Quantifying Industry Spending on Promotional Events Using Open Payments Data.

Importance: Sponsorship of promotional events for health professionals is a key facet of marketing campaigns for pharmaceuticals and medical devices; however, there appears to be limited transparency regarding the scope and scale of this spending. Objective: To develop a novel method for describing the scope and quantifying the spending by US pharmaceutical and medical companies on industry-sponsored promotional events for particular products. Design and Setting: This was a cross-sectional study using records from the Centers for Medicare & Medicaid's Open Payments database on payments made to prescribing clinicians from January 1 to December 21, 2022. Main Outcomes and Measures: An event-centric approach was used to define sponsored events as groupings of payment records with matching variables. Events were characterized by value (coffee, lunch, dinner, or banquet) and number of attendees (small vs large). To test the method, the number of and total spending for each type of event across professional groups were calculated and used to identify the top 10 products related to dinner events. To validate the method, we extracted all event details advertised on the websites of 4 state-level nurse practitioner associations that regularly hosted industry-sponsored dinner events during 2022 and compared these with events identified in the Open Payments database. Results: A total of 1 154 806 events sponsored by pharmaceutical and medical device companies were identified for 2022. Of these, 1 151 351 (99.7%) had fewer than 20 attendees, and 922 214 (80.0%) were considered to be a lunch ($10-$30 per person). Seven companies sponsored 16 031 dinners for the top 10 products. Of the 227 sponsored in-person dinner events hosted by the 4 state-level nurse practitioner associations, 168 (74.0%) matched events constructed from the Open Payments dataset. Conclusions and Relevance: These findings indicate that an event-centric analysis of Open Payments data is a valid method to understand the scope and quantify spending by pharmaceutical and medical device companies on industry-sponsored promotional events attended by prescribers. Expanding and enforcing the reporting requirements to cover all payments to all registered health professionals would improve the accuracy of estimates of the true extent of all sponsored events and their impact on clinical practice.

Discordance in Published 30-Day Readmission Rates Following Primary Total Hip and Total Knee Arthroplasty: Centers for Medicare and Medicaid Services (CMS) Versus the National Surgical Quality Improvement Program (NSQIP).

Background: 30-day readmission is an important quality metric evaluated following primary total joint arthroplasty (TJA) that has implications for hospital performance and reimbursement. Differences in how 30-day readmissions are defined between Centers for Medicare and Medicaid Services (CMS) and other quality improvement programs (i.e., National Surgical Quality Improvement Program [NSQIP]) may create discordance in published 30-day readmission rates. The purpose of this study was to evaluate 30-day readmission rates following primary TJA using two different temporal definitions. Methods: Patients undergoing primary total hip and primary total knee arthroplasty at a single academic institution from 2015-2020 were identified via common procedural terminology (CPT) codes in the electronic medical record (EMR) and institutional NSQIP data. Readmissions that occurred within 30 days of surgery (consistent with definition of 30-day readmission in NSQIP) and readmissions that occurred within 30 days of hospital discharge (consistent with definition of 30-day readmission from CMS) were identified. Rates of 30-day readmission and the prevalence of readmission during immortal time were calculated. Results: In total, 4,202 primary TJA were included. The mean hospital length of stay (LOS) was 1.79 days. 91% of patients were discharged to home. 30-day readmission rate using the CMS definition was 3.1% (130/4,202). 30-day readmission rate using the NSQIP definition was 2.7% (113/4,202). Eight readmissions captured by the CMS definition (6.1%) occurred during immortal time. Conclusion: Differences in temporal definitions of 30-day readmission following primary TJA between CMS and NSQIP results in discordant rates of 30-day readmission. Level of Evidence: III.

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.

Effectiveness and Cost-Effectiveness of Colorectal Cancer Screening With a Blood Test That Meets the Centers for Medicare & Medicaid Services Coverage Decision.

BACKGROUND & AIMS: A blood-based colorectal cancer (CRC) screening test may increase screening participation. However, blood tests may be less effective than current guideline-endorsed options. The Centers for Medicare & Medicaid Services (CMS) covers blood tests with sensitivity of at least 74% for detection of CRC and specificity of at least 90%. In this study, we investigate whether a blood test that meets these criteria is cost-effective. METHODS: Three microsimulation models for CRC (MISCAN-Colon, CRC-SPIN, and SimCRC) were used to estimate the effectiveness and cost-effectiveness of triennial blood-based screening (from ages 45 to 75 years) compared to no screening, annual fecal immunochemical testing (FIT), triennial stool DNA testing combined with an FIT assay, and colonoscopy screening every 10 years. The CMS coverage criteria were used as performance characteristics of the hypothetical blood test. We varied screening ages, test performance characteristics, and screening uptake in a sensitivity analysis. RESULTS: Without screening, the models predicted 77-88 CRC cases and 32-36 CRC deaths per 1000 individuals, costing $5.3-$5.8 million. Compared to no screening, blood-based screening was cost-effective, with an additional cost of $25,600-$43,700 per quality-adjusted life-year gained (QALYG). However, compared to FIT, triennial stool DNA testing combined with FIT, and colonoscopy, blood-based screening was not cost-effective, with both a decrease in QALYG and an increase in costs. FIT remained more effective (+5-24 QALYG) and less costly (-$3.2 to -$3.5 million) than blood-based screening even when uptake of blood-based screening was 20 percentage points higher than uptake of FIT. CONCLUSION: Even with higher screening uptake, triennial blood-based screening, with the CMS-specified minimum performance sensitivity of 74% and specificity of 90%, was not projected to be cost-effective compared with established strategies for colorectal cancer screening.

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.