Quality Star Ratings Research Articles

An open-source application for obtaining retrospective and prospective insights into overall hospital quality star ratings

Overall Hospital Quality Star Ratings (overall star ratings) are designed to assist healthcare consumers by summarizing dozens of hospital quality measures. These ratings are also used by hospitals to direct quality improvements and are often used in healthcare research. However, no analytical tools have been developed to provide insights into the data, measures, and scores of the overall star rating system. To this end, we developed a novel open-source application to provide retrospective insights, prospective estimates, and research-ready data. Users can 1) examine changes in hospital performance from 2021 onward, 2) recalculate overall star ratings based on hypothetical improvements, 3) download data for all hospitals included in the overall star rating system since 2021, and 4) obtain prospective estimates based on the overall star rating methodology and its data source (Care Compare). We demonstrate 99.6% accuracy when estimating overall star ratings six months prior to public release. Estimates of whether hospitals will retain their star rating are up to 90% accurate a year before public release. We discuss the use of our application in healthcare research and the potential for similar tools to be developed for other hospital rating and ranking systems.

Inclusion of Veterans Health Administration hospitals in Centers for Medicare & Medicaid Services Overall Hospital Quality Star Ratings.

The Centers for Medicare & Medicaid Services (CMS) Overall Hospital Quality Star Rating, established in 2016, is a summary of publicly available quality information for acute care hospitals. In July 2023, Veterans Health Administration (VHA) hospitals became eligible to receive a CMS Overall Hospital Quality Star Rating for the first time. Our objective was to compare performance in quality ratings among VHA and non-VHA hospitals. We used the hospital quality measure scores posted to Care Compare on Medicare.gov as of January 2023 as our primary data set. We conducted a pair of analyses to characterize the performance of VHA hospitals compared to non-VHA hospitals: an overall analysis including all rated hospitals, and a matched analysis in which only a single nearby hospital was included for each VHA hospital. Of the 4518 non-VHA hospitals, 2962 (65.6%) received a Star Rating, compared to 114 (84%) of 136 VHA hospitals. VHA hospitals tended to receive higher ratings overall (one-star: 8%; two-star: 11%; three-star: 14%; four-star: 35%; five-star: 32%) than non-VHA (one-star: 8%; two-star: 22%; three-star: 29%; four-star: 26%; five-star: 15%). A similar pattern was observed in the matched analysis. VHA hospitals tended to perform better on the Overall Star Rating compared to non-VHA hospitals, as evidenced by being more likely to receive a four- or five-star rating. The eligibility of VHA hospitals to receive an Overall Star Rating signifies an important addition to the program that will allow Veterans to make more informed healthcare decisions.

Leadership Matters: Investigating the Association Between Nursing Home Administrator Turnover and Quality.

Existing research has associated nursing home (NH) staff turnover with poor performance, yet the impact of nursing home administrator (NHA) turnover remains relatively understudied. This study aimed to explore the relationship between NHA turnover and NH quality, and to determine if this relationship was mediated by registered nurse (RN) turnover. Utilizing data from multiple secondary sources, including the Care Compare: Skilled Nursing Facility Quality Reporting Program (SNF QRP) and LTCFocus.org, this study employed a longitudinal analysis covering the period 2021-2022 (n = 19,645). The dependent variable was quality star ratings from SNF QRP, whereas the independent variable was NHA turnover, reflecting the number of administrators who left each facility annually. We used Baron and Kenny's mediation testing method, incorporating 2-way fixed effects (state and year level) with appropriate organizational and market level control variables. The results affirmed that NHA turnover negatively affected quality, decreasing the likelihood of a higher star rating by 14% with one departure and 25% with multiple departures (p = .001). Additionally, NHA turnover correlated with an increase in RN turnover by 7% and 11%, respectively, for one and multiple departures (p = .001). RN turnover fully mediated the impact of NHA turnover on quality, nullifying the direct effect of NHA turnover. The results of this study highlight the synergistic relationship between administrators and caregivers in NHs. To enhance NHA retention, NHs should improve compensation and benefits. Nonetheless, broader governmental support and interventions might be necessary to sustain these improvements.

