Search

Objective: The authors sought to update and expand the evidence on the quality of health care and disparities in care among Medicaid beneficiaries with schizophrenia., Methods: Adult beneficiaries of New York State Medicaid with schizophrenia receiving care during 2016-2019 were identified. Composite quality scores were derived from item response theory models by using evidence-based indicators of the quality of mental and general medical health care. Risk-adjusted racial-ethnic differences in quality were estimated and summarized as percentiles relative to White beneficiaries' mean quality scores., Results: The study included 71,013 beneficiaries; 42.8% were Black, 22.9% Latinx, 27.4% White, and 6.9% other race-ethnicity. Overall, 68.8% had a mental health follow-up within 30 days of discharge, and 90.2% had no preventable hospitalizations for chronic obstructive pulmonary disease or asthma. Among beneficiaries receiving antipsychotic medications, medication adherence was adequate for 43.7%. Fourteen indicators for mental and general medical health care quality yielded three composites: two for mental health care (pharmacological and ambulatory) and one for acute mental and general medical health care. Mean quality of pharmacological mental health care for Black and Latinx beneficiaries was lower than for White beneficiaries (39th and 44th percentile, respectively). For Black beneficiaries, mean quality of ambulatory mental health care was also lower (46th percentile). In New York City, Black beneficiaries received lower-quality care in all domains. The only meaningful group difference in the quality of acute mental and general medical health care indicated higher-quality care for individuals with other race-ethnicity., Conclusions: Disparities in the quality of Medicaid-financed health care persist, particularly for Black beneficiaries. Regional differences merit further attention., Competing Interests: The authors report no financial relationships with commercial interests.

China has substantially increased financial investment and introduced favourable policies for strengthening its primary health care system with core responsibilities in preventing and managing chronic diseases such as hypertension and emerging infectious diseases such as coronavirus disease 2019 (COVID-19). However, widespread gaps in the quality of primary health care still exist. In this Review, we aim to identify the causes for this poor quality, and provide policy recommendations. System challenges include: the suboptimal education and training of primary health-care practitioners, a fee-for-service payment system that incentivises testing and treatments over prevention, fragmentation of clinical care and public health service, and insufficient continuity of care throughout the entire health-care system. The following recommendations merit consideration: (1) enhancement of the quality of training for primary health-care physicians, (2) establishment of performance accountability to incentivise high-quality and high-value care; (3) integration of clinical care with the basic public health services, and (4) strengthening of the coordination between primary health-care institutions and hospitals. Additionally, China should consider modernising its primary health-care system through the establishment of a learning health system built on digital data and innovative technologies., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Millions of Americans live in the US territories, but health outcomes and payments among Medicare beneficiaries in these territories are not well characterized., Methods: Among Fee-for-Service Medicare beneficiaries aged 65 years and older hospitalized between 1999 and 2012 for acute myocardial infarction (AMI), heart failure (HF), and pneumonia, we compared hospitalization rates, patient outcomes, and inpatient payments in the territories and states., Results: Over 14 years, there were 4,350,813 unique beneficiaries in the territories and 402,902,615 in the states. Hospitalization rates for AMI, HF, and pneumonia declined overall and did not differ significantly. However, 30-day mortality rates were higher in the territories for all 3 conditions: in the most recent time period (2008-2012), the adjusted odds of 30-day mortality were 1.34 [95% confidence interval (CI), 1.21-1.48], 1.24 (95% CI, 1.12-1.37), and 1.85 (95% CI, 1.71-2.00) for AMI, HF, and pneumonia, respectively; adjusted odds of 1-year mortality were also higher. In the most recent study period, inflation-adjusted Medicare in-patient payments, in 2012 dollars, were lower in the territories than the states, at $9234 less (61% lower than states), $4479 less (50% lower), and $4403 less (39% lower) for AMI, HF, and pneumonia hospitalizations, respectively (P<0.001 for all)., Conclusions and Relevance: Among Medicare Fee-for-Service beneficiaries, in 2008-2012 mortality rates were higher, or not significantly different, and hospital reimbursements were lower for patients hospitalized with AMI, HF, and pneumonia in the territories. Improvement of health care and policies in the territories is critical to ensure health equity for all Americans.

Objective: To determine whether (a) quality in schizophrenia care varies by race/ethnicity and over time and (b) these patterns differ across counties within states., Data Sources: Medicaid claims data from California, Florida, New York, and North Carolina during 2002-2008., Study Design: We studied black, Latino, and white Medicaid beneficiaries with schizophrenia. Hierarchical regression models, by state, quantified person and county effects of race/ethnicity and year on a composite quality measure, adjusting for person-level characteristics., Principal Findings: Overall, our cohort included 164,014 person-years (41-61 percent non-whites), corresponding to 98,400 beneficiaries. Relative to whites, quality was lower for blacks in every state and also lower for Latinos except in North Carolina. Temporal improvements were observed in California and North Carolina only. Within each state, counties differed in quality and disparities. Between-county variation in the black disparity was larger than between-county variation in the Latino disparity in California, and smaller in North Carolina; Latino disparities did not vary by county in Florida. In every state, counties differed in annual changes in quality; by 2008, no county had narrowed the initial disparities., Conclusions: For Medicaid beneficiaries living in the same state, quality and disparities in schizophrenia care are influenced by county of residence for reasons beyond patients' characteristics., (© Health Research and Educational Trust.)

