CMS Quality Measures Research Articles

249 Differences in Emergency Department Sepsis Care: Do Race, Sex, and Language Matter?

249 Differences in Emergency Department Sepsis Care: Do Race, Sex, and Language Matter?

Evaluating Pharmacist-Driven Interventions in a Primary Care Setting to Improve Proportion of Days Covered and Medication Adherence

Background: Medication nonadherence is the leading cause of poor health outcomes and increased risk of hospitalizations. Previous studies have shown that pharmacist interventions can help improve medication adherence and CMS quality measures. Objective: The purpose of this study was to examine the impact of clinical pharmacists’ interventions on medication adherence and PDC scores for ACEi/ARBs, statins, and noninsulin antidiabetic medications in the primary care setting. Methods: This observational study was conducted at four primary care clinics to evaluate PDC scores pre- and post-pharmacist interventions from April 2020 to December 2020. Eligible patients were Humana Part D beneficiaries with a baseline PDC score <85%. The primary outcome of this study was to evaluate the average change in final PDC scores, and 1-month change in PDC scores following a pharmacist intervention. Secondary outcomes were number and types of adherence barriers identified, interventions provided by the pharmacist, and barriers and interventions category (pharmacy, patient or physician-related). Results: A total of 89 barriers were identified and 208 interventions were completed. A statistically significant difference in the average change of final PDC score from baseline was seen among those on ACEi/ARBs (72.5 to 78.0, p = 0.004) and statins (73.3 to 76.6, p < 0.001). Similarly, a statistically significant change was observed from baseline to 1-month PDC among those on ACEi/ARBS (72.5 to 75.4, p = 0.001) and statins (73.3 to 74.9, p < 0.001). Conclusion: Pharmacists located in a primary care setting improved medication adherence and PDC score for patients on ACEIs/ARBs and statins.

Characterizing the Relationship Between Hospital Google Star Ratings, Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Scores, and Quality.

Google searches for hospitals typically yield a Google star rating (GSR). These ratings are an important source of information for consumers. The degree to which GSRs are associated with traditional quality measures has not been evaluated recently. We sought to characterize the relationship between a hospital’s GSR, its Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores, and Centers for Medicare and Medicaid Services (CMS) quality measures. We found a moderate association between a hospital’s GSR and its HCAHPS score. The relationship between a hospital’s GSR and CMS quality measures was statistically significant, but the magnitude was quite low. Our findings suggest that consumers should not use GSRs as a hospital quality proxy.

Measuring disparities in quality of oncology care across oncology practices.

6533 Background: Equity is now recognized as an essential aspect of health care quality. Racial inequities in clinical performance diminish overall system performance. We assessed the feasibility and reliability of practice-level measures of racial disparities in chemotherapy-associated emergency department (ED) visits and hospitalizations. Methods: Using fee-for-service Medicare data, we identified 1,196,970 Black or White fee-for-service Medicare beneficiaries with cancer receiving chemotherapy in 2016-2019, who were attributed to 5511 oncology practices that treated at least 1 Black and 1 White beneficiary (96.4% of all beneficiaries). We studied two CMS quality measures: chemotherapy associated ED visits and chemotherapy associated hospitalizations. For each outcome, we estimated multi-level models with separate practice-level random intercepts for Black and White patients to quantify practice-level Black-White disparities in adjusted rates of these measures and assess the associations of these rates with the proportion of Black patients in the practice. Results: Overall, 108,177 Black and 966,381 White beneficiaries with cancer were treated at 1321 practices with reliable estimates (reliability ≥70%) of Black-White differences in rates of chemotherapy-associated ED visits; 101,411 Black and 915,895 White beneficiaries were treated at 1,012 practices with reliable estimates of chemotherapy-associated hospitalizations. These practices treated 80% or more of all Black and White beneficiaries; 10% of these practices treated 75% of Black beneficiaries. The median adjusted Black-White rate difference across practices was +8.9% [interquartile interval (IQI) +5.0%, +12.8%; 5th, 95th percentile -1.8 to +19.2%] for chemotherapy associated ED visits and +4.4% [IQI +1.3%, +7.7%; 5th, 95th percentile -3.5% to +13.5%] for chemotherapy associated hospitalizations. Chemotherapy-associated ED visit rates were 3.2 percentage points higher for Black vs White patients (P &lt;.001) at the practice with the mean % of Black patients, but the difference was smaller in practices with more Black patients (0.4 percentage points less for each 10% increase in Black share, P &lt;.001). Chemotherapy-associated hospitalization rates were 0.6 percentage points lower for Black vs White patients (P =.01) but did not vary by practice racial composition. Conclusions: Using data from more than 1000 practices over 4 years, we calculated reliable estimates of practice-level racial disparities in chemotherapy-associated ED visits and hospitalizations. Practice-level performance for these quality measures was generally lower for Black versus White beneficiaries. Measuring and providing feedback on practice-level Black-White disparities in oncology performance measures may be one effective tool for advancing racial equity in care quality for cancer patients receiving chemotherapy.

