Centers for Medicare & Medicaid Services

Background Poor health outcomes are well documented among patients with a non-English language preference (NELP). The use of interpreters can improve the quality of care for patients with NELP. Despite a growing and unmet need for interpretation services in the US health care system, rates of interpreter use in the care setting are consistently low. Standardized collection and exchange of patient interpretation needs can improve access to appropriate language assistance services. Objective This study aims to examine current practices for collecting, documenting, and exchanging information on a patient’s self-reported preference for an interpreter in the electronic health record (EHR) and the implementation maturity and adoption level of available data standards. The paper identifies standards implementation; data collection workflows; use cases for collecting, documenting, and exchanging information on a patient’s self-reported preference for an interpreter; challenges to data collection and use; and opportunities to advance standardization of the interpreter needed data element to facilitate patient-centered care. Methods We conducted a narrative review to describe the availability of terminology standards to facilitate health care organization documentation of a patient’s self-reported preference for an interpreter in the EHR. Key informant discussions with EHR developers, health systems, clinicians, a practice-based research organization, a national standards collaborative, a professional health care association, and Federal agency representatives filled in gaps from the narrative review. Results The findings indicate that health care organizations value standardized collection and exchange of patient language assistance service needs and preferences. Informants identified three use cases for collecting, documenting, and exchanging information on a patient’s self-reported preference for an interpreter, which are (1) person-centered care, (2) transitions of care, and (3) health care administration. The discussions revealed that EHR developers provide a data field for documenting interpreter needed data, which are routinely collected across health care organizations through commonly used data collection workflows. However, this data element is not mapped to standard terminologies, such as Logical Observation Identifiers Names and Codes (LOINC) or Systematized Medical Nomenclature for Medicine–Clinical Terminology (SNOMED-CT), consequently limiting the opportunities to electronically share these data between health systems and community-based organizations. The narrative review and key informant discussions identified three potential challenges to using information on a patient’s self-reported preference for an interpreter for person-centered care and quality improvement, which are (1) lack of adoption of available data standards, (2) limited electronic exchange, and (3) patient mistrust. Conclusions Collecting and documenting patient’s self-reported interpreter preferences can improve the quality of services provided, patient care experiences, and equitable health care delivery without invoking a significant burden on the health care system. Although there is routine collection and documentation of patient interpretation needs, the lack of standardization limits the exchange of this information among health care and community-based organizations.

Background Since its 2006 launch, the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey has become the most used hospital patient experience survey, included in public reporting and accountability systems. Responding to emerging evidence of possible gaps in survey content, the Centers for Medicare & Medicaid Services undertook extensive efforts to develop and test new HCAHPS measures. Objectives Describe the development and testing of new HCAHPS measures. Research Design Activities supporting the development and refinement of survey items and measures included 4 patient focus groups, 27 cognitive interviews, and a 12-member technical expert panel. Psychometric testing of proposed survey items and measures used data from a 2021 large-scale randomized experiment. Results Patient focus groups, cognitive testing, psychometric analyses, and a technical expert panel supported the addition of 2 new multi-item measures (Care Coordination and Restfulness of Hospital Environment), a new single-item measure (Information about Symptoms), and the placement of one current HCAHPS item (Quietness) within one of the new composite measures. All proposed measures had strong psychometric properties, including good construct validity and hospital-level reliabilities that ranged from 0.73 for the Information about Symptoms standalone measure to 0.87 for Restfulness of Hospital Environment. Cronbach alpha for the 3 new or modified composite measures ranged from 0.74 to 0.76. Conclusions The new content would broaden HCAHPS by adding new aspects of care that are important to patients. The public reporting of the new measures and their role in payment may support hospital quality improvement efforts.

Background Web-first multimode survey protocols increase HCAHPS survey response rates and representativeness but may result in different HCAHPS scores because of survey mode effects and selective email address availability. A variable absent from many patient-mix adjustment models that may result in more positive patient experiences is whether the hospital admission was planned; adjustment for planned stays may better measure hospital performance. Objectives Develop adjustments for new Web-first survey protocols and planned admissions to facilitate comparisons across hospitals. Research Design Using 2021 survey mode experiment data, we estimate survey protocol effects in linear models predicting HCAHPS top-box outcomes from protocol indicators (which incorporate email availability for Web-first protocols), patient-mix adjustors, and hospital intercepts. We evaluate the unique effect on scores of whether a stay was planned. Results Phone-only and Web-Phone without email produce more positive responses than Mail-only, requiring negative adjustments. All other survey protocol effects and adjustments are mixed in direction and generally small. Planned stays are associated with more positive experiences for otherwise similar patients and make a unique contribution beyond other current patient-mix adjustment variables. Conclusions It is important to adjust HCAHPS scores for survey protocol effects to ensure fair comparisons across hospitals and to enable hospitals to choose the survey protocol that best represents their patients. Incomplete email address availability necessitates that HCAHPS survey protocol adjustment control for email address availability when a Web-first protocol is used. Accounting for differences associated with planned stays may improve patient-mix adjustment.