Objective: To characterize hospitals based on patterns of their combined financial and clinical outcomes for heart failure hospitalizations longitudinally., Data Source: Detailed cost and administrative data on hospitalizations for heart failure from 424 hospitals in the 2005-2011 Premier database., Study Design: Using a mixture modeling approach, we identified groups of hospitals with distinct joint trajectories of risk-standardized cost (RSC) per hospitalization and risk-standardized in-hospital mortality rate (RSMR), and assessed hospital characteristics associated with the distinct patterns using multinomial logistic regression., Principal Findings: During 2005-2011, mean hospital RSC decreased from $12,003 to $10,782, while mean hospital RSMR declined from 3.9 to 3.2 percent. We identified five distinct hospital patterns: highest cost and low mortality (3.2 percent of the hospitals), high cost and low mortality (20.4 percent), medium cost and low mortality (34.6 percent), medium cost and high mortality (6.2 percent), and low cost and low mortality (35.6 percent). Longer hospital stay and greater use of intensive care unit and surgical procedures were associated with phenotypes with higher costs or greater mortality., Conclusions: Hospitals vary substantially in the joint longitudinal patterns of cost and mortality, suggesting marked difference in value of care. Understanding determinants of the variation will inform strategies for improving the value of hospital care., (© Health Research and Educational Trust.)

Objective: To examine racial/ethnic disparities in quality of schizophrenia care and assess the size of observed disparities across states and over time., Data Sources: Medicaid claims data from CA, FL, NY, and NC., Study Design: Observational repeated cross-sectional panel cohort study of white, black, and Latino fee-for-service adult beneficiaries with schizophrenia. Main outcome was the relationship of race/ethnicity and year with a composite measure of quality of schizophrenia care derived from 14 evidence-based quality indicators., Principal Findings: Quality was assessed for 325,373 twelve-month person-episodes between 2002 and 2008, corresponding to 123,496 Medicaid beneficiaries. In 2002, quality was lowest for blacks in all states. With the exception of FL, quality was lower for Latinos than whites. In CA, blacks had about 43 percent of the individual indicators met compared to 58 percent for whites. Quality improved annually for all groups in CA, NY, and NC. While in CA the improvement was slightly larger for Latinos, in FL quality improved for blacks but declined for Latinos and whites., Conclusions: Quality of schizophrenia care is poor and racial/ethnic disparities exist among Medicaid beneficiaries from four states. The size of the disparities varied across the states, and most of the initial disparities were unchanged by 2008., (© Health Research and Educational Trust.)

Safety-net hospitals, which include urban hospitals serving large numbers of low-income, uninsured, and otherwise vulnerable populations, have historically faced greater financial strains than hospitals that serve more affluent populations. These strains can affect hospitals' quality of care, perhaps resulting in worse outcomes that are commonly used as indicators of care quality-mortality and readmission rates. We compared risk-standardized rates of both of these clinical outcomes among fee-for-service Medicare beneficiaries admitted for acute myocardial infarction, heart failure, or pneumonia. These beneficiaries were admitted to urban hospitals within Metropolitan Statistical Areas that contained at least one safety-net and at least one non-safety-net hospital. We found that outcomes varied across the urban areas for both safety-net and non-safety-net hospitals for all three conditions. However, mortality and readmission rates were broadly similar, with non-safety-net hospitals outperforming safety-net hospitals on average by less than one percentage point across most conditions. For heart failure mortality, there was no difference between safety-net and non-safety-net hospitals. These findings suggest that safety-net hospitals are performing better than many would have expected.

Purpose: To compare the performance of U.S. teaching and nonteaching hospitals using a portfolio of contemporary, publicly reported metrics., Method: The authors classified acute care general hospitals filing a Medicare Institutional Cost Report according to teaching intensity: nonteaching, teaching, or Council of Teaching Hospitals member. They compared aggregate results across categories for Hospital Compare process compliance, mortality, and readmission rates (acute myocardial infarction [AMI], heart failure, pneumonia); Surgical Care Improvement Project (SCIP) performance; compliance with Leapfrog standards; patient experience; patient services and key technologies; safety (computerized physician order entry, intensive care unit staffing, National Quality Forum safe practices, hospital-acquired conditions); and cost/resource utilization (Medicare-adjusted expense per case; Leapfrog efficiency and resource use standards)., Results: Availability of patient services and advanced technologies were associated with teaching intensity (P < .0001), as were most hospital safety metrics. Teaching intensity was favorably associated with SCIP performance, AMI and heart failure process scores, and mortality (P < .0001). It was unfavorably associated with higher AMI and pneumonia readmission rates (P < .0001) and lower scores for individual patient satisfaction measures. Costs per case were similar (P = .4194) across hospital categories after correction for federally allowed adjustments (case mix, wages, and low-income patient care)., Conclusions: Teaching hospitals offer advanced clinical capabilities, educate the next generation of providers, care for disadvantaged urban populations, and are leaders in health care research and innovation. However, many stakeholders may be unaware of an additional value-relatively higher quality and safety in many areas, with similar adjusted costs.

