Quality Measurement in Oncology: Time to Take the Next Step!

Oncology is a complex clinical specialty often requiring the close interaction of teams of different medical specialists for a successful outcome. The field is rapidly evolving scientifically, with successive discoveries of oncologic driver mutations soon followed by therapeutic agents able to interrupt the neoplastic process. Unfortunately, objective quality measurement demonstrates that many patients are not receiving optimal care, from diagnostic accuracy, therapeutic, or end-of-life perspectives. Quality measurement, reporting, and payment programs have the potential to focus attention on these care gaps and drive improvement. The federal government, as the largest single payer of health care services in the United States, has a compelling national interest to ensure that the medical care of Americans is at the highest level achievable. Accordingly, quality reporting and payment programs have been established in federal health care payment programs to drive improvements in care. This article reviews the science of quality measurement, documented gaps in oncology care, and ways to use new information technologies to decrease clinician burden associated with quality reporting. The article reviews how a measure is developed and incorporated into a Centers for Medicare & Medicaid Services (CMS) program. It also summarizes federal programs relevant to oncology care and the individual measures used in these programs. CMS looks forward to working jointly with the oncology community to drive continuous improvements in care.

Processing and validation of inpatient Medicare Advantage data for use in hospital outcome measures.

OBJECTIVE: To determine the feasibility of integrating Medicare Advantage (MA) admissions into the Centers for Medicare & Medicaid Services (CMS) hospital outcome measures through combining Medicare Advantage Organization (MAO) encounter- and hospital-submitted inpatient claims. DATA SOURCES AND STUDY SETTING: Beneficiary enrollment data and inpatient claims from the Integrated Data Repository for 2018 Medicare discharges. STUDY DESIGN: We examined timeliness of MA claims, compared diagnosis and procedure codes for admissions with claims submitted both by the hospital and the MAO (overlapping claims), and compared demographic characteristics and principal diagnosis codes for admissions with overlapping claims versus admissions with a single claim. DATA COLLECTION/EXTRACTION METHODS: We combined hospital- and MAO-submitted claims to capture MA admissions from all hospitals and identified overlapping claims. For admissions with only an MAO-submitted claim, we used provider history data to match the National Provider Identifier on the claim to the CMS Certification Number used for reporting purposes in CMS outcome measures. PRINCIPAL FINDINGS: After removing void and duplicate claims, identifying overlapped claims between the hospital- and MAO-submitted datasets, restricting claims to acute care and critical access hospitals, and bundling same admission claims, we identified 5,078,611 MA admissions. Of these, 76.1% were submitted by both the hospital and MAO, 14.2% were submitted only by MAOs, and 9.7% were submitted only by hospitals. Nearly all (96.6%) hospital-submitted claims were submitted within 3 months after a one-year performance period, versus 85.2% of MAO-submitted claims. Among the 3,864,524 admissions with overlapping claims, 98.9% shared the same principal diagnosis code between the two datasets, and 97.5% shared the same first procedure code. CONCLUSIONS: Inpatient MA data are feasible for use in CMS claims-based hospital outcome measures. We recommend prioritizing hospital-submitted over MAO-submitted claims for analyses. Monitoring, data audits, and ongoing policies to improve the quality of MA data are important approaches to address potential missing data and errors.

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.

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.

Pre-COVID-19 hospital quality and hospital response to COVID-19: examining associations between risk-adjusted mortality for patients hospitalised with COVID-19 and pre-COVID-19 hospital quality.