Quality of care and emergency department throughput during the COVID-19 pandemic in a community health system Pandemic in a Community Health System

Objective: This retrospective study explores the strategic plan formulated by AHMC Health System in California, USA, to sustain and improve quality of care and emergency department (ED) efficiency during the COVID-19 pandemic. It also analyzes the plan’s outcomes.Background: The COVID-19 pandemic has posed challenges for both individuals and healthcare industries alike, impacting decision-making and access to care. AHMC faced staff and resource shortages, patient reluctance, and difficulties adapting to rapidly evolving public health guidelines. These challenges highlighted the critical need for effective plans to maintain or improve healthcare quality and ED performance.Methods: AHMC adopted a comprehensive three-layer strategic plan in 2020. The first layer, “Pandemic Response,” focused on leadership, staff training and education, infection control, new treatments, and employee vaccination rates. The second layer, “ED Throughput,” set objectives for metrics such as door-to-doctor (door-to-doc) time, ancillary turnaround time (TAT), ED length of stay (LOS), and the left-without-being-seen (LWBS) rates. Progress was monitored through monthly improvement meetings. The third layer, “Quality Excellence,” tracked improvements in COVID-adapted objectives on quality initiatives, based on CMS Quality Star Ratings, Leapfrog Hospital Safety Grades, and Yelp review scores.Results: By 2023, the three-layer strategic plan had led to many improvements in the quality of care and ED efficiency. AHMC identified 22,287 positive COVID-19 cases, expanded its ventilator inventory by 50%, and enhanced patient outcomes by applying updated treatments. Additionally, AHMC saw a 3% reduction in ED wait times and sustained its overall patient satisfaction rates, CMS Quality Star Rating, and Leapfrog Hospital Safety Grade scores.Conclusions: AHMC’s three-layer strategic plan showed effectiveness in maintaining quality of care and ED efficiency during the COVID-19 pandemic. By focusing on “Pandemic Response,” “ED Throughput,” and “Quality Excellence,” AHMC was able to adapt to the rapidly evolving public health guidelines, expand its capacity to treat COVID-19 patients and sustain its overall patient safety, satisfaction, and quality ratings. The implementation of this plan highlights the importance of proactive and comprehensive strategies in managing healthcare crises.

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

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. 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. 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. Peer grouping vs no peer grouping. 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. 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. 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.

Do Quality Measures or Hospital Characteristics Predict Readmission Penalties for Hip and Knee Arthroplasty?

BackgroundQuality rating systems exist to grade the value of care provided by hospitals, but the extent to which these rating systems correlate with patient outcomes is unclear. The association of quality rating systems and hospital characteristics with excess readmission penalties for total hip arthroplasty (THA) and total knee arthroplasty (TKA) was studied. MethodsThe fiscal year 2022 Inpatient Prospective Payment System final rule was used to identify 2,286 hospitals subject to the Hospital Readmissions Reduction Program. Overall, 6 hospital quality rating systems and 5 hospital characteristics were obtained. These factors were analyzed to determine the effect on hospital penalties for THA and TKA excess readmissions. ResultsHospitals that achieved a higher Medicare Overall Hospital Quality Star Rating demonstrated a significantly lower likelihood of receiving THA and TKA readmission penalties (Cramer’s V = 0.236 and Rp = −0.233; P < .001 for both). Hospitals ranked among the US News & World Report’s top 50 best hospitals for orthopaedics were significantly less likely to be penalized (V = 0.042; P = .043). The remaining 4 quality rating systems were not associated with readmission penalties. Penalization was more likely for hospitals with fewer THA and TKA discharges (Rp = −0.142; P < .001), medium-sized institutions (100 to 499 beds; V = 0.075; P = .002), teaching hospitals (V = 0.049; P = .019), and safety net hospitals (V = 0.043; P = .039). Penalization was less likely for West and Midwest hospitals (V = 0.112; P < .001). ConclusionsA higher Overall Hospital Quality Star Rating and recognition among the US News & World Report’s top 50 orthopaedic hospitals were associated with a reduced likelihood of THA and TKA readmission penalties. The other 4 widely accepted quality rating systems did not correlate with readmission penalties. Teaching and safety net hospitals may be biased toward higher readmission rates.