Background: In-hospital mortality measures, which are widely used to assess hospital quality, are not based on a standardized follow-up period and may systematically favor hospitals with shorter lengths of stay (LOSs)., Objective: To assess the agreement between performance measures of U.S. hospitals by using risk-standardized in-hospital and 30-day mortality rates., Design: Observational study., Setting: Nonfederal acute care hospitals in the United States with at least 30 admissions for acute myocardial infarction (AMI), heart failure (HF), and pneumonia from 2004 to 2006., Patients: Medicare fee-for-service patients admitted for AMI, HF, or pneumonia from 2004 to 2006., Measurements: The primary outcomes were in-hospital and 30-day risk-standardized mortality rates (RSMRs)., Results: Included patients comprised 718,508 admissions to 3135 hospitals for AMI, 1,315,845 admissions to 4209 hospitals for HF, and 1,415,237 admissions to 4498 hospitals for pneumonia. The hospital-level mean patient LOS varied across hospitals for each condition, ranging from 2.3 to 13.7 days for AMI, 3.5 to 11.9 days for HF, and 3.8 to 14.8 days for pneumonia. The mean RSMR differences (30-day RSMR minus in-hospital RSMR) were 5.3% (SD, 1.3) for AMI, 6.0% (SD, 1.3) for HF, and 5.7% (SD, 1.4) for pneumonia; distributions varied widely across hospitals. Performance classifications differed between the in-hospital and 30-day models for 257 hospitals (8.2%) for AMI, 456 (10.8%) for HF, and 662 (14.7%) for pneumonia. Hospital mean LOS was positively correlated with in-hospital RSMRs for all 3 conditions., Limitation: Medicare claims data were used for risk adjustment., Conclusion: In-hospital mortality measures provide a different assessment of hospital performance than 30-day mortality and are biased in favor of hospitals with shorter LOSs., Primary Funding Source: The Centers for Medicare & Medicaid Services and National Heart, Lung, and Blood Institute.

Context: During the past decade, the introduction of generic versions of newer antidepressants and the release of Food and Drug Administration warnings regarding suicidality in children, adolescents, and young adults may have had an effect on cost and quality of depression treatment., Objectives: To examine longitudinal trends in health service utilization, spending, and quality of care for depression., Design: Observational trend study., Setting: Florida Medicaid enrollees, between July 1, 1996, and June 30, 2006. Patients  Annual cohorts aged 18 to 64 years diagnosed as having depression., Main Outcome Measures: Mental health care spending (adjusted for inflation and case mix), as well as its components, including inpatient, outpatient, and medication expenditures. Quality-of-care measures included medication adherence, psychotherapy, and follow-up visits., Results: Mental health care spending increased from a mean of $2802 per enrollee to $3610 during this period (29% increase). This increase occurred despite a mean decrease in inpatient spending from $641 per enrollee to $373 and was driven primarily by an increase in pharmacotherapy spending (up 110%), the bulk of which was due to spending on antipsychotics (949% increase). The percentage of enrollees with depression who were hospitalized decreased from 9.1% to 5.1%, and the percentage who received psychotherapy decreased from 56.6% to 37.5%. Antidepressant use increased from 80.6% to 86.8%, anxiety medication use was unchanged at 62.7% and 64.4%, and antipsychotic use increased from 25.9% to 41.9%. Changes in quality of care were mixed, with antidepressant use improving slightly, psychotherapy utilization fluctuating, and follow-up visits decreasing., Conclusions: During a 10-year period, spending for Medicaid enrollees with depression increased substantially, with minimal improvements in quality of care. Antipsychotic use contributed significantly to the increase in spending, while contributing little to traditional measures of quality of care.

Background: Health care quality in the US territories is poorly characterized. We used process measures to compare the performance of hospitals in the US territories and in the US states., Methods: Our sample included nonfederal hospitals located in the United States and its territories discharging Medicare fee-for-service (FFS) patients with a principal discharge diagnosis of acute myocardial infarction (AMI), heart failure (HF), or pneumonia (PNE) (July 2005-June 2008). We compared risk-standardized 30-day mortality and readmission rates between territorial and stateside hospitals, adjusting for performance on core process measures and hospital characteristics., Results: In 57 territorial hospitals and 4799 stateside hospitals, hospital mean 30-day risk-standardized mortality rates were significantly higher in the US territories (P<.001) for AMI (18.8% vs 16.0%), HF (12.3% vs 10.8%), and PNE (14.9% vs 11.4%). Hospital mean 30-day risk-standardized readmission rates (RSRRs) were also significantly higher in the US territories for AMI (20.6% vs 19.8%; P=.04), and PNE (19.4% vs 18.4%; P=.01) but was not significant for HF (25.5% vs 24.5%; P=.07). The higher risk-standardized mortality rates in the US territories remained statistically significant after adjusting for hospital characteristics and core process measure performance. Hospitals in the US territories had lower performance on all core process measures (P<.05)., Conclusions: Compared with hospitals in the US states, hospitals in the US territories have significantly higher 30-day mortality rates and lower performance on every core process measure for patients discharged after AMI, HF, and PNE. Eliminating the substantial quality gap in the US territories should be a national priority.