OBJECTIVES: The extent to which care quality influenced outcomes for patients hospitalised with COVID-19 is unknown. Our objective was to determine if prepandemic hospital quality is associated with mortality among Medicare patients hospitalised with COVID-19. DESIGN: This is a retrospective observational study. We calculated hospital-level risk-standardised in-hospital and 30-day mortality rates (risk-standardised mortality rates, RSMRs) for patients hospitalised with COVID-19, and correlation coefficients between RSMRs and pre-COVID-19 hospital quality, overall and stratified by hospital characteristics. SETTING: Short-term acute care hospitals and critical access hospitals in the USA. PARTICIPANTS: Hospitalised Medicare beneficiaries (Fee-For-Service and Medicare Advantage) age 65 and older hospitalised with COVID-19, discharged between 1 April 2020 and 30 September 2021. INTERVENTION/EXPOSURE: Pre-COVID-19 hospital quality. OUTCOMES: Risk-standardised COVID-19 in-hospital and 30-day mortality rates (RSMRs). RESULTS: In-hospital (n=4256) RSMRs for Medicare patients hospitalised with COVID-19 (April 2020-September 2021) ranged from 4.5% to 59.9% (median 18.2%; IQR 14.7%-23.7%); 30-day RSMRs ranged from 12.9% to 56.2% (IQR 24.6%-30.6%). COVID-19 RSMRs were negatively correlated with star rating summary scores (in-hospital correlation coefficient -0.41, p<0.0001; 30 days -0.38, p<0.0001). Correlations with in-hospital RSMRs were strongest for patient experience (-0.39, p<0.0001) and timely and effective care (-0.30, p<0.0001) group scores; 30-day RSMRs were strongest for patient experience (-0.34, p<0.0001) and mortality (-0.33, p<0.0001) groups. Patients admitted to 1-star hospitals had higher odds of mortality (in-hospital OR 1.87, 95% CI 1.83 to 1.91; 30-day OR 1.46, 95% CI 1.43 to 1.48) compared with 5-star hospitals. If all hospitals performed like an average 5-star hospital, we estimate 38 000 fewer COVID-19-related deaths would have occurred between April 2020 and September 2021. CONCLUSIONS: Hospitals with better prepandemic quality may have care structures and processes that allowed for better care delivery and outcomes during the COVID-19 pandemic. Understanding the relationship between pre-COVID-19 hospital quality and COVID-19 outcomes will allow policy-makers and hospitals better prepare for future public health emergencies.

The CMS Strategy to Promote Equity in Quality and Value Programs.

This Viewpoint discusses the CMS approach to incentivize excellent care for underserved populations.

Assessing Hospital Quality Scores By Proportion Of Patients Dually Eligible For Medicare And Medicaid.

The Centers for Medicare and Medicaid Services has been reporting hospital star ratings since 2016. Some stakeholders have criticized the star ratings methodology for not adjusting for social risk factors. We examined the relationship between 2021 star rating scores and hospitals' proportion of Medicare patients dually eligible for Medicaid. We found that, on average, hospitals caring for a greater proportion of dually eligible patients had lower star ratings, but there was significant overlap in performance among hospitals when we stratified them by quintile of dually eligible patients. Hospitals in the highest quintile (those with the greatest proportion of dually eligible patients) had the best mean mortality scores (0.28) but the worst readmission (-0.44) and patient experience (-0.78) scores. We assigned star ratings after stratifying the readmission measure group by proportion of dually eligible patients and found that a total of 142 hospitals gained a star and 161 hospitals lost a star, of which 126 (89 percent) and 1 (<1 percent) were in the highest quintile, respectively. Adjusting public reporting tools such as star ratings for social risk factors is ultimately a policy decision, and views on the appropriateness of accounting for factors such as proportion of dually eligible patients are mixed, depending on the organization and stakeholder.

Balancing value and burden: the Centers for Medicare & Medicaid Services electronic Clinical Quality Measure (eCQM) Strategy Project.