THE IMPACT OF CONTRACT NURSE UTILIZATION ON NURSING HOME QUALITY

Abstract Nurse staffing intensity and skill mix have a significant impact on NH quality of care. For instance, higher staffing levels, particularly for registered nurses (RNs), have been associated with better resident outcomes. NHs have increasingly relied on contract nurse staffing to address pressing and persistent nursing shortages. Research suggests that contract nurses may be unfamiliar with institutional policies and procedures or organizational culture. They may require increased supervision, be less participatory in teamwork, and may increase the workload for the permanent staff. The objective of this study is to examine the association between contract nurse utilization and quality of care at US nursing homes.We used secondary data from PBJ nurse staffing, Nursing Home Compare, Healthcare Cost Report Information System (HCRIS) Dataset, and the American Community Survey (ACS) for the period 2017-2021. The dependent variable was CMS quality star rating, and the independent variables were contract nurse to total nurse proportions for each of RNs, LPNs, and CNAs. We employed multivariate random effects ordered logistic regression to test the hypothesis. We found that across all our three models (RNs, LPNs, CNAs), quality star ratings were negatively associated with contract nurse utilization. A 10% increase in contract nurse proportion was associated with a 10% decrease in the odds of reporting a higher quality star rating (OR=0.99, p&amp;lt;0.001). Our results suggest the increased utilization of contract nurse staffing may have a negative impact on US NH quality---an important issue that has agitated policymakers for nearly three decades.

HOW IS THE NURSING HOME QUALITY OF CARE ALIGNED WITH THE QUALITY OF LIFE?

Abstract The CMS Five-Star Quality Rating System does not currently incorporate nursing home (NH) consumers’ opinions. This study aims to investigate the association between star ratings and resident/family satisfaction. A unique, facility-level dataset of Ohio NHs that contains star ratings, consumer satisfaction scores, and NH characteristics was constructed (N=701). The key dependent variables were the overall, health inspection, and quality star ratings, categorized into two groups: 1-3 stars and 4-5 stars. Key independent variables were overall resident and family satisfaction scores, which ranged from 0 (low) to 100 (high). Bivariate and logistic regression analyses were conducted. Bivariate results showed consumer satisfaction and star ratings were correlated, but not perfectly related. Regression results found an increase in resident satisfaction was associated with an increased probability of being a 4-5 star rated facility across all star ratings (p&amp;lt;.05). An increase in family satisfaction was also associated with an increased probability of being a 4-5 star rated facility for the overall and health inspection ratings (p&amp;lt;.01). Early studies found little relationship between star ratings and consumer satisfaction. Due to improvements made to the star ratings, this study found consumer satisfaction is associated, though imperfectly, with star ratings, suggesting that a consumer-driven measure of quality still needs to be publicly reported.

Impact of Epic Smartlist and Lumens Software in Improving OP-29 Compliance at a Tertiary Health Care Network.

Background OP-29 is a Centers for Medicaid and Medicare Services (CMS) measure to ensure that endoscopists recommend appropriate follow-up intervals after normal colonoscopy in average risk patients. Failure to report OP-29 compliance can adversely affect hospital quality star rating as well as reimbursement for health care. The aim of our quality improvement project was to improve OP-29 compliance to the top decile over threeyears. Methodology Our sample included patients between 50-75 years of age who received average risk screening colonoscopies with normal findings. We provided intensive education to endoscopists about the importance of OP-29 compliance, developed an Epic Smartlist that directs our endoscopists to list an appropriate reason for colonoscopy intervals other than 10 years, and monitored OP-29 compliance monthly. We became the first health network in the United States to implement the Lumens endoscopy report writing software (Epic Systems Corporation, Verona, USA) and added the OP-29-related Epic Smartlist to the Lumens colonoscopy note template. All statistical analyses were conducted in SPSS version 26 (IBM Corp., Armonk, USA) to compute the means and frequencies of outcomes. Results Our sample included 2,171 patients with a mean age of 60.5 years of whom the majority were female (57.2%) and Caucasians (90%). Our OP-29 score increased from 87.47% to 100% over the course of three years, and this steady improvement was seen broadly across our network. We compared our network score averages to our state and national averagesand consistently demonstrated higher compliance rates while reaching the top decile by 2020. Conclusion We believe our improved OP-29 compliance has reduced colonoscopy overutilization, improved health care quality, and reduced health care costs for our patients and health network. To our knowledge, this is the first reported project towards improving OP-29 compliance utilizing the Epic Lumens software. Epic Lumens (Epic Systems Corporation, Verona, USA)added this Smartlist as quick buttons in the standard colonoscopy procedure note templates they built for other organizations to improve health care quality and cost nationally.