Purpose To characterize long‐term effects of COVID‐19 among older adults (aged ≥ 65 years). Methods This retrospective descriptive study utilized Medicare Fee‐for‐Service beneficiaries' claims to characterize post‐COVID condition diagnosis code usage, long COVID (defined as post‐COVID condition diagnoses made ≥ 28 days after an initial COVID‐19 diagnosis) incidence, patient demographics, and concurrent diagnoses. Results During April 1, 2020 to May 21, 2022, 193 691 (0.6%) of 31 847 927 Medicare beneficiaries were diagnosed with post‐COVID conditions using ICD‐10‐CM diagnosis codes U09.9 and B94.8, regardless of prior COVID‐19 diagnosis. Post‐COVID condition diagnosis rate was higher among nursing home residents (18.7 per 1000 person‐years) than community‐dwelling beneficiaries (2.8). Among community‐dwelling beneficiaries with a post‐COVID condition diagnosis, 17.5% did not have any prior COVID‐19 diagnosis code U07.1 recorded. Among beneficiaries with COVID‐19 diagnosis, there were no significant sex, age, or race/ethnicity differences between those with post‐COVID conditions ≥ 28 days after COVID‐19 (i.e., long COVID) and those without post‐COVID conditions. Certain myopathies and interstitial pulmonary disease codes were disproportionately present concurrently with long COVID compared to COVID‐19. Conclusions In this large study of 32 million Medicare beneficiaries, we found approximately 194 000 post‐COVID condition diagnoses. Post‐COVID condition diagnosis rate was higher among nursing home residents, highlighting the substantial burden of COVID‐19 in this vulnerable population. Community‐dwelling beneficiaries were less likely to seek medical care for COVID‐19 events than nursing home residents, which may suggest differences in COVID‐19 severity and respiratory disease detection between these populations. Long COVID risk after COVID‐19 infection may be similar across demographic groups.

Importance Health information technology, such as electronic health records (EHRs), has been widely adopted, yet accessing and exchanging data in the fragmented US health care system remains challenging. To unlock the potential of EHR data to improve patient health, public health, and health care, it is essential to streamline the exchange of health data. As leaders across the US Department of Health and Human Services (DHHS), we describe how DHHS has implemented fundamental building blocks to achieve this vision. Observations Across DHHS, we have implemented 3 foundational building blocks called for by the 2016 21st Century Cures Act to create a unified approach for secure, high-quality, and timely exchange of health data across the health care system. The United States Core Data for Interoperability provides a minimum baseline for data elements that must be available in federally regulated health information technology systems such as certified EHRs. These data elements now must be accessible using Fast Healthcare Interoperability Resources—a secure, flexible, and open-industry standard for health data exchange. The Trusted Exchange Framework and Common Agreement provides a network to securely exchange health data across the country. The 3 building blocks of United States Core Data for Interoperability, Fast Healthcare Interoperability Resources, and Trusted Exchange Framework and Common Agreement are now in place thanks to diligent public and private sector work over 2 administrations. Across DHHS, we are working to refine these building blocks and increase their adoption through regulatory authorities, grants, and public-private partnerships. Conclusions and Relevance The technological building blocks described in this article are creating a unified approach to health data exchange for patient access, clinical care, quality improvement, scientific research, public health, and other uses of health data. Collaborations between the public, nonprofit, and private sectors are needed to maximize their potential. By unlocking the potential of health data, these building blocks are the foundation of a 21st-century digital health care system that will improve the experience of patients and clinicians and result in better health outcomes.

This Viewpoint discusses a Centers for Medicare and Medicaid Innovation volunteer greenhouse gas emissions reporting and technical assistance program for hospitals and hospital systems.

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Objectives This study illustrates how the statistical reliability of an individual measure relates to the overall reliability of a composite metric, as understanding this relationship provides additional information when evaluating measures for endorsement. Background National quality measure endorsement processes typically evaluate individual metrics on criteria such as importance and scientific acceptability (eg, reliability). In practice, quality measures may be used in composite rating systems, which aid in the interpretation of overall quality differences. Methods We define an individual measure’s reliability by its intraclass correlation and analytically establish the relationship between a composite’s reliability and the reliability of its components. We use real data to confirm this relationship under various scenarios. We are motivated by 8 quality measures, which comprise the Quality of Patient Care Star Ratings on Dialysis Facility Care Compare. These measure 4 primary outcomes (mortality, hospitalizations, readmissions, and blood transfusions), vascular access (2 measures), and facility processes (2 measures). Results Depending on the reliability of the individual measures, their respective weights in the composite, and their pairwise correlations, there are circumstances when adding a new measure, even if it is less reliable, increases the composite’s reliability. For the dialysis facility Star Ratings, we find that the combined reliability of measures grouped within certain domains of care exceeded the reliability of the individual measures within those domains. Conclusions New quality measures may add utility to a composite rating system under certain circumstances—a consideration that should, in part, factor into quality measure endorsement processes.

