Incorporating Medicare Advantage Admissions Into the CMS Hospital-Wide Readmission Measure.

Importance: Medicare Advantage (MA) enrollment is rapidly expanding, yet Centers for Medicare & Medicaid Services (CMS) claims-based hospital outcome measures, including readmission rates, have historically included only fee-for-service (FFS) beneficiaries. Objective: To assess the outcomes of incorporating MA data into the CMS claims-based FFS Hospital-Wide All-Cause Unplanned Readmission (HWR) measure. Design, Setting, and Participants: This cohort study assessed differences in 30-day unadjusted readmission rates and demographic and risk adjustment variables for MA vs FFS admissions. Inpatient FFS and MA administrative claims data were extracted from the Integrated Data Repository for all admissions for Medicare beneficiaries from July 1, 2018, to June 30, 2019. Measure reliability and risk-standardized readmission rates were calculated for the FFS and MA cohort vs the FFS-only cohort, overall and within specialty subgroups (cardiorespiratory, cardiovascular, medicine, surgery, neurology), then changes in hospital performance quintiles were assessed after adding MA admissions. Main Outcome and Measure: Risk-standardized readmission rates. Results: The cohort included 11 029 470 admissions (4 077 633 [37.0%] MA; 6 044 060 [54.8%] female; mean [SD] age, 77.7 [8.2] years). Unadjusted readmission rates were slightly higher for MA vs FFS admissions (15.7% vs 15.4%), yet comorbidities were generally lower among MA beneficiaries. Test-retest reliability for the FFS and MA cohort was higher than for the FFS-only cohort (0.78 vs 0.73) and signal-to-noise reliability increased in each specialty subgroup. Mean hospital risk-standardized readmission rates were similar for the FFS and MA cohort and FFS-only cohorts (15.5% vs 15.3%); this trend was consistent across the 5 specialty subgroups. After adding MA admissions to the FFS-only HWR measure, 1489 hospitals (33.1%) had their performance quintile ranking changed. As their proportion of MA admissions increased, more hospitals experienced a change in their performance quintile ranking (147 hospitals [16.3%] in the lowest quintile of percentage MA admissions; 408 [45.3%] in the highest). The combined cohort added 63 hospitals eligible for public reporting and more than 4 million admissions to the measure. Conclusions and Relevance: In this cohort study, adding MA admissions to the HWR measure was associated with improved measure reliability and precision and enabled the inclusion of more hospitals and beneficiaries. After MA admissions were included, 1 in 3 hospitals had their performance quintile changed, with the greatest shifts among hospitals with a high percentage of MA admissions.

Access denied: CMS' action hurts patients with cancer in rural America.

In 2020, cancer claimed more than 600,000 lives in the US. Cancer is an unyielding public health crisis. Cancer treatments typically involve multidisciplinary approaches, including surgery, radiation therapy, chemotherapy, and oral medications. For patients, especially those in rural areas, obtaining multiple oral medications can be inconvenient. The adoption of delivering cancer medications from medically integrated pharmacies (MIPs) has become popular in recent years. On May 12, 2023, CMS introduced guidelines restricting MIPs from delivering cancer-specific medications by mail or to oncology satellite offices and also requiring patients themselves to pick up the medications in person. This regulatory change has had a devastating impact on patients with cancer in rural and underserved communities, exacerbating existing health care disparities. This commentary explores the negative impacts of the policy change by CMS in rural America.

Advanced Hospital Healthcare at Home-How Is It Going?

The Centers for Medicare & Medicaid Services' (CMS) Acute Hospital Care at Home (AHCAH) waiver, which launched in November 2020, has prompted hundreds of hospitals across the country to initiate programs that allow certain patients to complete their acute care stays in the familiar comfort of their homes. But this waiver is about to expire in December 2024. It is a success; but can we continue it?

A Rule-Based Solution to Opaque Medical Billing in the U.S.

Patients and physicians do not know the cost of medical procedures. Opaque medical billing thus contributes to exorbitant, rising medical costs, burdening the healthcare system and individuals. After criticizing two proposed solutions to the problem of opaque medical billing, I argue that the Centers for Medicare and Medicaid Services should pursue a rule requiring that patients be informed by the physician of a reasonable out-of-pocket expense estimate for non-urgent procedures prior to services rendered.