MEASURING QUALITY: EVALUATING HOSPITAL VALUE-BASED PURCHASING AND OVERALL HOSPITAL RATINGS

The Centers for Medicare &amp; Medicaid Services designed the Hospital Value Based Purchasing (HVBP) program and the Overall Hospital Quality Star Ratings program to improve healthcare quality across the US. Controversy remains concerning whether these initiatives truly encourage institutions to continuously work to improve the quality of care provided. To address some of these knowledge gaps, we applied data from the Modern Healthcare Hospital Penalties and Rewards dataset to identify potential relationships. We found the mean HVBP adjustments differed significantly between Ohio and Washington with a mean difference of 0.10% and between Pennsylvania and Washington 0.08%; and the mean hospital overall ratings differed significantly between Georgia and Ohio with mean a difference of 0.58 and between Ohio and Washington 0.65. Therefore, by evaluating the potential relationships, we can interpret the impact of geographic region on Medicare rewards and penalties, healthcare quality, and thus the impact on patient care.

A QUALITY INITIATIVE TO INCREASE HOSPICE REFERRALS FOR STARS PATIENTS DURING THE SECOND COVID SURGE

Abstract Background CMS uses the Overall Hospital Quality Star Rating program to stratify hospitals based on specific quality criteria (e.g., 30-day readmissions of older adults with pneumonia). “STARS patients” experience more readmissions, longer LOS and often have more complex discharge plans. During the second surge of COVID we implemented a program to increase hospice referrals through early identification and implementation of goals of care (GOC) conversations. Methods Electronic Medical Records reviewed for STARS patients from pre- (1/2019-7/2020) and post (3/2021–2/2022) program implementation. Location: 2 community-based hospitals. Data collected: demographics, hospital outcomes, discharge disposition. Data compared with Student’s t tests and Chi square. Results 459 patients, 177 pre-program and 282 post-program were included. Groups were similar in age (83.0 vs 83.6), LACE score (13.0 vs 12.8), principal diagnoses (PNA: 41.5% vs 46.0%, HF/COPD/AMI: 58.5% vs 54.0%), and mortality (3.5 vs. 4.0%). LOS increased 4.9 days vs. 6.1 days (p &amp;lt; 0.01), readmission rates unchanged: 12.6% vs 13.0% (p=0.90). GOC conversations increased, 48.6% to 75.0% (p &amp;lt; 0.01), DNR from 24% to 44% (p &amp;lt; 0.01), and comfort measures from 0.5% to 5% (p &amp;lt; 0.01). Hospice referrals increased from 0.5% to 11.2% (p &amp;lt; 0.01). Discussion: Early identification of STARS patients increased GOC conversations, DNR, comfort measures and hospice referral. Patients across time periods were similar in age, LACE and admitting diagnoses. LOS increased by a day, likely reflecting time needed to arrange discharge disposition. Increased hospice at end-of-life is associated with better quality care and patient/family satisfaction. This program may be adapted to larger, academic medical centers within the health system.

Determining the value proposition of surgical care in CMS star rated hospitals

BackgroundCMS Hospital Quality Star ratings reflect the quality of care given to patients. It is hypothesized that increased Star-rating is associated with higher cost and that the value proposition is diminished. MethodsThis study used the Florida AHCA inpatient dataset, CY2019. Partial colectomy was selected as a representative inpatient surgical procedure. Analysis was performed on this data to compare high and low Star-rated hospitals. ResultsTotal costs were equivalent among all Star levels on initial analysis. In a propensity matched comparison with 1 Star, 5 Star hospitals had significantly lower length-of-stay and ICU, anesthesia, radiology and lab costs, and conversely, had higher total (+2%), operating room and med-surg supply costs. ConclusionsThese results demonstrate that total colectomy costs are functionally equivalent among the CMS 1- and 5- Star categories. The results indicate that higher CMS Star ratings fulfill the value proposition and indeed offer higher quality without significantly increased cost.

