R. Adams Dudley

Comparison of change in quality of care between safety-net and non-safety-net hospitals.

CONTEXT Safety-net hospitals (ie, those that predominantly treat poor and underserved patients) often have lower quality of care than non-safety-net hospitals. While public reporting and pay for performance have the potential to improve quality of care at poorly performing hospitals, safety-net hospitals may be unable to invest in quality improvement. As such, some have expressed concern that these incentives have the potential to worsen existing disparities among hospitals. OBJECTIVE To examine trends in disparities of quality of care between hospitals with high and low percentages of Medicaid patients. DESIGN AND SETTING Longitudinal study of the relationship between hospital performance and percentage Medicaid coverage from 2004 to 2006, using publicly available data on hospital performance. A simulation model was used to estimate payments at hospitals with high and low percentages of Medicaid patients. MAIN OUTCOME MEASURES Changes in hospital performance between 2004 and 2006, estimating whether disparities in hospital quality between hospitals with high and low percentages of Medicaid patients have changed. RESULTS Of the 4464 participating hospitals, 3665 (82%) were included in the final analysis. Hospitals with high percentages of Medicaid patients had worse performance in 2004 and had significantly smaller improvement over time than those with low percentages of Medicaid patients. Hospitals with low percentages of Medicaid patients improved composite acute myocardial infarction performance by 3.8 percentage points vs 2.3 percentage points for those with high percentages, an absolute difference of 1.5 (P = .03). This resulted in a relative difference in performance gains of 39%. Larger performance gains at hospitals with low percentages of Medicaid patients were also seen for heart failure (difference of 1.4 percentage points, P = 0.04) and pneumonia (difference of 1.3 percentage points, P <.001). Over time, hospitals with high percentages of Medicaid patients had a lower probability of achieving high-performance status. In a simulation model, these hospitals were more likely to incur financial penalties due to low performance and were less likely to receive bonuses. CONCLUSIONS Safety-net hospitals tended to have smaller gains in quality performance measures over 3 years and were less likely to be high-performing over time than non-safety-net hospitals. An incentive system based on these measures has the potential to increase disparities among hospitals.

Treatment of Unruptured Cerebral Aneurysms in California

BACKGROUND AND PURPOSE The impact of endovascular therapy on treatment outcomes of unruptured cerebral aneurysms has not been studied in a defined geographic area. METHODS All primary diagnoses of unruptured aneurysms were retrieved from a statewide database of hospital discharges in California from January 1990 through December 1998. Admissions for initial treatment and all follow-up care were combined to reflect the entire course of therapy. An adverse outcome was defined as an in-hospital death or discharge to nursing home or rehabilitation hospital at any point during the treatment course. Multivariable analyses were performed with generalized estimating equations with adjustment for age, sex, ethnicity, source of admission, year of treatment, hospital volume, and clustering of observations at institutions. RESULTS A total of 2069 patients were treated for unruptured aneurysms. Adverse outcomes were more frequent in the 1699 patients treated with surgery (25%) than in those treated with endovascular therapy (10%; P:<0.001). The difference persisted after multivariable adjustment (surgery versus endovascular therapy: odds ratio for adverse outcomes, 3.1; 95% CI, 2.5 to 4.0; P:<0.001). Adverse outcomes declined from 1991 to 1998 in patients treated with endovascular therapy (P:<0.005) but not for surgery. In-hospital deaths occurred in 3.5% of surgical cases and 0.5% of endovascular cases (P:=0.003), and the difference remained significant after adjustment (odds ratio, 6.3; 95% CI, 3.5 to 11.4; P:<0.001). Total length of stay and hospital charges were greater in surgical cases (both P:<0.001). Results were similar in a confirmatory analysis focusing on treatment differences between institutions. Institutional treatment volume was also associated with outcome but did not account for the differences between surgery and endovascular therapy. CONCLUSIONS In California, endovascular therapy of unruptured aneurysms is associated with less risk of adverse outcomes and in-hospital death, lower hospital charges, and shorter hospital stays compared with surgery. Differences between therapies became more distinct through the years. Uncontrolled differences in prognosis of patients receiving endovascular therapy and surgery cannot be ruled out in this study of discharge abstracts.

Frequency of Stress Testing to Document Ischemia Prior to Elective Percutaneous Coronary Intervention

CONTEXT Guidelines call for documenting ischemia in patients with stable coronary artery disease prior to elective percutaneous coronary intervention (PCI). OBJECTIVE To determine the frequency and predictors of stress testing prior to elective PCI in a Medicare population. DESIGN, SETTING, AND PATIENTS Retrospective, observational cohort study using claims data from a 20% random sample of 2004 Medicare fee-for-service beneficiaries aged 65 years or older who had an elective PCI (N = 23 887). MAIN OUTCOME MEASURES Percentage of patients who underwent stress testing within 90 days prior to elective PCI; variation in stress testing prior to PCI across 306 hospital referral regions; patient, physician, and hospital characteristics that predicted the appropriate use of stress testing prior to elective PCI. RESULTS In the United States, 44.5% (n = 10 629) of patients underwent stress testing within the 90 days prior to elective PCI. There was wide regional variation among the hospital referral regions with stress test rates ranging from 22.1% to 70.6% (national mean, 44.5%; interquartile range, 39.0%-50.9%). Female sex (adjusted odds ratio [AOR], 0.91; 95% confidence interval [CI], 0.86-0.97), age of 85 years or older (AOR, 0.83; 95% CI, 0.72-0.95), a history of congestive heart failure (AOR, 0.85; 95% CI, 0.79-0.92), and prior cardiac catheterization (AOR, 0.45; 95% CI, 0.38-0.54) were associated with a decreased likelihood of prior stress testing. A history of chest pain (AOR, 1.28; 95% CI, 1.09-1.54) and black race (AOR, 1.26; 95% CI, 1.09-1.46) increased the likelihood of stress testing prior to PCI. Patients treated by physicians performing 150 or more PCIs per year were less likely to have stress testing prior to PCI (AOR, 0.84; 95% CI, 0.77-0.93). No hospital characteristics were associated with receipt of stress testing. CONCLUSION The majority of Medicare patients with stable coronary artery disease do not have documentation of ischemia by noninvasive testing prior to elective PCI.