Background: National attention has increasingly focused on readmission as a target for quality improvement. We present the development and validation of a model approved by the National Quality Forum and used by the Centers for Medicare & Medicaid Services for hospital-level public reporting of risk-standardized readmission rates for patients discharged from the hospital after an acute myocardial infarction., Methods and Results: We developed a hierarchical logistic regression model to calculate hospital risk-standardized 30-day all-cause readmission rates for patients hospitalized with acute myocardial infarction. The model was derived using Medicare claims data for a 2006 cohort and validated using claims and medical record data. The unadjusted readmission rate was 18.9%. The final model included 31 variables and had discrimination ranging from 8% observed 30-day readmission rate in the lowest predictive decile to 32% in the highest decile and a C statistic of 0.63. The 25th and 75th percentiles of the risk-standardized readmission rates across 3890 hospitals were 18.6% and 19.1%, with fifth and 95th percentiles of 18.0% and 19.9%, respectively. The odds of all-cause readmission for a hospital 1 SD above average were 1.35 times that of a hospital 1 SD below average. Hospital-level adjusted readmission rates developed using the claims model were similar to rates produced for the same cohort using a medical record model (correlation, 0.98; median difference, 0.02 percentage points)., Conclusions: This claims-based model of hospital risk-standardized readmission rates for patients with acute myocardial infarction produces estimates that are excellent surrogates for those produced from a medical record model.

Background: Several countries use hospital-wide mortality rates to evaluate the quality of hospital care, although the usefulness of this metric has been questioned. Massachusetts policymakers recently requested an assessment of methods to calculate this aggregate mortality metric for use as a measure of hospital quality., Methods: The Massachusetts Division of Health Care Finance and Policy provided four vendors with identical information on 2,528,624 discharges from Massachusetts acute care hospitals from October 1, 2004, through September 30, 2007. Vendors applied their risk-adjustment algorithms and provided predicted probabilities of in-hospital death for each discharge and for hospital-level observed and expected mortality rates. We compared the numbers and characteristics of discharges and hospitals included by each of the four methods. We also compared hospitals' standardized mortality ratios and classification of hospitals with mortality rates that were higher or lower than expected, according to each method., Results: The proportions of discharges that were included by each method ranged from 28% to 95%, and the severity of patients' diagnoses varied widely. Because of their discharge-selection criteria, two methods calculated in-hospital mortality rates (4.0% and 5.9%) that were twice the state average (2.1%). Pairwise associations (Pearson correlation coefficients) of discharge-level predicted mortality probabilities ranged from 0.46 to 0.70. Hospital-performance categorizations varied substantially and were sometimes completely discordant. In 2006, a total of 12 of 28 hospitals that had higher-than-expected hospital-wide mortality when classified by one method had lower-than-expected mortality when classified by one or more of the other methods., Conclusions: Four common methods for calculating hospital-wide mortality produced substantially different results. This may have resulted from a lack of standardized national eligibility and exclusion criteria, different statistical methods, or fundamental flaws in the hypothesized association between hospital-wide mortality and quality of care. (Funded by the Massachusetts Division of Health Care Finance and Policy.).

Objective: To investigate the feasibility, reliability, and validity of comprehensively assessing physician-level performance in ambulatory practice., Data Sources/study Setting: Ambulatory-based general internists in 13 states participated in the assessment., Study Design: We assessed physician-level performance, adjusted for patient factors, on 46 individual measures, an overall composite measure, and composite measures for chronic, acute, and preventive care. Between- versus within-physician variation was quantified by intraclass correlation coefficients (ICC). External validity was assessed by correlating performance on a certification exam., Data Collection/extraction Methods: Medical records for 236 physicians were audited for seven chronic and four acute care conditions, and six age- and gender-appropriate preventive services., Principal Findings: Performance on the individual and composite measures varied substantially within (range 5-86 percent compliance on 46 measures) and between physicians (ICC range 0.12-0.88). Reliabilities for the composite measures were robust: 0.88 for chronic care and 0.87 for preventive services. Higher certification exam scores were associated with better performance on the overall (r = 0.19; p<.01), chronic care (r = 0.14, p = .04), and preventive services composites (r = 0.17, p = .01)., Conclusions: Our results suggest that reliable and valid comprehensive assessment of the quality of chronic and preventive care can be achieved by creating composite measures and by sampling feasible numbers of patients for each condition., (© Health Research and Educational Trust.)

The assessment of medical practice is evolving rapidly in the United States. An initial focus on structure and process performance measures assessing the quality of medical care is now being supplemented with efficiency measures to quantify the "value" of healthcare delivery. This statement, building on prior work that articulated standards for publicly reported outcomes measures, identifies preferred attributes for measures used to assess efficiency in the allocation of healthcare resources. The attributes identified in this document combined with the previously published standards are intended to serve as criteria for assessing the suitability of efficiency measures for public reporting. This statement identifies the following attributes to be considered for publicly reported efficiency measures: integration of the quality and cost; valid cost measurement and analysis; minimal incentive to provide poor quality care; and proper attribution of the measure. The attributes described in this statement are relevant to a wide range of efforts to profile the efficiency of various healthcare providers, including hospitals, healthcare systems, managed-care organizations, physicians, group practices, and others that deliver coordinated care.