Variation in Postoperative Outcomes Across Centers for Medicare and Medicaid Services Hospital Star Ratings

INTRODUCTION: To provide a single public measure of hospital quality, the Centers for Medicare and Medicaid Services (CMS) introduced the Overall Hospital Quality Star Rating program. However, whether CMS hospital star ratings are associated with postoperative outcomes and a reliable measure of hospital quality for the public remains unknown. METHODS: We performed a cross-sectional review of 1,953,057 Medicare beneficiaries undergoing 1 of 5 common surgical procedures (colon resection, coronary artery bypass, cholecystectomy, appendectomy, and incisional hernia repair) between 2014 and 2018. Hospitals were stratified by their CMS Star Rating. We then calculated risk- and reliability adjusted hospital mortality rate. Finally, we analyzed the variation in mortality within and across CMS Star Ratings. RESULTS: Overall, risk- and reliability adjusted hospital mortality was 5.7% (95% CI: 5.7 to 5.8) and ranged from 2.4% to 16.2%. Hospital mortality decreased in a stepwise fashion from 6.5% (95% CI: 6.3 to 6.6) at 1-star hospitals to 6.1% (95% CI: 6.0 to 6.2) at 2-star hospitals, 5.8% (95% CI: 5.8 to 5.9) at 3-star hospitals, 5.5% (95% CI: 5.5 to 5.6) at 4-star hospitals, and 5.1% (95% CI: 5.0 to 5.2) at 5-star hospitals (odds ratio [OR]: 0.53, 95% CI 0.49 to 0.57). There was wide variation in mortality withing CMS star ratings (Figure). Specifically, 1-star hospital mortality varied by 3.4×, 2-star hospitals varied 5.8×, 3-star hospitals varied 4.2×, 4-star hospitals varied 4.0×, and 5-star hospitals varied 3.8×.FigureCONCLUSION: Although CMS hospital star rating was associated with postoperative mortality, there was wide variation in mortality within each star rating group. These findings highlight the limitations of CMS’s star rating system and should be a call for continued improvement of publicly reports hospital grade measures.

Use of hospitals in the New York City Metropolitan Region, by race: how separate? How equal in resources and quality?

BackgroundAlthough racial and ethnic minorities disproportionately use some hospitals, hospital-based racial and ethnic composition relative to geographic region and its association with quality indicators has not been systematically analyzed.MethodsWe used four race and ethnicity categories: non-Hispanic white (NHW), non-Hispanic black (NHB), Hispanic, and Asian/Pacific Islander/Alaskan Native/American Indian (API/AIAN), as well as a combined non-NHW category, from the 2010 (latest year publicly available) Medicare Institutional Provider & Beneficiary Summary public use file for 84 hospitals in the New York City region. We assessed the relative distribution of race and ethnicity across hospitals grouped at different geographic levels (region, county, hospital referral region [HRR], or hospital service areas [HSA]) using the dissimilarity index. Hospital characteristics included quality star ratings, essential professional services and diagnostic/treatment equipment, bed size, total expenses, and patients with dual Medicare and Medicaid enrollment. We assessed Spearman’s rank correlation between hospital-based racial and ethnic composition and quality/structural measures.ResultsDissimilarity Index decreases from region (range 30.3–40.1%) to county (range 13.7–23.5%), HRR (range 10.5–27.5%), and HSA (range 12.0–16.9%) levels. Hospitals with larger non-NHW patients tended to have lower hospital ratings and higher proportions of dually-enrolled patients. They were also more likely to be safety net hospitals and non-federal governmental hospitals.ConclusionsIn the NYC metropolitan region, there is considerable hospital-based racial and ethnic segregation of Medicare patients among non-NHW populations, extending previous research limited to NHB. Availability of data on racial and ethnic composition of hospitals should be made publicly available for researchers and consumers.

Nursing Home Compare Star Rating and Daily Direct-Care Nurse Staffing During Hurricane Irma.

