Is Bigger Better?: A Closer Look at Small Health Systems in the United States.

BACKGROUND: Research on US health systems has focused on large systems with at least 50 physicians. Little is known about small systems. OBJECTIVES: Compare the characteristics, quality, and costs of care between small and large health systems. RESEARCH DESIGN: Retrospective, repeated cross-sectional analysis. SUBJECTS: Between 468 and 479 large health systems, and between 608 and 641 small systems serving fee-for-service Medicare beneficiaries, yearly between 2013 and 2017. MEASURES: We compared organizational, provider and beneficiary characteristics of large and small systems, and their geographic distribution, using multiple Medicare and Internal Revenue Service administrative data sources. We used mixed-effects regression models to estimate differences between small and large systems in claims-based Healthcare Effectiveness Data and Information Set (HEDIS) quality measures and HealthPartners' Total Cost of Care measure using a 100% sample of Medicare fee-for-service claims. We fit linear spline models to examine the relationship between the number of a system's affiliated physicians and its quality and costs. RESULTS: The number of both small and large systems increased from 2013 to 2017. Small systems had a larger share of practice sites (43.1% vs. 11.7% for large systems in 2017) and beneficiaries (51.4% vs. 15.5% for large systems in 2017) in rural areas or small towns. Quality performance was lower among small systems than large systems (-0.52 SDs of a composite quality measure) and increased with system size up to ∼75 physicians. There was no difference in total costs of care. CONCLUSIONS: Small systems are a growing source of care for rural Medicare populations, but their quality performance lags behind large systems. Future studies should examine the mechanisms responsible for quality differences.

Trends in US Ambulatory Care Patterns During the COVID-19 Pandemic, 2019-2021.

Importance: Following reductions in US ambulatory care early in the pandemic, it remains unclear whether care consistently returned to expected rates across insurance types and services. Objective: To assess whether patients with Medicaid or Medicare-Medicaid dual eligibility had significantly lower than expected return to use of ambulatory care rates than patients with commercial, Medicare Advantage, or Medicare fee-for-service insurance. Design, Setting, and Participants: In this retrospective cohort study examining ambulatory care service patterns from January 1, 2019, through February 28, 2021, claims data from multiple US payers were combined using the Milliman MedInsight research database. Using a difference-in-differences design, the extent to which utilization during the pandemic differed from expected rates had the pandemic not occurred was estimated. Changes in utilization rates between January and February 2020 and each subsequent 2-month time frame during the pandemic were compared with the changes in the corresponding months from the year prior. Age- and sex-adjusted Poisson regression models of monthly utilization counts were used, offsetting for total patient-months and stratifying by service and insurance type. Exposures: Patients with Medicaid or Medicare-Medicaid dual eligibility compared with patients with commercial, Medicare Advantage, or Medicare fee-for-service insurance, respectively. Main Outcomes and Measures: Utilization rates per 100 people for 6 services: emergency department, office and urgent care, behavioral health, screening colonoscopies, screening mammograms, and contraception counseling or HIV screening. Results: More than 14.5 million US adults were included (mean age, 52.7 years; 54.9% women). In the March-April 2020 time frame, the combined use of 6 ambulatory services declined to 67.0% (95% CI, 66.9%-67.1%) of expected rates, but returned to 96.7% (95% CI, 96.6%-96.8%) of expected rates by the November-December 2020 time frame. During the second COVID-19 wave in the January-February 2021 time frame, overall utilization again declined to 86.2% (95% CI, 86.1%-86.3%) of expected rates, with colonoscopy remaining at 65.0% (95% CI, 64.1%-65.9%) and mammography at 79.2% (95% CI, 78.5%-79.8%) of expected rates. By the January-February 2021 time frame, overall utilization returned to expected rates as follows: patients with Medicaid at 78.4% (95% CI, 78.2%-78.7%), Medicare-Medicaid dual eligibility at 73.3% (95% CI, 72.8%-73.8%), commercial at 90.7% (95% CI, 90.5%-90.9%), Medicare Advantage at 83.2% (95% CI, 81.7%-82.2%), and Medicare fee-for-service at 82.0% (95% CI, 81.7%-82.2%; P < .001; comparing return to expected utilization rates among patients with Medicaid and Medicare-Medicaid dual eligibility, respectively, with each of the other insurance types). Conclusions and Relevance: Between March 2020 and February 2021, aggregate use of 6 ambulatory care services increased after the preceding decrease in utilization that followed the onset of the COVID-19 pandemic. However, the rate of increase in use of these ambulatory care services was significantly lower for participants with Medicaid or Medicare-Medicaid dual eligibility than for those insured by commercial, Medicare Advantage, or Medicare fee-for-service.