Objective The objective of this study was to compare 2 approaches for representing self-reported race-and-ethnicity, additive modeling (AM), in which every race or ethnicity a person endorses counts toward measurement of that category, and a commonly used mutually exclusive categorization (MEC) approach. The benchmark was a gold-standard, but often impractical approach that analyzes all combinations of race-and-ethnicity as distinct groups. Methods Data came from 313,739 respondents to the 2021 Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys who self-reported race-and-ethnicity. We used regression to estimate how accurately AM and MEC approaches predicted racial-and-ethnic differences in 5 CAHPS patient experience measures and 4 patient characteristics that we considered proxies for social determinants of health (SDOH): age, educational attainment, and self-reported general and mental health. We calculated average residual error proportions for AM and MEC estimates relative to all-combination estimates. Results In predicting CAHPS scores by race-and-ethnicity, on average 0.9% of the variance across groups in the AM and MEC approaches represented a departure from the gold standard. In predicting proxy SDOH variables, on average 4.7% of the AM variance across groups and 7.1% of the MEC variance across groups represented departures from the gold standard. Conclusion Researchers may want to consider AM over MEC when modeling outcomes by race-and-ethnicity given that AM outperforms MEC in predicting racial-and-ethnic differences in proxy SDOH characteristics and is comparably accurate in predicting differences in patient experience. Unlike MEC, AM does not assume that every multiracial person has similar outcomes and that Hispanic persons have similar outcomes irrespective of race.

Background Elective primary total hip and total knee arthroplasty (collectively, total joint arthroplasties [TJAs]) are commonly performed procedures that can reduce pain and improve function. TJAs are generally safe, but complications can occur. Although historically performed as inpatient procedures, TJAs are increasingly being performed in the outpatient setting. We sought to develop a scientifically acceptable cross-setting measure for evaluating care quality across inpatient and outpatient settings. Methods Using Medicare administrative claims and enrollment data for qualifying TJA patients, we respecified the Centers for Medicare & Medicaid Services (CMS) inpatient-only risk-standardized TJA complications measure to assess complication rates following elective primary TJAs performed in an inpatient or outpatient setting. We aligned inpatient and outpatient coding practices and used hierarchical logistic regression to calculate hospital-specific, risk-standardized complication rates (RSCRs). Lower rates correspond to better quality. Using accepted approaches for CMS measures, we tested measure reliability and vetted key measure decisions with patient and provider input. Results A single combined model including the procedure setting as a risk variable produced the highest discrimination (C-statistic for a single combined model with a setting indicator: 0.664, C-statistic for the inpatient-only model: 0.651, C-statistic for the outpatient-only model: 0.638). Among the 2,747 hospitals with at least 25 TJAs, the mean RSCR (using the combined model with a setting indicator) was 2.91% (median RSCR: 2.85%; interquartile range: 2.59% to 3.18%). The median odds ratio for complication occurrence at a higher-risk hospital compared with a lower-risk hospital was 1.33. Conclusions We respecified a measure to assess hospital inpatient or outpatient TJA performance and evaluated the reliability and validity of the measure. The findings showed variation in hospital-level complication rates across settings as indicated by this measure, supporting the feasibility of evaluating hospital performance using a more representative population than inpatient TJAs alone. Level of Evidence Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.

This article estimates differences and difference-in-differences in patient experiences for Veterans Health Administration (VA) compared to non-VA patients in 2017, when there was concern about the health quality of VA hospitals, and in 2021, the second year of the COVID-19 pandemic, both overall, and for specific patient groups. We used data from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey. In 2017, HCAHPS performance was somewhat better for non-VA than for VA hospitals, with Care Transition being the only measure for which VA hospitals performed better on average. By 2021, HCAHPS performance was better for VA than for non-VA hospitals for all but two measures (Quietness and Discharge Information), for which there were no differences from non-VA hospitals. In 2017, the VA provided worse experiences than non-VA hospitals for Black and poor-health patients; in 2021, VA hospitals outperformed non-VA hospitals for these, and all patient subgroups examined.