Healthcare is undergoing a digital transformation, and the Centers for Medicare & Medicaid Services (CMS) aims to help providers navigate the clinical quality improvement landscape. In December 2017, CMS launched the Electronic Clinical Quality Measure (eCQM) Strategy Project. This article consists of 2 parts. The first part describes stakeholder outreach aimed to identify burdens and recommendations related to eCQM implementation and reporting. The second part describes how these burdens were addressed by CMS and how to engage in the digital transformation journey. Six themes emerged from the stakeholder feedback: Alignment, Value, Development Process, Implementation and Reporting Processes; EHR certification process; and Communication, Education, and Outreach. CMS and its partners addressed over 100 recommendations to improve the eCQM development, implementation, and reporting experience by creating implementation strategies. This included the development of new tools, such as the Measure Collaboration (MC) Workspace and ongoing testing of Fast Healthcare Interoperability Resources (FHIR)-based standards for quality measurement. CMS is sharing this summary of the eCQM Strategy Project to reflect CMS' interest in stakeholder engagement and burden reduction, increase awareness of available resources, and encourage continued engagement throughout this digital transformation in quality reporting.

Logic model framework for considering the inputs, processes and outcomes of a healthcare organisation-research partnership.

BACKGROUND: The published literature provides few insights regarding how to develop or consider the effects of knowledge co-production partnerships in the context of delivery system science. OBJECTIVE: To describe how a healthcare organisation-university-based research partnership was developed and used to design, develop and implement a practice-integrated decision support tool for patients with a physician recommendation for colorectal cancer screening. DESIGN: Instrumental case study. PARTICIPANTS: Data were ascertained from project documentation records and semistructured questionnaires sent to 16 healthcare organisation leaders and staff, research investigators and research staff members. RESULTS: Using a logic model framework, we organised the key inputs, processes and outcomes of a healthcare organisation-university-based research partnership. In addition to pragmatic researchers, partnership inputs included a healthcare organisation with a supportive practice environment and an executive-level project sponsor, a mid-level manager to serve as the organisational champion and continual access to organisational employees with relevant technical, policy and system/process knowledge. During programme design and implementation, partnership processes included using project team meetings, standing organisational meetings and one-on-one consultancies to provide platforms for shared learning and problem solving. Decision-making responsibility was shared between the healthcare organisation and research team. We discuss the short-term outcomes of the partnership, including how the partnership affected the current research team's knowledge and health system initiatives. CONCLUSION: Using a logic model framework, we have described how a healthcare organisation-university-based research team partnership was developed. Others interested in developing, implementing and evaluating knowledge co-production partnerships in the context of delivery system science projects can use the experiences to consider ways to develop, implement and evaluate similar co-production partnerships.

Randomised trial to evaluate the effectiveness and impact of offering postvisit decision support and assistance in obtaining physician-recommended colorectal cancer screening: the e-assist: Colon Health study-a protocol study.

INTRODUCTION: How to provide practice-integrated decision support to patients remains a challenge. We are testing the effectiveness of a practice-integrated programme targeting patients with a physician recommendation for colorectal cancer (CRC) screening. METHODS AND ANALYSIS: In partnership with healthcare teams, we developed 'e-assist: Colon Health', a patient-targeted, postvisit CRC screening decision support programme. The programme is housed within an electronic health record (EHR)-embedded patient portal. It leverages a physician screening recommendation as the cue to action and uses the portal to enrol and intervene with patients. Programme content complements patient-physician discussions by encouraging screening, addressing common questions and assisting with barrier removal. For evaluation, we are using a randomised trial in which patients are randomised to receive e-assist: Colon Health or one of two controls (usual care plus or usual care). Trial participants are average-risk, aged 50-75 years, due for CRC screening and received a physician order for stool testing or colonoscopy. Effectiveness will be evaluated by comparing screening use, as documented in the EHR, between trial enrollees in the e-assist: Colon Health and usual care plus (CRC screening information receipt) groups. Secondary outcomes include patient-perceived benefits of, barriers to and support for CRC screening and patient-reported CRC screening intent. The usual care group will be used to estimate screening use without intervention and programme impact at the population level. Differences in outcomes by study arm will be estimated with hierarchical logit models where patients are nested within physicians. ETHICS AND DISSEMINATION: All trial aspects have been approved by the Institutional Review Board of the health system in which the trial is being conducted. We will disseminate findings in diverse scientific venues and will target clinical and quality improvement audiences via other venues. The intervention could serve as a model for filling the gap between physician recommendations and patient action. TRIAL REGISTRATION NUMBER: NCT02798224; Pre-results.