Rates of Disenrollment From Medicare Advantage Plans Are Higher for Racial/Ethnic Minority Beneficiaries.

BACKGROUND: Each year, about 10% of Medicare Advantage (MA) enrollees voluntarily switch to another MA contract, while another 2% voluntarily switch from MA to fee-for-service Medicare. Voluntary disenrollment from MA plans is related to beneficiaries' negative experiences with their plan, disrupts the continuity of care, and conflicts with goals to reduce Medicare costs. Little is known about racial/ethnic disparities in voluntary disenrollment from MA plans. OBJECTIVE: The objective of this study was to investigate differences in rates of voluntary disenrollment from MA plans by race/ethnicity. SUBJECTS: A total of 116,770,319 beneficiaries enrolled in 736 MA plans in 2015. METHODS: Differences in rates of disenrollment across racial/ethnic groups [Asian or Pacific Islander (API), Black, Hispanic, and White] were summarized using 4 types of logistic regression models: adjusted and unadjusted models estimating overall differences and adjusted and unadjusted models estimating within-plan differences. Unadjusted overall models included only racial/ethnic group probabilities as predictors. Adjusted overall models added age, sex, dual eligibility, disability, and state of residence as control variables. Between-plan differences were estimated by subtracting within-plan differences from overall differences. RESULTS: Adjusted rates of disenrollment were significantly (P<0.001) higher for Hispanic (+1.2 percentage points), Black (+1.2 percentage points), and API beneficiaries (+2.4 percentage points) than for Whites. Within states, all 3 racial/ethnic minority groups tended to be concentrated in higher disenrollment plans. Within plans, API beneficiaries voluntarily disenrolled considerably more often than otherwise similar White beneficiaries. CONCLUSION: These findings suggest the need to address cost, information, and other factors that may create barriers to racial/ethnic minority beneficiaries' enrollment in plans with lower overall disenrollment rates.

Distinguishing High-Performing Health Systems Using a Composite of Publicly Reported Measures of Ambulatory Care.

BACKGROUND: Payers and policymakers are rewarding high-performing health care providers on the basis of summaries of overall quality performance, and the methods they use for measuring performance are increasingly important. OBJECTIVE: To develop and evaluate a measure that ranks health care systems by ambulatory care quality. DESIGN: Systems were ranked using a composite model that summarizes individual measures of quality, accounts for their correlation, and does not require health care systems to report every measure. The composite measure's suitability was evaluated by examining whether it captured the quality indicated by component measures (validity), whether differences in rank between health care systems were larger than statistical noise (reliability), and whether year-to-year changes in rank were small (stability). SETTING: California and Minnesota, 2014 to 2016. PARTICIPANTS: 55 health care systems. MEASUREMENTS: Publicly reported measures of ambulatory care quality. RESULTS: The composite measure was valid in that it was broadly representative of the component measures and was not dominated by any single measure. The measure was reliable because the ranks for 93% of California systems and 80% of Minnesota systems were unlikely to be more than 2 places lower or higher. The measure was stable because fewer than half of systems changed ranks by more than 2 ranks from year to year. LIMITATION: The analysis is limited to available measures of ambulatory care quality and includes only 2 states. CONCLUSION: This composite measure uses publicly reported data to produce valid, reliable, and stable ranks of ambulatory care quality for health care systems in Minnesota and California, and this approach could be used in other applications. PRIMARY FUNDING SOURCE: Agency for Healthcare Research and Quality.