Background and Objective: The Centers for Medicare and Medicaid Services (CMS) report quality of care for patients hospitalized with acute myocardial infarction (AMI), congestive heart failure (CHF), and community-acquired pneumonia (CAP) with the intention of rewarding superior performing hospitals. The aim of the study was to compare identification of superior hospitals for providing financial rewards using 2 different scoring systems: a latent score that weights individual clinical performance measures according to how well each discriminated hospital quality and a raw sum score (the system adopted by CMS)., Methods: This observational cohort study used 2761 acute care hospitals in the United States reporting AMI clinical performance measures, 3271 reporting CHF measures, and 3714 hospitals reporting CAP measures. For each clinical condition, the main outcome measures included the average raw sum score, the latent score estimated from an item response theory (IRT) model, and the percentage of false negative superior designations made on the basis of raw sum scores relative to latent scores., Results: The average raw sum score was highest for AMI (88.8%) and lower for CHF (73.1%) and CAP (76.3%). AMI measures were equally nondiscriminating of hospital quality; hospital discharge instruction was most discriminating of CHF quality; pneumococcal vaccination was most discriminating of CAP quality. False negative rates varied 2-fold: AMI (10%), CHF (16%), and CAP (24%)., Conclusions: Neither the AMI raw sum score nor latent score discriminates hospital quality due to ceiling effects. Current methods for aggregating measures result in different hospital superior designations than those based on the latent score. Organizations that financially reward hospitals on the basis of such scores need to assess predictive validity of scores and determine a minimum level of classification accuracy.

Provider profiling and performance-based incentive programs have expanded in recent years but need a theoretical framework for measuring and comparing the "value'' of clinical care across medical providers. Cost-effectiveness analysis provides such a framework but has rarely been used outside of the treatment choice context. The authors present a profiling framework based on cost-effectiveness methods and illustrate their approach using data on in-hospital survival and the cost of care for a heart attack from a sample of Massachusetts hospitals during fiscal year 2003. They model each outcome using hierarchical models that allow performance to vary across hospitals as a function of a latent quality effect and an effect of case mix. They also estimate incremental outcomes by conditioning on each hospital's pair of random effects, using indirect standardization to estimate "expected'' outcomes, and then taking their difference. Incremental cost and effectiveness outcomes are combined using incremental net monetary benefits. Using cost-effectiveness methods to profile hospital "value'' permits the comparison of the benefit of a service relative to the cost using existing societal weights.

Health plans have begun to combine data on the quality and cost of medical providers in an attempt to identify and reward those that offer the greatest 'value.' The analytical methods used to combine these measures in the context of provider profiling have not been rigorously studied. We propose three methods to measure and compare the value of hospital care following acute myocardial infarction by combining a single measure of quality, in-hospital survival, and the cost of an episode of acute care. To illustrate these methods, we use administrative data for heart attack patients treated at 69 acute care hospitals in Massachusetts in fiscal year 2003. In the first method we reproduce a common approach to value profiling by modeling the two case mix-standardized outcomes independently. In the second approach, survival is regressed on patient risk factors and the average cost of care at each hospital. The third method models survival and cost for each hospital jointly and combines the outcomes on a common scale using a cost-effectiveness framework. For each method we use the resulting parameter estimates or functions of the estimates to compute posterior tail probabilities, representing the probability of being classified in the upper or lower quartile of the statewide distribution. Hospitals estimated to have the highest and lowest value according to each method are compared for consistency, and the advantages and disadvantages of each approach are discussed., (2008 John Wiley & Sons, Ltd)

Context: Value-based purchasing by employers has often been portrayed as the lynchpin to quality improvement in a market-based health care system. Although a small group of the largest national employers has been actively engaged in promoting quality measurement, reporting, and pay for performance, it is unknown whether these ideas have significantly permeated employer-sponsored health benefit purchasing., Objective: To provide systematic descriptions and analyses of value-based purchasing and related efforts to improve quality of care by health care purchasers., Design, Setting, and Participants: We conducted telephone interviews with executives at 609 of the largest employers across 41 US markets between July 2005 and March 2006. The 41 randomly selected markets have at least 100,000 persons enrolled in health maintenance organizations, include approximately 91% of individuals enrolled in health maintenance organizations nationally, and represent roughly 78% of the US metropolitan population. Using the Dun & Bradstreet database of US employers, we identified the 26 largest firms in each market. Firms ranged in size from 60 to 250,000 employees., Main Outcome Measure: The degree to which value-based purchasing and related strategies are reported being used by employers. Percentages were weighted by number of employees., Results: Of 1041 companies contacted, 609 employer representatives completed the survey (response rate, 64%). A large percentage of surveyed executives reported that they examine health plan quality data (269 respondents; 65% [95% confidence interval {CI}, 57%-74%]; P<.001), but few reported using it for performance rewards (49 respondents; 17% [95% CI, 7%-27%]; P=.008) or to influence employees (71 respondents; 23% [95% CI, 13%-33%]). Physician quality information is even less commonly examined (71 respondents; 16% [95% CI, 9%-23%]) or used by employers to reward performance (8 respondents; 2% [95% CI, 0%-3%]) or influence employee choice of providers (34 respondents; 8% [95% CI, 3%-12%])., Conclusion: Surveyed employers as a whole do not appear to be individually implementing incentives and programs in line with value-based purchasing ideals.