Identification of patients at high risk for preventable emergency department visits and inpatient admissions after starting chemotherapy: Machine learning applied to comprehensive electronic health record data.

1511 Background: Acute care use is one of the largest drivers of cancer care costs. OP-35: Admissions and Emergency Department Visits for Patients Receiving Outpatient Chemotherapy is a CMS quality measure that will affect reimbursement based on unplanned inpatient admissions (IP) and emergency department (ED) visits. Targeted measures can reduce preventable acute care use but identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data available in the Electronic Health Record (EHR). We hypothesized dense, structured EHR data could be used to train machine learning algorithms to predict risk of preventable ED and IP visits. Methods: Patients treated at Stanford Health Care and affiliated community care sites between 2013 and 2015 who met inclusion criteria for OP-35 were selected from our EHR. Preventable ED or IP visits were identified using OP-35 criteria. Demographic, diagnosis, procedure, medication, laboratory, vital sign, and healthcare utilization data generated prior to chemotherapy treatment were obtained. A random split of 80% of the cohort was used to train a logistic regression with least absolute shrinkage and selection operator regularization (LASSO) model to predict risk for acute care events within the first 180 days of chemotherapy. The remaining 20% were used to measure model performance by the Area Under the Receiver Operator Curve (AUROC). Results: 8,439 patients were included, of whom 35% had one or more preventable event within 180 days of starting chemotherapy. Our LASSO model classified patients at risk for preventable ED or IP visits with an AUROC of 0.783 (95% CI: 0.761-0.806). Model performance was better for identifying risk for IP visits than ED visits. LASSO selected 125 of 760 possible features to use when classifying patients. These included prior acute care visits, cancer stage, race, laboratory values, and a diagnosis of depression. Key features for the model are shown in the table. Conclusions: Machine learning models trained on a large number of routinely collected clinical variables can identify patients at risk for acute care events with promising accuracy. These models have the potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted preventative interventions. Future work will include prospective and external validation in other healthcare systems.[Table: see text]

Trends in Publicly Reported Quality Measures of Hospital Imaging Efficiency, 2011-2018.

OBJECTIVE. In 2011, the Centers for Medicare & Medicaid Services (CMS) initiated public reporting of outpatient imaging efficiency measures to reduce potentially inappropriate imaging and unnecessary exposure to ionizing radiation performed in hospital outpatient departments. Three CMS quality measures were designed to reduce duplicative CT in the Medicare population: OP-10, which CMS lists as "Abdomen Computed Tomography-Use of Contrast Material"; OP-11, which CMS lists as "Thorax CT-Use of Contrast Material"; and OP-14, which CMS lists as "Simultaneous Use of Brain CT and Sinus CT." We describe trends in hospital performance on these national hospital outpatient imaging efficiency measures since the inception of their public reporting. MATERIALS AND METHODS. This observational analysis used standard Medicare fee-for-service administrative claims to calculate hospital-specific scores for OP-10, OP-11, and OP-14. Consistent with CMS specifications, each measure was calculated as a percentage with appropriate exclusions and minimum case count requirements to ensure measure score validity and reliability. We report national performance as well as distributions of hospital performance scores for each annual public reporting period. Trend analyses were performed to examine changes in annual mean performance over time. Secondary analyses assessed trends and hospital performance by location (rural vs urban) and hospital characteristics. RESULTS. Between 2011 and 2018, the national mean rate of duplicate imaging declined for all three measures (OP-10, 18.9% vs 7.7%; OP-11, 5.6% vs 2.0%; OP-14, 2.5% vs 1.0%). For OP-10 and OP-11, most outlier hospitals were rural, small, and government-owned. For OP-10, rural facilities accounted for 32.2% of all facilities but 46.0% of outliers by the end of the study period. Similarly, for OP-11, rural facilities accounted for 30.1% of all facilities but 47.0% of outliers by the end of the study period. In general, the proportion of outliers located in rural areas decreased over time. CONCLUSION. National performance on CMS quality measures of duplicative CT has improved over time, with reduced variation observed between hospitals since the inception of public reporting. These successes support recent CMS policy initiatives to retire duplicative imaging measures from public reporting. Future work should seek to identify opportunities to use national public reporting initiatives to yield similar improvements across broader indications and settings.

Nursing Home Responses to Performance-based Accountability: Results of a National Survey.