Alignment Between Objective and Subjective Assessments of Health System Performance: Findings From a Mixed-Methods Study.

EXECUTIVE SUMMARY: The article examines whether subjective performance assessments from health system executives match objective performance assessments and qualitatively explores ways to achieve high performance. We interviewed 138 C-suite executives of 24 health systems in California, Minnesota, Washington, and Wisconsin between 2017 and 2019. We used maximum variation sampling to select health systems to achieve diversity in performance on objective measures of clinical performance. Our interviews focused on executives' perceptions of their own health system's performance and factors they thought generally contributed to high performance. In our analysis, we grouped health systems based on objective performance levels (high, medium, and low) used in sampling, compared objective performance ratings with executives' subjective performance assessments, and used thematic analysis to identify reasons for subjective assessment of health system performance and levers of high performance in general. There was poor agreement between objective and subjective performance assessments (kappa = 0.082). Subjective assessments were higher than objective assessments and captured more factors than are typically considered in performance accountability and value-based payment initiatives. Executives whose views were inconsistent with objective performance assessments did not cite clinical care quality per se as the basis for their assessment, focusing instead on market competition, financial performance, and high customer satisfaction and loyalty. Executives who cited clinical quality metrics as the basis of their assessment offered subjective ratings consistent with objective ratings. Executives identified organizational culture, organizational governance, and staff engagement as levers for achieving high performance. Future research should explore the benefits and drawbacks of considering subjective performance assessments in value-based payment initiatives.

Trends in the use of computerized physician order entry by health-system affiliated ambulatory clinics in the United States, 2014-2016.

BACKGROUND: Computerized provider order entry (CPOE) can help providers deliver better quality care. We aimed to understand recent trends in use of CPOE by health system-affiliated ambulatory clinics. METHODS: We analyzed longitudinal data (2014-2016) for 19,109 ambulatory clinics that participated in all 3 years of the Healthcare Information and Management Systems Society Analytics survey to assess use of CPOE and identify characteristics of clinics associated with CPOE use. We calculated descriptive statistics to examine overall trends in use, location of order entry (bedside vs. clinical station), and system-level use CPOE across all clinics. We used linear probability models to explore the association between clinic characteristics (practice size, practice type, and health system type) and two outcomes of interest: CPOE use at any point between 2014 and 2016, and CPOE use beginning in 2015 or 2016. RESULTS: Between 2014 and 2016, use of CPOE increased more than 9 percentage points from 58 to 67%. Larger clinics and those affiliated with multi-hospital health systems were more likely to have reported use of CPOE. We found no difference in CPOE use by primary care versus specialty care clinics. When used, most clinics reported using CPOE for most or all of their orders. Health systems that used CPOE usually did so for all system-affiliated clinics. CONCLUSIONS: Small practice size or not being part of a multi-hospital system are associated with lower use of CPOE between 2014 and 2016. Less than optimal use in these environments may be harming patient outcomes.

Association of Primary and Specialty Care Integration on Physician Communication and Cancer Screening in Safety-Net Clinics.