Proposed CMS Data Access Changes May Hamper Health Services Research.

This Viewpoint discusses how proposed Centers for Medicare & Medicaid Services data access changes may impede health services research.

Preserving Access to CMS Data for Early-Career Researchers.

This Viewpoint describes the potential consequences of the Centers for Medicare & Medicaid Services' (CMS') proposed data access policy change for graduate students and early-career researchers.

Calculation of Overall Hospital Quality Star Ratings With and Without Inclusion of the Peer Grouping Step.

Importance: The Centers for Medicare & Medicaid Services (CMS) Overall Star Rating is widely used by patients and consumers, and there is continued stakeholder curiosity surrounding the inclusion of a peer grouping step, implemented to the 2021 Overall Star Rating methods. Objective: To calculate hospital star rating scores with and without the peer grouping step, with the former approach stratifying hospitals into 3-, 4-, and 5-measure group peer groups based on the number of measure groups with at least 3 reported measures. Design, Setting, and Participants: This cross-sectional study used Care Compare website data from January 2023 for 3076 hospitals that received a star rating in 2023. Data were analyzed from April 2023 to December 2023. Exposure: Peer grouping vs no peer grouping. Main Outcomes and Measures: The primary outcome was the distribution of star ratings, with 1 star being the lowest-performing hospitals and 5 stars, the highest. Analyses additionally identified the number of hospitals with a higher, lower, or identical star rating with the use of the peer grouping step compared with its nonuse, stratified by certain hospital characteristics. Results: Among 3076 hospitals that received a star rating in 2023, most were nonspecialty (1994 hospitals [64.8%]), nonteaching (1807 hospitals [58.7%]), non-safety net (2326 hospitals [75.6%]), non-critical access (2826 hospitals [91.9%]) hospitals with fewer than 200 beds (1822 hospitals [59.2%]) and located in an urban geographic designations (1935 hospitals [62.9%]). The presence of the peer grouping step resulted in 585 hospitals (19.0%) being assigned a different star rating than if the peer grouping step was absent, including considerably more hospitals receiving a higher star rating (517 hospitals) rather than a lower (68 hospitals) star rating. Hospital characteristics associated with a higher star rating included urbanicity (351 hospitals [67.9%]), non-safety net status (414 hospitals [80.1%]), and fewer than 200 beds (287 hospitals [55.6%]). Collectively, the presence of the peer grouping step supports a like-to-like comparison among hospitals and supports the ability of patients to assess overall hospital quality. Conclusions and Relevance: In this cross-sectional study, inclusion of the peer grouping in the CMS star rating method resulted in modest changes in hospital star ratings compared with application of the method without peer grouping. Given improvement in face validity and the close association between the current peer grouping approach and stakeholder needs for peer-comparison, the current CMS Overall Star Rating method allows for durable comparisons in hospital performance.

Individualized empirical null estimation for exact tests of healthcare quality.

United States federal agencies evaluate healthcare providers to identify, flag, and potentially penalize those that deliver low-quality care compared to national expectations. In practice, evaluation metrics are inevitably impacted by unobserved confounding factors, which reduce flagging accuracy and cause the statistics to be overdispersed relative to the theoretical null distributions. In response to this issue, several authors have proposed individualized empirical null (IEN) methods to estimate an appropriate null distribution for each provider's evaluation statistic while taking into account the provider's effective size. However, existing IEN methods require that the statistics asymptotically follow normal distributions, which often does not hold in applications with small providers or misspecified models. In this article, we develop an IEN framework for exact hypothesis tests that accounts for the impact of unobserved confounding without making any asymptotic assumptions. Simulations show that the proposed IEN method has greater flagging accuracy compared to conventional approaches. We apply these methods to evaluate dialysis facilities and transplant centers that are monitored by the Centers for Medicare and Medicaid Services.

Infection Control Measure Performance in Long-Term Care Hospitals and Their Relationship to Joint Commission Accreditation.