Nursing homes (NHs) are affected by major hurricanes and other natural disasters. To mitigate adverse effects of a major hurricane, NHs often increase their direct-care nurse staffing levels to meet the needs of their residents. However, the quality rating of the NH may affect the resources available to obtain and retain staff. This data brief provides estimates of direct-care nurse staffing levels by quality star rating during Hurricane Irma. Retrospective cohort study from September 3, 2017, to September 10,2017. 570 Florida NHs that sheltered in place during Hurricane Irma. We stratified NHs by their NH Compare overall quality star rating and then measured change in direct-care nurse staffing levels for registered nurses, licensed practical nurses, and certified nursing assistants. We found that the NH Compare overall star rating was positively associated with a greater staffing level response during Hurricane Irma among registered nurses, licensed practical nurses, and certified nursing assistants. This change was largest for 5-star facilities and smallest for 1-star facilities. Higher-quality NHs may be more responsive and have the resources to be more responsive, to increased needs during a natural disaster. Our findings may serve as a platform for ongoing discussion on the role of the federal, state, and local governments in ensuring minimum staffing standards during natural disasters.

Internal Operational Metrics and Center for Medicare and Medicaid Services Hospital Compare Quality Ratings.

The Center for Medicare and Medicaid Services (CMS) has made several refinements to their model for calculating hospital quality star ratings (Hospital Compare) amidst criticism and evidence of bias against some institutions. We argue that the CMS model does align with important internal quality metrics and encourage a measured approach to redesign, potentially using categorizations or tiers, rather than a complete abandonment of the ratings system. We find that institutional characteristics (available resources, average severity of illness, and academic affiliation) are associated with internal quality metrics related to patient flow. Furthermore, regression results from the original and revised CMS star rating methodologies suggest that patient flow metrics (discharges before noon [p < .01] and weekend discharges [p < .001]) have a positive relationship with the Hospital Compare rating. Hospitals with better patient flow, as measured by higher levels of discharges before noon and weekend discharges, are associated with higher CMS quality ratings. These findings suggest that CMS star ratings do reflect key aspects of operational performance, specifically efforts to improve patient flow, but the ranking system should consider hospital characteristics that influence internal operations as we move toward a system capable of quality and price transparency for consumers.

Marketplace Health Insurance Ratings: Most Potential Enrollees Have Access To Plans Of Medium Or High Quality.

The Affordable Care Act (ACA) Marketplace plays a critical role in providing affordable health insurance for the nongroup market, yet the accessibility of plans from insurers with high quality ratings has not been investigated. Our analysis of recently released insurer quality star ratings for plan year 2020 found substantial variation in access to high rated plans in the federally facilitated ACA Marketplace. In most participating counties (1,390 of 2,265, or 61.4percent), the highest-rated ACA Marketplace insurer had a three-star rating. Fewer than one-third of counties (703, or 31.0percent) had access to four- or five-star-rated insurers. Fewer than 10percent (172, or 7.6percent) had access to only one- or two-star-rated insurers. In plan-based analyses, each one-point increase in star rating was associated with a $28 increase in the average monthly plan premium. Counties with the highest proportion of residents obtaining individual coverage through the ACA Marketplace and counties with more insurers were the most likely to have access to plans from high-rated insurers. We found no systematic racial or ethnic disparities in access to plans from high-rated insurers. Policy makers should continue to monitor the quality of available health plans.

Measuring implementation fidelity in a cluster-randomized pragmatic trial: development and use of a quantitative multi-component approach