INTRODUCTION: Primary care providers who lack reliable referral relationships with specialists may be less likely than those who do have such relationships to conduct cancer screenings. Community health centers (CHCs), which provide primary care to disadvantaged populations, have historically reported difficulty accessing specialty care for their patients. This study aimed to describe strategies CHCs use to integrate care with specialists and examine whether more strongly integrated CHCs have higher rates of screening for colorectal and cervical cancers and report better communication with specialists. METHODS: Using a 2017 survey of CHCs in 12 states and the District of Columbia and administrative data, we estimated the association between a composite measure of CHC/specialist integration and 1) colorectal and cervical cancer screening rates, and 2) 4 measures of CHC/specialist communication using multivariate regression models. RESULTS: Integration strategies commonly reported by CHCs included having specialists deliver care on-site (80%) and establishing referral agreements with specialists (70%). CHCs that were most integrated with specialists had 5.6 and 6.8 percentage-point higher colorectal and cervical cancer screening rates, respectively, than the least integrated CHCs (P < .05). They also had significantly higher rates of knowing that specialist visits happened (67% vs 42%), knowing visit outcomes (65% vs 42%), receiving information after visits (47% vs 21%), and timely receipt of information (44% vs 27%). CONCLUSION: CHCs use various strategies to integrate primary and specialty care. Efforts to promote CHC/specialist integration may help increase rates of cancer screening.

How are medical groups identified as high-performing? The effect of different approaches to classification of performance.

BACKGROUND: Payers and policy makers across the international healthcare market are increasingly using publicly available summary measures to designate providers as "high-performing", but no consistently-applied approach exists to identifying high performers. This paper uses publicly available data to examine how different classification approaches influence which providers are designated as "high-performers". METHODS: We conducted a quantitative analysis of cross-sectional publicly-available performance data in the U.S. We used 2014 Minnesota Community Measurement data from 58 medical groups to classify performance across 4 domains: quality (two process measures of cancer screening and 2 composite measures of chronic disease management), total cost of care, access (a composite CAHPS measure), and patient experience (3 CAHPS measures). We classified medical groups based on performance using either relative thresholds or absolute values of performance on all included measures. RESULTS: Using relative thresholds, none of the 58 medical groups achieved performance in the top 25% or 35% in all 4 performance domains. A relative threshold of 40% was needed before one group was classified as high-performing in all 4 domains. Using absolute threshold values, two medical groups were classified as high-performing across all 4 domains. In both approaches, designating "high performance" using fewer domains led to more groups designated as high-performers, though there was little to moderate concordance across identified "high-performing" groups. CONCLUSIONS: Classification of medical groups as high performing is sensitive to the domains of performance included, the classification approach, and choice of threshold. With increasing focus on achieving high performance in healthcare delivery, the absence of a consistently-applied approach to identify high performers impedes efforts to reliably compare, select and reward high-performing providers.

What Role Does Efficiency Play in Understanding the Relationship Between Cost and Quality in Physician Organizations?

BACKGROUND: The belief that there is inefficiency, or the potential to improve patient health at current levels of spending, is driving the push for greater value in health care. Previous studies demonstrate overuse of a narrow set of services, suggesting provider inefficiency, but existing studies neither quantify inefficiency more broadly nor assess its variation across physician organizations (POs). DATA AND METHODS: We used data on quality of care and total cost of care from 129 California POs participating in a statewide value-based pay-for-performance program. We estimated a production function with quality as the output and cost as the input, using a stochastic frontier model, to develop a measure of relative efficiency for each PO. To validate the efficiency measure, we examined correlations of PO efficiency estimates with indicators representing overuse of services. RESULTS: The estimated production function showed that PO quality was positively associated with costs, although there were diminishing marginal returns to spending. A certain minimum level of spending was associated with high quality even among efficient POs. Most strikingly, however, POs had substantial variation in efficiency, producing widely differing levels of quality for the same cost. CONCLUSIONS: Differences among POs in the efficiency with which they produce quality suggest opportunities for improvements in care delivery that increase quality without increasing spending.

What Defines a High-Performing Health Care Delivery System: A Systematic Review.