BACKGROUND: This study evaluated the relationship between Joint Commission accreditation and health care-associated infections (HAIs) in long-term care hospitals (LTCHs). METHODS: This observational study used Centers for Medicare & Medicaid Services (CMS) LTCH data for the period 2017 to June 2021. The standardized infection ratio (SIR) of three measures used by the Centers for Disease Control and Prevention's National Healthcare Safety Network were used as dependent variables in a random coefficient Poisson regression model (adjusting for CMS region, owner type, and bed size quartile): catheter-associated urinary tract infections (CAUTIs), Clostridioides difficile infections (CDIs), and central line-associated bloodstream infections (CLABSIs) for the periods 2017 to 2019 and July 1, 2020, to June 30, 2021. Data from January 1 to June 30, 2020, were excluded due to the COVID-19 pandemic. RESULTS: The data set included 244 (73.3%) Joint Commission-accredited and 89 (26.7%) non-Joint Commission-accredited LTCHs. Compared to non-Joint Commission-accredited LTCHs, accredited LTCHs had significantly better (lower) SIRs for CLABSI and CAUTI measures, although no differences were observed for CDI SIRs. There were no significant differences in year trends for any of the HAI measures. For each year of the study period, a greater proportion of Joint Commission-accredited LTCHs performed significantly better than the national benchmark for all three measures (p = 0.04 for CAUTI, p = 0.02 for CDI, p = 0.01 for CLABSI). CONCLUSION: Although this study was not designed to establish causality, positive associations were observed between Joint Commission accreditation and CLABSI and CAUTI measures, and Joint Commission-accredited LTCHs attained more consistent high performance over the four-year study period for all three measures. Influencing factors may include the focus of Joint Commission standards on infection control and prevention (ICP), including the hierarchical approach to selecting ICP-related standards as inputs into LTCH policy.

Requesting That Delirium Achieve Parity With Acute Encephalopathy in the MS-DRG System.

Since 2007, the Medicare Severity Diagnosis Related Groups classification system has favored billing codes for acute encephalopathy over delirium codes in determining hospital reimbursement and several quality-of-care value metrics, despite broad overlap between these sets of diagnostic codes. Toxic and metabolic encephalopathy codes are designated as major complication or comorbidity, whereas causally specified delirium codes are designated as complication or comorbidity and thus associated with a lower reimbursement and lesser impact on value metrics. The authors led a submission to the U.S. Centers for Medicare and Medicaid Services requesting that causally specified delirium be designated major complication or comorbidity alongside toxic and metabolic encephalopathy. Delirium warrants reclassification because it satisfies U.S. Centers for Medicare and Medicaid Services' guiding principles for re-evaluating Medicare Severity Diagnosis Related Group severity levels. Delirium: (1) has a bidirectional relationship with the permanent condition of dementia (major neurocognitive disorder per DSM-5-TR), (2) indexes vulnerability across populations, (3) impacts healthcare systems across levels of care, (4) complicates postoperative recovery, (5) consigns patients to higher levels of care, (6) impedes patient engagement in care, (7) has several recent treatment guidelines, (8) often indicates neuronal/brain injury, and (9) represents a common expression of terminal illness. The proposal's impact was explored using the 2019 National Inpatient Sample, which suggested that increasing delirium's complexity designation would lead to an upcoding of less than 1% of eligible discharges. Parity for delirium is essential to enhancing awareness of delirium's clinical and economic costs. Appreciating delirium's impact would encourage delirium prevention and screening efforts, thereby mitigating its dire outcomes for patients, families, and healthcare systems.

Comparing the Centers for Medicare and Medicaid Services (CMS) enrollment data death dates to the National Death Index (NDI).

PURPOSE: In the United States, the National Death Index (NDI) is the most complete source of death information, while epidemiologic studies with mortality outcomes often rely on U.S. Medicare data for outcome ascertainment. The purpose of this study was to assess the agreement of death information between the Centers for Medicare & Medicaid Services (CMS) Medicare enrolment data and NDI. METHODS: Using Medicare and NDI data from 1999 through 2016, we identified Medicare beneficiaries who were reported dead in the CMS Medicare enrolment database (EDB) and Common Medicare Environment (CME), linked these beneficiaries to the NDI using CMS Health Insurance Claim number, and compared death dates between the two data sources. To assess agreement between our data sources, we calculated kappa scores; where a kappa of 1 indicates perfect agreement and a kappa of 0 indicates agreement equivalent to chance. We also examined CMS to NDI linkage and death date matching for stability over time. RESULTS: Of the 36 785 640, Medicare beneficiaries reported dead in CMS enrollment data from 1999 to 2016, 97.5% were linked to the NDI. A kappa score of 0.98 showed a near perfect agreement between NDI and CMS reported deaths. The percentage of linked cases exactly matching on death dates increased from 94.8% in 1999 to 99.4% in 2016. CONCLUSIONS: Our findings suggest strong concordance between death dates as recorded by CMS enrollment data and the NDI in the entire Medicare population.