Context: In contrast to the commercially insured population, the proportion of Medicaid beneficiaries enrolling in health maintenance organizations continues to increase., Objective: To compare quality of care within and between the Medicaid and commercial populations in 3 types of managed care plans: Medicaid-only plans (serving predominantly Medicaid enrollees), commercial-only plans (serving predominantly commercial enrollees), and Medicaid/commercial plans (serving substantial numbers of both types of enrollees)., Design, Setting, and Participants: All 383 health plans that reported quality-of-care data to the National Committee for Quality Assurance for 2002 and 2003, including 204 commercial-only plans, 142 Medicaid/commercial plans (plans reported data for the Medicaid and commercial populations separately); and 37 Medicaid-only plans., Main Outcome Measures: Eleven quality indicators from the Healthcare Effectiveness Data and Information Set (HEDIS) applicable to the Medicaid population., Results: Among Medicaid enrollees, performance on the 11 measures observed in this study were comparable for Medicaid-only plans and Medicaid/commercial plans. Similarly, among commercial enrollees, there was virtually no difference in performance between health plans that served only the commercial population and those that also served the Medicaid population. Overall across all health plan types, the performance for the commercial population exceeded the performance for the Medicaid population on all measures except 1, ranging from a difference of 4.9% for controlling hypertension (58.4% for commercial vs 53.5% for Medicaid; P = .002) to 24.5% for rates of appropriate postpartum care (77.2% for commercial vs 52.7% for Medicaid; P = .001). Differences of similar magnitude were observed for commercial and Medicaid populations treated within the same health plan., Conclusions: Medicaid managed care enrollees receive lower-quality care than that received by commercial managed care enrollees. There were no differences in quality of care for the Medicaid population between Medicaid-only plans and commercial plans that also served the Medicaid population.

Background: The ranking of "America's Best Hospitals" by U.S. News & World Report for "Heart and Heart Surgery" is a popular hospital profiling system, but it is not known if hospitals ranked by the magazine vs nonranked hospitals have lower risk-standardized, 30-day mortality rates (RSMRs) for patients with acute myocardial infarction (AMI)., Methods: Using a hierarchical regression model based on 2003 Medicare administrative data, we calculated RSMRs for ranked and nonranked hospitals in the treatment of AMI. We identified ranked and nonranked hospitals with standardized mortality ratios (SMRs) significantly less than the mean expected for all hospitals in the study., Results: We compared 13 662 patients in 50 ranked hospitals with 254 907 patients in 3813 nonranked hospitals. The RSMRs were lower in ranked vs nonranked hospitals (16.0% vs 17.9%, P<.001). The RSMR range for ranked vs nonranked hospitals overlapped (11.4%-20.0% vs 13.1%-23.3%, respectively). In an RSMR quartile distribution of all hospitals, 35 ranked hospitals (70%) were in the lowest RSMR or best performing quartile, 11 (22%) were in the middle 2 quartiles, and 4 (8%) were in the highest RSMR or worst performing quartile. There were 11 ranked hospitals (22%) and 28 nonranked hospitals (0.73%) that each had an SMR significantly less than 1 (defined by a 95% confidence interval with an upper limit of <1.0)., Conclusions: On average, admission to a ranked hospital for AMI was associated with a lower risk of 30-day mortality, although about one-third of the ranked hospitals fell outside the best performing quartile based on RSMR. Although ranked hospitals were much more likely to have an SMR significantly less than 1, many more nonranked hospitals had this distinction.

Safety-net hospitals are experiencing increasing financial strains, possibly affecting their quality of care. We compare quality at safety-net and non-safety-net urban hospitals for Medicare beneficiaries admitted with acute myocardial infarction (AMI). Although safety-net hospitals had modestly higher risk-standardized thirty-day all-cause mortality rates and modestly lower adherence to quality-of-care performance measures than non-safety-net hospitals, there was much heterogeneity among safety-net hospitals and substantial overlap with non-safety-net hospitals. We examine the implications of these findings for the millions of vulnerable Americans who rely on safety-net hospitals for their care.

Background: The Joint Commission on Accreditation of Healthcare Organizations and the Centers for Medicare and Medicaid Services recently began reporting on quality of care for acute myocardial infarction, congestive heart failure, and pneumonia., Methods: We linked performance data submitted for the first half of 2004 to American Hospital Association data on hospital characteristics. We created composite scales for each disease and used factor analysis to identify 2 additional composites based on underlying domains of quality. We estimated logistic regression models to examine the relationship between hospital characteristics and quality., Results: Overall, 75.9% of patients hospitalized with these conditions received recommended care. The mean composite scores and their associated interquartile ranges were 0.85 (0.81-0.95), 0.64 (0.52-0.78), and 0.88 (0.80-0.97) for acute myocardial infarction, congestive heart failure, and pneumonia, respectively. After adjustment, for-profit hospitals consistently underperformed not-for-profit hospitals for each condition, with odds ratios (ORs) ranging from 0.79 (95% confidence interval [CI], 0.78-0.80) for the congestive heart failure composite measure to 0.90 (95% CI, 0.89-0.91) for the pneumonia composite. Major teaching hospitals had better performance on the treatment and diagnosis composite (OR, 1.37; 95% CI, 1.34-1.39) but worse performance on the counseling and prevention composite (OR, 0.83; 95% CI, 0.82-0.84). Hospitals with more technology available, higher registered nurse staffing, and federal/military designation had higher performance., Conclusions: Patients are more likely to receive high-quality care in not-for-profit hospitals and in hospitals with high registered nurse staffing ratios and more investment in technology. Because payments and sources of payments affect some of these factors (eg, investments in technology and staffing ratios), policy makers should evaluate the effect of alternative payment approaches on quality.