BACKGROUND: Purchasers, payers, and policy makers are increasingly measuring and rewarding high-performing health systems, which use a variety of definitions of high performance, yet it is unclear if a consistently applied definition exists. A systematic review was conducted to determine if there is a commonly used, agreed-on definition of what constitutes a "high-performing" health care delivery system. METHODS: Searches were conducted for English-language articles defining high performance with respect to a health care system or organization in PubMed and WorldCat databases from 2005 to 2015 and the New York Academy of Medicine Grey Literature Report from 1999 to 2016. The entity/condition to which the definition was applied was extracted from included articles. The number and type of dimensions used to define high performance within and across articles was tabulated and the number and type of metrics used by performance dimension and by article was calculated. RESULTS: No consistent definition of a high-performing health care system or organization was identified. High performance was variably defined across different dimensions, including quality (93% of articles), cost (67%), access (35%), equity (26%), patient experience (21%), and patient safety (18%). Most articles used more than one dimension to define high performance (75%), but only five used five or more dimensions. The most commonly paired dimensions were quality and cost (63%). CONCLUSION: The absence of a consistent definition of what constitutes high performance and how to measure it hinders our ability to compare and reward health care delivery systems on performance, underscoring the need to develop a consistent definition of high performance.

Implementation Processes and Pay for Performance in Healthcare: A Systematic Review.

BACKGROUND: Over the last decade, various pay-for-performance (P4P) programs have been implemented to improve quality in health systems, including the VHA. P4P programs are complex, and their effects may vary by design, context, and other implementation processes. We conducted a systematic review and key informant (KI) interviews to better understand the implementation factors that modify the effectiveness of P4P. METHODS: We searched PubMed, PsycINFO, and CINAHL through April 2014, and reviewed reference lists. We included trials and observational studies of P4P implementation. Two investigators abstracted data and assessed study quality. We interviewed P4P researchers to gain further insight. RESULTS: Among 1363 titles and abstracts, we selected 509 for full-text review, and included 41 primary studies. Of these 41 studies, 33 examined P4P programs in ambulatory settings, 7 targeted hospitals, and 1 study applied to nursing homes. Related to implementation, 13 studies examined program design, 8 examined implementation processes, 6 the outer setting, 18 the inner setting, and 5 provider characteristics. Results suggest the importance of considering underlying payment models and using statistically stringent methods of composite measure development, and ensuring that high-quality care will be maintained after incentive removal. We found no conclusive evidence that provider or practice characteristics relate to P4P effectiveness. Interviews with 14 KIs supported limited evidence that effective P4P program measures should be aligned with organizational goals, that incentive structures should be carefully considered, and that factors such as a strong infrastructure and public reporting may have a large influence. DISCUSSION: There is limited evidence from which to draw firm conclusions related to P4P implementation. Findings from studies and KI interviews suggest that P4P programs should undergo regular evaluation and should target areas of poor performance. Additionally, measures and incentives should align with organizational priorities, and programs should allow for changes over time in response to data and provider input.

Challenges in assessing the process-outcome link in practice.

The expanded use of clinical process-of-care measures to assess the quality of health care in the context of public reporting and pay-for-performance applications has led to a desire to demonstrate the value of such efforts in terms of improved patient outcomes. The inability to observe associations between improved delivery of clinical processes and improved clinical outcomes in practice has raised concerns about the value of holding providers accountable for delivery of clinical processes of care. Analyses that attempt to investigate this relationship are fraught with many challenges, including selection of an appropriate outcome, the proximity of the outcome to the receipt of the clinical process, limited power to detect an effect, small expected effect sizes in practice, potential bias due to unmeasured confounding factors, and difficulties due to changes in measure specification over time. To avoid potentially misleading conclusions about an observed or lack of observed association between a clinical process of care and an outcome in the context of observational studies, individuals conducting and interpreting such studies should carefully consider, evaluate, and acknowledge these types of challenges.

Market landscape and insurer-provider integration: the case of ambulatory surgery centers.