Acute respiratory failure (ARF) associated with antipsychotic use has been documented through case reports and population-based studies. To assess whether the recent use of antipsychotics is associated with an increased risk of ARF in U.S. Medicare beneficiaries with chronic obstructive pulmonary disease. Case-crossover study conducted among U.S. Fee-for-Service Medicare beneficiaries with chronic obstructive pulmonary disease hospitalized with ARF, from January 1, 2007, through December 31, 2019. Oral antipsychotics. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) for ARF requiring invasive mechanical ventilation associated with the use of antipsychotics in the case period (days -14 to -1) compared to the control period (days -75 to -88). We identified 145,018 cases (mean age 69.4 years, 57.2% female). Of these, 2,003 had antipsychotic use only during the case period and 1,728 only during the control period. The aOR of ARF within 14 days after antipsychotic use was 1.13 (95% CI, 1.06, 1.20). The risk increased with increasing age, being statistically significant in patients ages 75–84 years (aOR: 1.37 [95% CI, 1.17, 1.60]) and 85 + years (aOR: 1.50 [95% CI, 1.20, 1.89]), but not in beneficiaries under 75 years of age (aOR 18–49 years: 1.01 [95% CI, 0.85, 1.20]; 50–64 years: 1.03 [95% CI, 0.92, 1.15]; 65–74 years: 1.12 [95% CI, 0.98, 1.27]). Recent antipsychotic use by Medicare beneficiaries with chronic obstructive pulmonary disease was associated with an increased risk of ARF in those aged 75 years and older.

Background: There is a paucity of data on treatment of osteoporosis in patients with advanced chronic kidney disease (CKD). Objective: To assess the risk for emergently treated hypocalcemia with denosumab by stage of CKD and presence of CKD-mineral and bone disorder (CKD-MBD). Design: Target trial emulation. Setting: Medicare fee-for-service data with prescription drug coverage, 2012 to 2020. Participants: Female patients aged 65 years or older initiating denosumab, oral bisphosphonates, or intravenous (IV) bisphosphonates for osteoporosis. Measurements: Hospital and emergency department admissions (that is, emergent care) for hypocalcemia were assessed in the first 12 treatment weeks. Inverse probability of treatment weighted cumulative incidence and weighted risk differences (RDs) were calculated. Results: A total of 361 453 patients treated with denosumab, 829 044 treated with oral bisphosphonates, and 160 413 treated with IV bisphosphonates were identified. Risk for emergently treated hypocalcemia with denosumab versus oral bisphosphonates increased with worsening CKD stage (P < 0.001), with greatest risk among dialysis-dependent (DD) patients (3.01% vs. 0.00%; RD, 3.01% [95% CI, 2.27% to 3.77%]) and non-dialysis-dependent (NDD) patients with CKD stages 4 and 5 (0.57% vs. 0.03%; RD, 0.54% [CI, 0.41% to 0.68%]). Among patients with stages 4 and 5 CKD (NDD + DD), denosumab had a greater risk for emergently treated hypocalcemia versus oral bisphosphonates in those with CKD-MBD (1.53% vs. 0.02%; RD, 1.51% [CI, 1.21% to 1.78%]) than in those without CKD-MBD (0.22% vs. 0.03%; RD, 0.19% [CI, 0.08% to 0.31%]). Denosumab also showed increased risk compared with IV bisphosphonates. Limitation: Generalizability to men and non-Medicare populations. Conclusion: Risk for emergently treated hypocalcemia with denosumab increased with worsening CKD stage and was highest in DD patients and those with CKD-MBD. Primary funding source: U.S. Food and Drug Administration.

Significant research and attention to date have focused on cost-related medication nonadherence as rising prescription drug prices worsen affordability and access for many Americans. This study investigated self-reported sources of medication nonadherence, measuring both cost- and non–cost-related medication nonadherence among community-dwelling Medicare Part D beneficiaries in 2022. A total of 13.7% of beneficiaries (4 589 843) reported some type of medication nonadherence; 7.5% reported medication nonadherence related to cost and 6.2% reported for non-cost reasons. Beneficiaries reporting food insecurity, poor functional status, and lack of understanding of the Part D benefit were more likely to report both types of medication nonadherence after adjustment for sociodemographic factors. Beneficiaries receiving the Low-Income Subsidy had lower odds of reporting cost-related but greater odds of reporting non–cost-related medication nonadherence. These findings suggest that non–cost-related sources of medication nonadherence, such as beneficiary preferences or beliefs, understanding of their health situation or insurance coverage, and ability to fill a prescription, are significant contributors to overall nonadherence. Non–cost-related medication nonadherence should be considered alongside recent changes to the Part D benefit and in future Part D Centers for Medicare and Medicaid Services (CMS) Innovation Center models, such as the proposed Medicare $2 Drug List Model, in order to maximize the impact of these initiatives.