small, but important associations between reduced readmissions rates with patient mortality. Therefore, are we masking the issue by only examining the adjusted rates? Examination of unadjusted and risk-adjusted rates could help address this concern. We also believe that the timeframe of the readmission measures and whether the post discharge period is appropriate must be reexamined.
In addition, this measure includes some of the same changes that CMS has made to the readmission measures in HRRP; specifically, the risk adjustment model was updated to use individual ICD-10 codes rather than the CMS Hierarchical Condition Category (HCC) model; the measure now includes Medicare Advantage (MA) beneficiaries; and the data collection timeframe is two years. How each of these changes impact the reliability and validity of the measure has not been provided in this submission and we are concerned that without a phased approach, it will be extremely difficult for hospitals to determine their impact (e.g., what is the effect of the expansion to MA beneficiaries as compared to the reduction in the number of years of data used to calculate the measure). We also recommend CMS provide data on how hospitals’ performance shift since the potential impact on each hospital is critical to ensure that the results can be used to drive further improvement in patient care.
Lastly, we also question the lack of socio-economic factors in the risk adjustment due to evidence that hospitals with larger populations of poor patients perform poorly on the measures. We recognize that some of the measures have been tested to consider economic related variables; however, we do not believe the appropriate risk models were tested. The traditional approach of risk adjusting at the patient level may not be appropriate for measures where the measurement period includes care that is outside of the control of the hospital and a 30-day post-acute phase where the availability of community supports, and other resources directly impact a patient’s care. We believe that there may be community-level variables that affect the risk of readmission during the days following hospital admission but are not currently addressed. Measures that extend beyond the hospital stay or outside the locus of control of the measured entity should continue to have socio-economic adjustments addressed and analyzed at different levels (e.g., patient, hospital, and community).
Due to these concerns and unanswered questions, the AMA does not support inclusion of this measure in the Hospital Inpatient Quality Reporting Program.
[1]Graham, Kelly. Et al (2018). Preventability of Early Versus Late Hospital Readmissions in a National Cohort of General Medicine Patients. Ann Intern Med. Doi. 10.7326/M17-1724.
[2]Gupta, Ankar, et al. Association of the Hospital Readmissions Reduction Program Implementation With Readmission and Mortality Outcomes in Heart Failure. JAMA Cardiol. 2017.
[3] Dharmarajan, Wang, Lin, et al. Association of Changing Hospital Readmission Rates With Mortality Rates After Hospital Discharge. JAMA. 2017;318:270-278.
MUC2025-036 RSMR for AMI
CMS should transparently assess how differences in MA versus FFS coding practices, care access, and case mix affect measured mortality and ensure that the inclusion of MA beneficiaries does not introduce systematic bias unrelated to true quality of care. Publicly available analyses comparing MA and FFS populations, along with sensitivity testing, would help stakeholders understand potential distributional effects. As part of this work, CMS should clearly document how updated risk-adjustment variables account for AMI severity and procedural complexity (for example, STEMI vs. NSTEMI and PCI vs. non-PCI cases), and evaluate whether differences in post-acute access, medication adherence, and outpatient follow-up influence mortality in ways that may not reflect inpatient quality alone.
Shortening the performance period may improve timeliness, but it also increases year-to-year variability, particularly for lower-volume hospitals and procedures. CMS should monitor reliability impacts and consider safeguards or minimum case thresholds to preserve measure stability.
Comments on MUC2025-036
Vizient appreciates efforts to update the MUC2025-036: Hospital 30-Day, All-Cause, Risk-Standardized Mortality Rate (RSMR) Following Acute Myocardial Infarction (AMI) Hospitalization measure by adding Medicare Advantage (MA) population data as more than half of Medicare patients are covered through MA plans. While Vizient recognizes the importance of updating quality measures to reflect a broader range of Medicare beneficiaries, we continue to believe that additional steps are needed to ensure that MA data can be used reliably alongside fee-for-service (FFS) data. For example, as noted in Vizient’s FY 2026 IPPS Proposed Rule comments, there may be differences in how FFS claims and MA encounter data are recorded, yet it does not appear that an analysis to identify the impact of these types of differences has been completed. Vizient encourages P4QM to consider our recommendation that CMS analyze the data to ensure encounter data is accurate and comparable between FFS and MA before including MA beneficiaries in these measures.