Insurer-provider integration is a new form of vertical integration, with increasing prominence in health care markets. While there are potential benefits from tighter alignment between providers and payers, risks of perverse impacts on health care markets loom large. Yet, little is known about this new wave of consolidation, which limits options for policy or regulatory responses. We focus on a dominant insurer's acquisitions of ambulatory surgery centers (ASCs) to document the growth and geographic spread of these ownership events. We found that a diverse swathe of the United States has experienced an insurer-led ASC takeover. The acquisitions are also more frequently in areas where the insurer holds a higher enrollee market share at baseline, although a linear prediction of the likelihood of ASC acquisition shows a more nuanced picture.

The segregation of physician networks providing care to black and white patients with heart disease: Concepts, measures, and empirical evaluation.

Black-White disparities in cardiac care may be related to physician referral network segregation. We developed and tested new geographic physician network segregation measures. We used Medicare claims to identify Black and White Medicare heart disease patients and map physician networks for 169 hospital referral regions (HRRs) with over 1000 Black patients. We constructed two network segregation indexes ranging from 0 (integration) to 100 (total segregation): Dissimilarity (the unevenness of Black and White patient distribution across physicians [Dn]) and Absolute Clustering (the propensity of Black patients' physicians to have closer ties with each other than with other physicians [ACLn]). We employed conditional logit models to estimate the probability of using the best (lowest mortality) geographically available hospital for Black and White patients undergoing coronary artery bypass grafting (CABG) surgery in 126 markets with sufficient sample size at increasing levels of network segregation and for low vs. high HRR Black patient population. Physician network segregation was lower than residential segregation (Dissimilarity 21.9 vs. 48.7, and Absolute Clustering 4.8 vs. 32.4) and positively correlated with residential segregation (p < .001). Network segregation effects differed by race and HRR Black patient population. For White patients, higher network segregation was associated with a higher probability of using the best available hospitals in HRRs with few black patients but unchanged (ACLn) or lower (Dn) probability of best hospital use in HRRs with many Black patients. For Black patients, higher network segregation was not associated with a substantial change in the probability of best hospital use regardless of the HRR Black patient population size. Measuring physician network segregation is feasible and associated with nuanced effects on Black-White differences in high-quality hospital use for heart disease. Further work is needed to understand underlying mechanisms and potential uses in health equity policy.

Association between physician age and patterns of end-of-life care among older Americans.

BACKGROUND: End-of-life (EOL) care patterns may differ by physician age given differences in how physicians are trained or changes associated with aging. We sought to compare patterns of EOL care delivered to older Americans according to physician age. METHODS: We conducted a cross-sectional study of a 20% sample of Medicare fee-for-service beneficiaries aged ≥66 years who died in 2016-2019 (n = 487,293). We attributed beneficiaries to the physician who had >50% of primary care visits during the last 6 months of life. We compared beneficiary-level outcomes by physician age (<40, 40-49, 50-59, or ≥60) in two areas: (1) advance care planning (ACP) and palliative care; and (2) high-intensity care at the EOL. RESULTS: Beneficiaries attributed to younger physicians had slightly higher proportions of billed ACP (adjusted proportions, 17.1%, 16.1%, 15.5%, and 14.0% for physicians aged <40, 40-49, 50-59, and ≥60, respectively; p-for-trend adjusted for multiple comparisons <0.001) and palliative care counseling or hospice use in the last 180 days of life (64.5%, 63.6%, 61.9%, and 60.8%; p-for-trend <0.001). Similarly, physicians' younger age was associated with slightly lower proportions of emergency department visits (57.4%, 57.0%, 57.4%, and 58.1%; p-for-trend <0.001), hospital admissions (51.2%, 51.1%, 51.4%, and 52.1%; p-for-trend <0.001), intensive care unit admissions (27.8%, 27.9%, 28.2%, and 28.3%; p-for-trend = 0.03), or mechanical ventilation or cardiopulmonary resuscitation (14.2, 14.9%, 15.2%, and 15.3%; p-for-trend <0.001) in the last 30 days of life, and in-hospital death (20.2%, 20.6%, 21.3%, and 21.5%; p-for-trend <0.001). CONCLUSIONS: We found that differences in patterns of EOL care between beneficiaries cared for by younger and older physicians were small, and thus, not clinically meaningful. Future research is warranted to understand the factors that can influence patterns of EOL care provided by physicians, including initial and continuing medical education.