The Centers for Medicare & Medicaid Services (CMS) Nursing Home Quality Initiative aims to improve quality through performance measurement. We describe quality improvement (QI) changes that skilled nursing facilities (SNFs) reported making in response to CMS performance measurements and whether reported QI changes were associated with better performance on CMS performance measures. Nationally representative survey. A total of 15,475 SNFs that reported quality performance on Nursing Home Compare in 2016. A total of 1,182 SNFs (58% of random sample of 2,045 SNFs). Adoption of 22 possible QI changes, grouped into seven categories (organizational culture, health information technology, care process redesign, provider incentives, changes to staffing responsibilities, performance monitoring, and measure-specific QI initiatives and technical assistance); performance on the CMS Nursing Home Compare Five-Star Quality Rating System's quality measure rating. SNFs reported making an average of 13 QI changes (interquartile range = 11-16 changes). SNFs mostly commonly reported becoming a learning organization (87%) and providing training to staff on QI strategies (87%). After controlling for patient and facility characteristics, larger SNFs were more likely to obtain assistance on measure reporting from QI organizations and use provider champions than smaller SNFs by 14 and 11 percentage points, respectively. Rural SNFs and SNFs with higher proportions of disabled, black, or Hispanic residents adopted QI changes at similar rates as other SNFs. Of the 22 QI changes, 20 were considered at least somewhat helpful by more than 80% of adopting SNFs. Implementation of all 22 QI changes (vs no changes) was associated with a .48-star higher quality measure rating (95% confidence interval = .003-.98 stars; P = .05). In response to CMS measurement programs, SNFs reported making substantial QI investments that were associated with better performance on CMS quality measures. To guide future SNF investments in QI, work is needed to identify the QI changes that yield the greatest performance improvements.

Abstract WP310: Timing of Comfort Care Decisions in Ischemic Stroke Patients: Implications for Publicly Reported Outcomes

Introduction: Risk-adjusted stroke mortality is a publicly-reported CMS quality measure. Patients (pts) admitted with stroke who are discharged to hospice prior to the first midnight are excluded in reported metrics. It is difficult to prognosticate outcomes early in ischemic stroke; additionally, the sudden nature of the disease makes it difficult for families to make rapid decisions. We hypothesized that the decision to proceed to comfort care in ischemic stroke pts takes longer than 24 hours. Methods: This was an observational study of all ischemic stroke pts seen at Intermountain Medical Center in 2016 (n=581) who were discharged to hospice (n=27) or died in hospital on comfort care (n=27). A Wilcoxon sign rank test was used to compare time to decision regarding comfort care to the CMS standard of 24 hours. Differences in the time to decision or death for the pt characteristics were also compared using Wilcoxon-Mann-Whitney test. Results: The average age was 80 years old ± 13 (range 44 - 98), 59% were female, all were Caucasian Non-Hispanic, and 65% reported a religious affiliation. NIH stroke score was reported in 42 subjects with an average of 15.9±7.9 (range 2-31). The mean time from admission to initiation of comfort care orders was 3.12 days (±3.6) with a median of 1.8 days and a range from 12.5 hours to 14.1 days. The time was significantly greater than 24 hours (p&lt;0.0001) with only a third (n=18) having the time less than 24 hours and with only a fifth (n=11) having the time before the first midnight. The table below contains the differences in the times based on pt characteristics and none of these were significantly associated with time differences. Conclusions: The decision to transition to comfort care or hospice often takes greater than 24 hours. If the stroke mortality metric is meant to exclude hospice pts or those that prefer comfort measures only, the time window for initiation of those orders should be extended beyond the first midnight.

The Fault in Our Stars: Does the Five-Star System Fall Short?

The Fault in Our Stars: Does the Five-Star System Fall Short?

Development of a CMS Quality Measure for Outpatient Colonoscopy

In the interest of instituting a quality measure for outpatient colonoscopy facilities, the Centers for Medicare and Medicaid Services (CMS) funded investigators to develop and validate a model to predict the rate of unplanned hospital visits within 7 days of colonoscopy. Utilizing a 20% sample of 2010 Medicare data, researchers identified patient-specific risk factors for hospital visits that were unrelated to quality. They included non–high-risk outpatient diagnostic and therapeutic colonoscopies, though they did not include any adjustment for known predictors of polypectomy complications. The model was validated using two different samples from 2010 and 2011. The most common diagnoses tied to unplanned hospitalizations were bleeding, abdominal pain, and …

Advancing Excellence Sets New Goals

Advancing Excellence Sets New Goals

EPs Weigh in on CMS Quality Measures

EPs Weigh in on CMS Quality Measures