New CMS Nursing Home Ownership Data: Major Gaps And Discrepancies.

Nursing home ownership has become increasingly complicated, partly because of the growth of facilities owned by institutional investors such as private equity (PE) firms and real estate investment trusts (REITs). Although the ownership transparency and accountability of nursing homes have historically been poor, the Biden administration's nursing home reform plans released in 2022 included a series of data releases on ownership. However, our evaluation of the newly released data identified several gaps: One-third of PE and fewer than one-fifth of REIT investments identified in the proprietary Irving Levin Associates and S&P Capital IQ investment data were present in Centers for Medicare and Medicaid Services (CMS) publicly available ownership data. Similarly, we obtained different results when searching for the ten top common owners of nursing homes using CMS data and facility survey reports of chain ownership. Finally, ownership percentages were missing in the CMS data for 82.40 percent of owners in the top ten chains and 55.21 percent of owners across all US facilities. Although the new data represent an important step forward, we highlight additional steps to ensure that the data are timely, accurate, and responsive. Transparent ownership data are fundamental to understanding the adequacy of public payments to provide patient care, enable policy makers to make timely decisions, and evaluate nursing home quality.

Missed opportunities in hospital quality measurement during the COVID-19 pandemic: a retrospective investigation of US hospitals' CMS Star Ratings and 30-day mortality during the early pandemic.

OBJECTIVES: In the USA and UK, pandemic-era outcome data have been excluded from hospital rankings and pay-for-performance programmes. We assessed the relationship between US hospitals' pre-pandemic Centers for Medicare and Medicaid Services (CMS) Overall Hospital Star ratings and early pandemic 30-day mortality among both patients with COVID and non-COVID to understand whether pre-existing structures, processes and outcomes related to quality enabled greater pandemic resiliency. DESIGN AND DATA SOURCE: A retrospective, claim-based data study using the 100% Inpatient Standard Analytic File and Medicare Beneficiary Summary File including all US Medicare Fee-for-Service inpatient encounters from 1 April 2020 to 30 November 2020 linked with the CMS Hospital Star Ratings using six-digit CMS provider IDs. OUTCOME MEASURE: The outcome was risk-adjusted 30-day mortality. We used multivariate logistic regression adjusting for age, sex, Elixhauser mortality index, US Census Region, month, hospital-specific January 2020 CMS Star rating (1-5 stars), COVID diagnosis (U07.1) and COVID diagnosis×CMS Star Rating interaction. RESULTS: We included 4 473 390 Medicare encounters from 2533 hospitals, with 92 896 (28.2%) mortalities among COVID-19 encounters and 387 029 (9.3%) mortalities among non-COVID encounters. There was significantly greater odds of mortality as CMS Star Ratings decreased, with 18% (95% CI 15% to 22%; p<0.0001), 33% (95% CI 30% to 37%; p<0.0001), 38% (95% CI 34% to 42%; p<0.0001) and 60% (95% CI 55% to 66%; p<0.0001), greater odds of COVID mortality comparing 4-star, 3-star, 2-star and 1-star hospitals (respectively) to 5-star hospitals. Among non-COVID encounters, there were 17% (95% CI 16% to 19%; p<0.0001), 24% (95% CI 23% to 26%; p<0.0001), 32% (95% CI 30% to 33%; p<0.0001) and 40% (95% CI 38% to 42%; p<0.0001) greater odds of mortality at 4-star, 3-star, 2-star and 1-star hospitals (respectively) as compared with 5-star hospitals. CONCLUSION: Our results support a need to further understand how quality outcomes were maintained during the pandemic. Valuable insights can be gained by including the reporting of risk-adjusted pandemic era hospital quality outcomes for high and low performing hospitals.