Contextual issues influencing implementation and outcomes associated with an integrated approach to managing pain, agitation, and delirium in adult ICUs.

OBJECTIVE: This pilot study was designed to identify which contextual factors facilitate/hinder the implementation of the awakening, breathing, coordination, delirium, and early mobility (ABCDE) bundle for guidance in future studies. DESIGN: The sources of data for this study included document review, planned site visits (including interviews and observations), a brief online contextual factors survey, and self-reported process and outcome data. PATIENTS: All patients in the four participating SF Bay Area ICUs were eligible to be included in this pilot study. SETTING: This study took place in the four San Francisco Bay Area ICUs participating in the ICU Clinical Impact Interest Group, funded by the Gordon and Betty Moore Foundation from January 2012 through June 2013. INTERVENTIONS: This was a pilot evaluation study to identify factors that facilitated/hindered the implementation of the ABCDE bundle, interventions designed to decrease the prevalence of ICU-acquired delirium and muscle weakness. The ABCDE bundle consists of spontaneous awakening trials, spontaneous breathing trials, coordination of awakening and breathing trials, choice of sedation, delirium screening and treatment, and early progressive mobility. MEASUREMENTS: Process data related to bundle element compliance were collected at baseline and monthly during the intervention period. Outcome data (average ICU length of stay and average days on mechanical ventilation) were collected at baseline and quarterly during the intervention period. Hospital-specific results of the online contextual factors survey and information gathered through interviews and observations during site visits also contributed to the analysis. MAIN RESULTS: Factors related to structural characteristics of the ICU, an organizational-wide patient safety culture, an ICU culture of quality improvement, implementation planning, training/support, and prompts/documentation are believed to have facilitated the rate and success of ABCDE bundle implementation. Excessive turnover (both in project and ICU leadership), staff morale issues, lack of respect among disciplines, knowledge deficits, and excessive use of registry staff are believed to have hindered implementation. CONCLUSIONS: Successful implementation of the elements of the ABCDE bundle can result in significant improvements in ICU patient care. The results of this study highlight specific structural and cultural elements of ICUs and hospitals that can positively and negatively influence the implementation of complex care bundles like the ABCDE bundle. Further research is needed to assess the influence of these contextual factors across a broader variety of ICUs and hospitals.

Changes in US Hospital Financial Performance During the COVID-19 Public Health Emergency.