Background: Since the 1990s, parity laws have been implemented to reduce inequities in mental health coverage compared with that for general medical conditions. It is unclear if parity under managed care is associated with improvements in mental health treatment quality. Major depressive disorder (MDD) is a prevalent but often undetected and undertreated and thus could potentially benefit from parity implementation., Objective: The objective of this study was to examine the association between parity implementation and changes in MDD treatment quality in the Federal Employees' Health Benefits (FEHB) Program., Methods: We conducted retrospective analyses of insurance claims data. Logistic regression models estimated quality changes for MDD-diagnosed enrollees from pre- to postparity., Subjects: Subjects included MDD-diagnosed FEHB insured enrollees, aged 18-64, across multiple states and 6 FEHB plans before (1999-2000) and after (2001-2002) parity implementation., Measures: Measures included receipt of any antidepressant or psychotherapy within a given calendar year of diagnosis; receipt of appropriate psychotherapy frequency/intensity and duration; and pharmacotherapy duration during acute-phase treatment episodes., Results: Postparity, several plans improved significantly in the likelihood of receiving antidepressant medication. In the acute-phase episodes, the greatest improvement was seen in the likelihood of follow up >or=4 months. Few or no other changes were observed in the acute-phase treatment intensity or duration quality measures., Conclusions: Parity under managed care was associated with modest improvements. The observed improvements were consistent with secular trends in MDD treatment. Whereas mental health parity is an important policy goal, these results highlight its limitations: improving the financing of care may not be sufficient to improve quality.

Background: A model using administrative claims data that is suitable for profiling hospital performance for heart failure would be useful in quality assessment and improvement efforts., Methods and Results: We developed a hierarchical regression model using Medicare claims data from 1998 that produces hospital risk-standardized 30-day mortality rates. We validated the model by comparing state-level standardized estimates with state-level standardized estimates calculated from a medical record model. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1999-2001. The final model included 24 variables and had an area under the receiver operating characteristic curve of 0.70. In the derivation set from 1998, the 25th and 75th percentiles of the risk-standardized mortality rates across hospitals were 11.6% and 12.8%, respectively. The 95th percentile was 14.2%, and the 5th percentile was 10.5%. In the validation samples, the 5th and 95th percentiles of risk-standardized mortality rates across states were 9.9% and 13.9%, respectively. Correlation between risk-standardized state mortality rates from claims data and rates derived from medical record data was 0.95 (SE=0.015). The slope of the weighted regression line from the 2 data sources was 0.76 (SE=0.04) with intercept of 0.03 (SE=0.004). The median difference between the claims-based state risk-standardized estimates and the chart-based rates was <0.001 (25th percentile=-0.003; 75th percentile=0.002). The performance of the model was stable over time., Conclusions: This administrative claims-based model produces estimates of risk-standardized state mortality that are very good surrogates for estimates derived from a medical record model.

Background: A model using administrative claims data that is suitable for profiling hospital performance for acute myocardial infarction would be useful in quality assessment and improvement efforts. We sought to develop a hierarchical regression model using Medicare claims data that produces hospital risk-standardized 30-day mortality rates and to validate the hospital estimates against those derived from a medical record model., Methods and Results: For hospital estimates derived from claims data, we developed a derivation model using 140,120 cases discharged from 4664 hospitals in 1998. For the comparison of models from claims data and medical record data, we used the Cooperative Cardiovascular Project database. To determine the stability of the model over time, we used annual Medicare cohorts discharged in 1995, 1997, and 1999-2001. The final model included 27 variables and had an area under the receiver operating characteristic curve of 0.71. In a comparison of the risk-standardized hospital mortality rates from the claims model with those of the medical record model, the correlation coefficient was 0.90 (SE=0.003). The slope of the weighted regression line was 0.95 (SE=0.007), and the intercept was 0.008 (SE=0.001), both indicating strong agreement of the hospital estimates between the 2 data sources. The median difference between the claims-based hospital risk-standardized mortality rates and the chart-based rates was <0.001 (25th and 75th percentiles, -0.003 and 0.003). The performance of the model was stable over time., Conclusions: This administrative claims-based model for profiling hospitals performs consistently over several years and produces estimates of risk-standardized mortality that are good surrogates for estimates from a medical record model.

With the proliferation of efforts to report publicly the outcomes of healthcare providers and institutions, there is a growing need to define standards for the methods that are being employed. An interdisciplinary writing group identified 7 preferred attributes of statistical models used for publicly reported outcomes. These attributes include (1) clear and explicit definition of an appropriate patient sample, (2) clinical coherence of model variables, (3) sufficiently high-quality and timely data, (4) designation of an appropriate reference time before which covariates are derived and after which outcomes are measured, (5) use of an appropriate outcome and a standardized period of outcome assessment, (6) application of an analytical approach that takes into account the multilevel organization of data, and (7) disclosure of the methods used to compare outcomes, including disclosure of performance of risk-adjustment methodology in derivation and validation samples.