In addition, another important consideration is that MA populations can vary significantly across plans, and in some cases may be selectively healthier. Without careful attention to appropriate risk adjustment, these differences could unintentionally bias performance results. Ensuring that MA data are risk‑adjusted correctly is essential for fair and meaningful comparisons across hospitals.
Comment re: MUC2025-036
We request transparent model specifications, transition periods, and social risk adjustment to avoid penalizing hospitals serving vulnerable populations.
Premier supports Hospital…
Premier supports Hospital-Level, 30-Day, Risk-Standardized Mortality Rate quality measures. Given the growth in Medicare Advantage (MA) enrollment, Premier supports the expansion of these measures to include MA beneficiaries.
However, Premier questions the application of these quality measures for improvement purposes when the data lags by two years. This can impact both the observed performance by not reflecting today’s outcomes and the risk adjustment by using older data to create expected values for more recent patients. Premier has also observed that the risk standardized results do not change much over time and may not reflect performance improvement because it is heavily driven by the random effect in the model. The use of the Predicted to Expected ratio rather than the Observed to Expected ratio, while helpful to reduce undue influence from outliers, does not provide a good reflection of actual performance. Consequently, CMS should emphasize that this methodology is best suited for payment purposes and not quality improvement purposes. The utility of the measure is further hindered by the facility level hierarchical methodology, which does not allow for drill downs into further subgroups, e.g. by physician grouping.
Additionally, Premier is concerned that the quality measurement risk adjustment methodology developed by CMS does not perform well. Premier disagrees with the prior research methodology committee’s decision that a C-statistic of 0.6 for model performance is sufficient to use the risk model to adjust quality measure performance. Similarly, the thresholds for validation and reliability are also too low for both public reporting and payment purposes. CMS should revisit its methodology immediately to ensure it is rewarding and/or penalizing hospitals accurately.
MUC2025-036 measure
Support with modification: Heavily influenced by hospital performance. Recommend strong risk adjustment and caution for use in MA Star Ratings.
Sepsis Measures
Sepsis is the #1 cause of hospital deaths in the U.S. and is the #1 cost of acute care. It is also the #1 cause for expensive and burdensome hospital readmissions with 40% of survivors requiring readmission within 90 days. Further, sepsis is the #2 cause of maternal mortality in the U.S., takes more children's lives than cancer, and disproportionately burdens our underserved communities across the nation.
We have to do better.
Sepsis is a medical emergency that requires timely recognition and treatment.
If we are going to be serious about reduce harm from sepsis, quality measurement must reflect structure, process, and outcomes.
I support the CDC Core Elements to assess sepsis care structure and outcome measures such as risk-adjusted mortality and 30-day all-cause readmissions. These measures are important but not sufficient on their own.
Process measures remain essential to ensure timely and appropriate care for individual patients. SEP-1 is the only nationally standardized process measure for sepsis and provides critical accountability. Research has show SEP-1 to be effective at saving lives without increasing the use of antimicrobials. Its addition to VBP will further secure its place as a critical component of sepsis care in the United States, and we look forward to continuing to revise and improve this measure as we continue to learn more about sepsis and we have more tools to assist it its diagnosis.
New structural and outcome measures should be adopted alongside SEP-1, not in place of it. Maintaining all three elements of measurement via these four measures is essential to protecting patients and improving sepsis care nationwide.
30-Day All-Cause Mortality Rate Following AMI Hospitalization
The American Occupational Therapy Association (AOTA) supports capturing data for Medicare Advantage (MA) beneficiaries in the Hospital 30-Day, All-Cause, Risk-Standardized Mortality Rate (RSMR) Following Acute Myocardial Infarction (AMI) measure. Including MA data is critical to providing a complete and accurate picture of outcomes, given the growing enrollment in MA plans. This addition will enhance transparency, allow for meaningful comparisons across coverage types, and ensure accountability for all payers to cover necessary, high-quality care provided to Medicare beneficiaries.