Importance: The COVID-19 public health emergency (PHE) caused substantial changes in hospital operations. The net effect of these changes on hospital financial performance is unclear. Objective: To evaluate changes in hospital financial performance before and during the COVID-19 PHE. Design, Setting, and Participants: This longitudinal within-hospital cohort analysis from 2017 to 2021 used national RAND hospital data merged with American Community Survey data. A total of 4223 short-term acute care or critical access hospitals in the US with financial data spanning 2017 to 2021 were evaluated. Exposure: Financial performance during the first 2 years of the PHE. Main Outcomes and Measures: The main outcome was PHE financial distress calculated based on net operating income (operating revenue minus operating expenses). Within-hospital changes in net operating income over time were evaluated with and without COVID-19 relief funding. From henceforth, 2020/2021 means the weighted average financial performance for both calendar year 2020 and 2021. Hospitals were characterized as having new financial distress if (1) their average 2020/2021 net operating income was negative and (2) the average 2020/2021 net operating income was less than that hospital's pre-2020 net operating income. Predictors of new financial distress were evaluated using logistic regression and predictors of COVID-19 relief using 2-part models. Results: In this sample of 4423 hospitals, 3529 (80.0%) received PHE funds during 2020/2021. A total of 846 (19.1%) were located in a census tract with more than 20% Hispanic residents. Of the total number of hospitals, 720 (16.3%) of hospitals had PHE financial distress, whereas 2047 (46.3%) had PHE financial distress after excluding COVID-19 relief funding from net operating income. The majority of hospitals (n = 3337; 74.8%) had a positive net operating income across 2020/2021, with 785 (17.8%) hospitals moving from a negative pre-2020 to a positive 2020/2021 net operating income. In adjusted analyses, hospitals treating a higher proportion of Hispanic populations were more likely to have PHE distress (adjusted odds ratio, 1.3; 95% CI, 1.1-1.6; P = .02). Median (IQR) operating margins from 2020/2021 were at an all-time high of 6.5% (0.2%-13.3%) compared with pre-2020 operating margins of 2.8% (-2.8% to 9.3%). Conclusions and Relevance: In this cohort study of US hospitals, the large majority had positive financial performance during 2020/2021, partly due to COVID-19 relief funds. However, hospitals serving Hispanic populations had substantially worsened financial performance during 2020/2021, even after accounting for COVID-19 relief. That COVID-19 relief funding aided in operating margins reaching all-time highs indicates funding amounts may have been larger than was necessary for many hospitals. With COVID-19 relief funding ending yet COVID-19 related continuing to affect hospital expenses, ongoing monitoring of hospital financial performance is vital to ensure patients retain access to care.

A Formal Framework For Incorporating Equity Into Health Care Quality Measurement.

Quality measurement is an important tool for incentivizing improvement in the quality of health care. Most quality measurement efforts do not explicitly target health equity. Although some measurement approaches may intend to realign incentives to focus quality improvement efforts on underserved groups, the extent to which they accomplish this goal is understudied. We posit that tying incentives to approaches on the basis of stratification or disparities may have unintended consequences or limited effects. Such approaches might not reduce existing disparities because addressing one aspect of equity may be in competition with addressing others. We propose equity weighting, a new measurement framework to advance equity on multiple fronts that addresses the shortcomings of existing approaches and explicitly calibrates incentives to align with equity goals. We use colorectal cancer screening data derived from 2017 Medicare claims to illustrate how equity weighting fixes unintended consequences in other methods and how it can be adapted to policy goals.

Understanding the social risk factor adjustment's effect on Star Ratings.

OBJECTIVES: CMS implemented the Categorical Adjustment Index (CAI) to address measurement bias in the Medicare Advantage (MA) Star Ratings, as unadjusted scores may disadvantage MA contracts serving more enrollees at greater social risk. CAI values are added to a contract's Star Ratings to adjust for the mean within-contract performance disparity associated with its percentage of enrollees with low socioeconomic status (ie, receipt of a Part D low-income subsidy or dual eligibility for Medicare and Medicaid [LIS/DE]) and who are disabled. We examined the CAI's effect on Star Ratings and the type of contracts affected. STUDY DESIGN: Observational study of MA contracts with health and prescription drug coverage. METHODS: We compared adjusted and unadjusted 2017-2020 Star Ratings overall and by contracts' proportion of LIS/DE and disabled enrollees. We assessed the CAI's effect on qualifying for quality bonus payments (QBPs), eligibility for rebate payments, and high-performing and low-performing designations. RESULTS: The CAI's impact was modest overall (3.2%-14.9% of contracts experienced one-half Star Rating changes). Upward changes were concentrated among contracts with high percentages of LIS/DE or disabled enrollees (7.7%-32.3% of these contracts saw increased Star Ratings). In 2020, 26.0% of contracts with a high proportion of LIS/DE or disabled enrollees that qualified for a QBP did so because of the CAI. CONCLUSIONS: The CAI primarily affected contracts with high LIS/DE or disabled enrollment, which received higher Star Ratings because of the CAI. The adjustment helps ensure that such contracts' performance is not understated and reduces incentives for MA contracts to avoid patients at greater social risk.