The ability to quantify the quality of cardiovascular care critically depends on the translation of recommendations for high-quality care into the measurement of that care. As payers and regulatory agencies increasingly seek to quantify healthcare quality, the implications of the measurement process on practicing physicians are likely to grow. This statement describes the methodology by which the American College of Cardiology and the American Heart Association approach creating performance measures and devising techniques for quantifying those aspects of care that directly reflect the quality of cardiovascular care. Methods for defining target populations, identifying dimensions of care, synthesizing the literature, and operationalizing the process of selecting measures are proposed. It is hoped that new sets of measures will be created through the implementation of this approach, and consequently, through the use of such measurement sets in the context of quality improvement efforts, the quality of cardiovascular care will improve.

Admissions to 20 publicly funded alcohol and drug detoxification centers in Massachusetts were examined to identify program and patient variables that influenced length of stay. The last admission during fiscal year 1996 was abstracted for patients 18 years of age and older seeking alcohol, cocaine, or heroin detoxification (n=21,311; 29% women). A hierarchical generalized linear model examined the effects of patient and program characteristics on variation in length of stay and tested case-mix adjustments. Program size had the most influence on mean adjusted length of stay; stays were more than 40% longer in detoxification centers with 35 or more beds (7.69 days) than in centers with less than 35 beds (5.42 days). The study highlights the contribution of program size to treatment processes and suggests the need for more attention to program attributes in studies of patient outcomes and treatment processes.

Background: In mental health services research there is little empirical evidence to support speculation about the relationship between costs and quality., Aims of the Study: The purpose of this paper is to provide a conceptual model and test its usefulness in determining the cost of care that meets evidence-based standards., Methods: A case study of individuals treated for an acute episode of schizophrenia is described and a conceptual model for determining the costs of evidence-based care is presented. Statistical tests of difference were used to compare two groups, those with care that met guideline standards and those that did not., Results: Compared to care that did not meet recommendations, evidence-based care was cheaper. Clinical benefits to patients were the same, but those with poor care (higher than recommended doses of anti-psychotic medication) had higher treatment costs as well as more side-effects., Discussion: The conceptual model faces many challenges in application, but shows promise as one approach to determining the cost of evidenced based care., Implications for Health Care Provision and Use: The results support efforts to encourage clinicians to follow practice guidelines., Implications for Health Policies: The growth of evidence-based medicine must be matched by efforts to assess the costs of adherence to practice guidelines. The pressure of fiscal restraints needs to be balanced with information about what the cost will be to provide recommended treatment., Implications for Further Research: The greatest effort needs to be in the conceptual development of the model so that we can confidently estimate the costs and effectiveness of evidence-based treatment recommendations.

Provider profiling is the activity of collecting, comparing and reporting quality of care measures for individuals, groups, agencies and institutions that provide health care services. Univariate provider profiles, such as hospital-specific mortality rates, have been constructed using cross-sectional data based on posterior summaries or maximum likelihood estimates. As data continue to be collected over time, the construction and interpretation of longitudinal profiles of health care providers will become increasingly important. Longitudinal series can be used to improve the precision of estimates - a feature that is particularly important for providers who treat a small number of patients per year. We extend and apply hierarchical models to examine and classify provider performance over time using two examples, one in the area of cardiology and the other in mental health. Performance is evaluated using the squared Mahalanobis distance and posterior probabilities based on this distance. By comparing providers based on level and temporal trend simultaneously, conservative but comprehensive assessments of performance are possible. Furthermore, the longitudinal profiles developed are easily interpreted and flexible, making them of practical use to policy-makers., (Copyright 2002 John Wiley & Sons, Ltd.)

The article discusses a study on the use of hierarchical regression models in assessing the rates of risk-standardized mortality and readmission of patients with heart failure in hospitals identified in the "America's Best Hospitals" ranking by the periodical "U.S. News & World Report" and in those non-ranked facilities. The study noted there was a reduced mean rate of in-hospital mortality in ranked hospitals, compared to non-ranked ones. It is said that ranked hospitals have not shown enough evidence of superiority for readmission.

The performance of physicians in their day-to-day clinical practices has become an area of intense public interest. Both patients and health care purchasers want more effective means of identifying excellent clinicians, and a variety of organizations are discussing and implementing plans for assessing the performance of individual clinicians. In this article, we review the current state of physician clinical performance assessment with a focus on its usefulness for competency assessment. We describe recommendations for a physician clinical performance assessment system for these purposes, and identify ways in which current methods of performance assessment fall short of these. We conclude that important technical barriers stand in the way of using physician clinical performance assessment for evaluating the competency of individual physicians. Overcoming these barriers will require considerable additional research and development. Even then, for some uses, physician clinical performance assessment at the individual physician level may be technically impossible to accomplish in a valid and fair way. jrnlabbrev="JAMA" year="2003" volume="290" page="1183-1189">. [ABSTRACT FROM AUTHOR]

Presents a letter to the editor regarding an article published in a previous issue of the "Journal of the American Medical Association," and discusses the uses of published physician performance data.