BackgroundIn pragmatic trials, on-site partners, rather than researchers, lead intervention delivery, which may result in implementation variation. There is a need to quantitatively measure this variation. Applying the Framework for Implementation Fidelity (FIF), we develop an approach for measuring variability in site-level implementation fidelity. This approach is then applied to measure site-level fidelity in a cluster-randomized pragmatic trial of Music & MemorySM (M&M), a personalized music intervention targeting agitated behaviors in residents living with dementia, in US nursing homes (NHs).MethodsIntervention NHs (N = 27) implemented M&M using a standardized manual, utilizing provided staff trainings and iPods for participating residents. Quantitative implementation data, including iPod metadata (i.e., song title, duration, number of plays), were collected during baseline, 4-month, and 8-month site visits. Three researchers developed four FIF adherence dimension scores. For Details of Content, we independently reviewed the implementation manual and reached consensus on six core M&M components. Coverage was the total number of residents exposed to the music at each NH. Frequency was the percent of participating residents in each NH exposed to M&M at least weekly. Duration was the median minutes of music received per resident day exposed. Data elements were scaled and summed to generate dimension-level NH scores, which were then summed to create a Composite adherence score. NHs were grouped by tercile (low-, medium-, high-fidelity).ResultsThe 27 NHs differed in size, resident composition, and publicly reported quality rating. The Composite score demonstrated significant variation across NHs, ranging from 4.0 to 12.0 [8.0, standard deviation (SD) 2.1]. Scaled dimension scores were significantly correlated with the Composite score. However, dimension scores were not highly correlated with each other; for example, the correlation of the Details of Content score with Coverage was τb = 0.11 (p = 0.59) and with Duration was τb = − 0.05 (p = 0.78). The Composite score correlated with CMS quality star rating and presence of an Alzheimer’s unit, suggesting face validity.ConclusionsGuided by the FIF, we developed and used an approach to quantitatively measure overall site-level fidelity in a multi-site pragmatic trial. Future pragmatic trials, particularly in the long-term care environment, may benefit from this approach.Trial registrationClinicaltrials.gov NCT03821844. Registered on 30 January 2019, https://clinicaltrials.gov/ct2/show/NCT03821844.

Association of Power Outage With Mortality and Hospitalizations Among Florida Nursing Home Residents After Hurricane Irma

Exposure to hurricanes is associated with increased mortality and morbidity in nursing home (NH) residents, but the factors contributing to these outcomes are less understood. One hypothesized pathway could be power outages from hurricanes that expose NH residents to excess ambient heat. To determine the association of power loss from Hurricane Irma with hospitalization and mortality in NH residents in Florida. This retrospective cohort study of NH residents residing in Florida when Hurricane Irma landed on September 10, 2017, assessed mortality at 7 and 30 days after the storm and hospitalization at 30 days after the storm. The analysis was conducted from May 2, 2021, to June 28, 2021. All NH residents residing in Florida at landfall were eligible (N = 67 273). We excluded those younger than 65 years, missing power status information, or who were evacuated (13 178 [19.6%]). We used state-administered surveys to determine NH power outage status. Exposed residents experienced a power outage poststorm, whereas unexposed residents did not experience a power outage poststorm. We used Medicare claims to assess mortality and hospitalization after Hurricane Irma landfall using generalized linear models with robust standard errors. In the aftermath of Hurricane Irma, 27 892 residents (18 510 women [66.4%]; 3906 [14.0%] Black, 1651 [5.9%] Hispanic, and 21 756 [78.0%] White individuals) in 299 NHs were exposed to power loss and 26 203 residents (17 620 women [67.2%]; 4175 [15.9%] Black, 1030 [3.9%] Hispanic, and 20 477 [78.1%] White individuals) in 292 NHs were unexposed. Nursing homes that lost power were similar in size, quality star rating, and type of ownership compared with NHs that did not lose power. Power loss was associated with an increased adjusted odds of mortality among all residents within 7 days (odds ratio [OR],1.25; 95% CI,1.05-1.48) and 30 days (OR, 1.12; 95% CI,1.02-1.23) poststorm and hospitalization within 30 days, although only among residents aged 65 to 74 years (OR, 1.16; 95% CI, 1.03-1.33). In this cohort study, power loss was associated with higher odds of mortality in all affected NH residents and hospitalization in some residents. The benefits and costs of policies that require NHs to have emergency alternate power sources should be assessed.

Impact of Epic Smartlist and Lumens Software in Improving Op-29 Compliance at a Tertiary Health Care Network

OP-29 is a Centers for Medicaid and Medicare Services (CMS) measure to ensure that endoscopists recommend appropriate follow-up intervals after normal colonoscopy in average risk patients. Failure to report OP-29 compliance can adversely affect hospital quality star rating as well reimbursement for health care. Our OP-29 score was 87.47% in 2017 first quarter (Q1). This was slightly above the PA/NJ average and the national average, but not in the top decile. The aim of our project was to improve OP-29 compliance to the top decile over the next 3 years through the use of an Epic Smartlist, education, and intensive feedback.