Patients' adoption of and feature access within electronic patient portals.

OBJECTIVES: We describe online portal account adoption and feature access among subgroups of patients who traditionally have been disadvantaged or represent those with high healthcare needs. STUDY DESIGN: Retrospective cohort study of insured primary care patients 18 years and older (N = 20,282) receiving care from an integrated health system. METHODS: Using data from an electronic health record repository, portal adoption was defined by 1 or more online sessions. Feature access (ie, messaging, appointment management, visit/admission summaries, and medical record access and management) was defined by user-initiated "clicks." Multivariable regression methods were used to identify patient factors associated with portal adoption and feature access among adopters. RESULTS: One-third of patients were portal adopters, with African Americans (odds ratio [OR], 0.50; 95% CI, 0.46-0.56), Hispanics (OR, 0.63; 95% CI, 0.47-0.84), those 70 years and older (OR, 0.48; 95% CI, 0.44-0.52), and those preferring a language other than English (OR, 0.43; 95% CI, 0.31-0.59) less likely to be adopters. On the other hand, the likelihood of portal adoption increased with a higher number of comorbidities (OR, 1.04; 95% CI, 1.02-1.07). Among adopters, record access and management features (95.9%) were accessed most commonly. The majority of adopters also accessed appointment management (76.6%) and messaging (59.1%) features. Similar race and age disparities were found in feature access among adopters. CONCLUSIONS: The diversity of portal features accessed may bode well for the ability of portals to engage some patients, but without purposeful intervention, reliance on portals alone for patient engagement may exacerbate known social disparities-even among those with an activated portal account.

Associations Between Community Sociodemographics and Performance in HEDIS Quality Measures: A Study of 22 Medical Centers in a Primary Care Network.

Evaluation and payment for health plans and providers have been increasingly tied to their performance on quality metrics, which can be influenced by patient- and community-level sociodemographic factors. The aim of this study was to examine whether performance on Healthcare Effectiveness Data and Information Set (HEDIS) measures varied as a function of community sociodemographic characteristics at the primary care clinic level. Twenty-two primary care sites of a large multispecialty group practice were studied during the period of April 2013 to June 2016. Significant associations were found between sites' performance on selected HEDIS measures and their neighborhood sociodemographic characteristics. Outcome measures had stronger associations with sociodemographic factors than did process measures, with a range of significant correlation coefficients (absolute value, regardless of sign) from 0.44 to 0.72. Sociodemographic factors accounted for as much as 25% to 50% of the observed variance in measures such as HbA1c or blood pressure control.

Correlations Among Hospital Quality Measures: What "Hospital Compare" Data Tell Us.

A number of quality rating systems to rank health care providers have been developed over the years with the intention of helping consumers make informed health care purchasing decisions. Many use sets of individual quality measures to calculate a global rating. The utility of a global rating for consumer choice hinges on the relationships among included measures and the extent to which they jointly reflect an underlying dimension of quality. Publicly reported data on 4 quality domains-complication, mortality, readmission, and patient safety-from Centers for Medicare & Medicaid Services' Hospital Compare website were used to examine correlations among individual measures within each measure group (within-group correlations) and correlations between pairs of measures across different measure groups (between-group correlations). Modest within-group correlations were found in only 2 domains (mortality and readmission), and there were no meaningful between-group associations. These findings raise questions about whether consumers can reliably depend on global quality ratings to make informed decisions.

A practical method for surveillance of novel H1N1 influenza using automated hospital data.

We report a surveillance method for influenza that is based on automated hospital laboratory and pharmacy data. During the 2009 H1N1 influenza pandemic, this method was objective, easy to perform, and utilized readily available automated hospital data. This surveillance method produced results that correlated strongly with influenza-like illness surveillance data.