The Association between Quality of Care and the Intensity of Diabetes Disease Management Programs

Context Little is known about the effects of quality-of-care improvement programs on the process of care and outcomes of diabetes. Contribution The study involved 8661 patients with diabetes, 63 provider groups, and 3 disease management strategies (provider feedback, reminders, and structured care). The quality measures included 8 processes of care, 3 intermediate diabetes outcomes, and medication management of these outcomes. More intense disease management strategies predicted higher measures of many processes of care but only 1 intermediate outcome and 1 medication management outcome. Implications The disease management strategies improved processes of care but not outcomes. Experts in quality improvement may need to refocus their efforts. The Editors Persons with diabetes continue to receive suboptimal care (16). To improve quality, many health systems have implemented disease management programs for diabetes and other chronic conditions (79). These programs typically incorporate population-based strategies, such as disease registries, clinical guidelines, performance feedback, physician reminders, self-management support for patients, and targeted case management for high-risk patients (10). Evidence for the effectiveness of disease management comes primarily from small efficacy trials (1020). Such studies consistently found improved processes of diabetes care; however, improvements in outcomes (such as control of cardiovascular disease risk factors) were less consistent (12, 17, 18, 2022). Furthermore, most studies evaluated only 1 or 2 strategies (instead of multicomponent programs) in selected clinical settings. It is unclear how well findings from these smaller studies apply to entire patient populations. Many components of disease management focus on improving processes of care. Early performance measurement projects, such as the Health Plan Employer Data Information System (23) and the Diabetes Quality Improvement Program (5), emphasized the importance of such processes as annual retinal screening or hemoglobin A1c determination. Particularly for health plans, process measures are more readily available than are outcomes data. However, if disease management is to improve patient outcomes, it must also improve intermediate outcomes, such as hemoglobin A1c levels, systolic blood pressure, and serum low-density lipoprotein (LDL) cholesterol levels. Translating Research into Action for Diabetes (TRIAD) (24) is a multicenter study of diabetes care in managed care. The TRIAD studys central hypothesis is that health care systems features can affect quality of care. Here, we examine how 3 disease management strategies vary in intensity across physician groups and whether physician groups with more intensive disease management have higher quality of diabetes care. We assess quality by processes of care, by levels of intermediate outcomes, and by current clinical management of these outcomes. Methods Overview of the TRIAD Study and Sample The TRIAD studys sampling frame, methods, key hypotheses, and power calculations are detailed elsewhere (24). The study comprised 6 collaborating translational research centers that were partnered with 10 managed care health plans in 7 states. Of the 10 plans, 7 contracted with 1 to 26 physician groups (total, 68 groups), whereas 4 plans directly contracted with individual physicians. A standard algorithm was applied to automated pharmacy, laboratory utilization, and inpatient and outpatient diagnostic data (25) to identify all community-dwelling patients with diabetes who were 18 years of age and older and who had been continuously enrolled in the TRIAD health plan for at least 18 months. The study cohort was randomly sampled from this population. Sampled patients were recruited between July 2000 and October 2001 by using computer-assisted telephone interviews or written surveys that were conducted in English or Spanish. Eligibility was confirmed if patients verified that they had had diabetes for at least 12 months and had received most of their diabetes care through the TRIAD health plan. Permission was sought from all respondents to request copies of their outpatient medical records for the previous 18 months. All health plan and physician group directors received mailed surveys (Appendix Figure) during the same interval. Face-to-face or telephone interviews were used to complete and clarify responses. Each director was offered

Extreme Makeover: Transformation of the Veterans Health Care System

100 for completing the survey. Appendix Figure. Translating Research into Action for Diabetes (TRIAD) Physician Group Survey. The TRIAD study was reviewed and approved by the institutional review boards of each research center and by the Centers for Disease Control and Prevention (CDC). Informed consent was obtained from all survey respondents. Data Sources Patient surveys included questions on health status, diabetes duration, current diabetes treatment, and demographic characteristics. Of 13086 contacted and eligible persons, 11927 (91%) completed the survey (56.6% by computer-assisted telephone interview and 43.4% by written survey) (Figure). We were unable to contact many individuals. Using a practice that is endorsed by the Council of American Survey Research Organizations (26), we assumed that persons whom we could not contact or for whom we could not confirm eligibility had the same eligibility rate as those contacted. Under that assumption, the response rate was 69%. Figure. Description of sampling and response rate. xx Of 11927 patients who completed a survey, 8661 (73%) consented to medical record review and subsequently had charts available for review. Centrally trained reviewers used standardized data collection software to abstract process measures, most recent levels of hemoglobin A1c, upper limits of normal for hemoglobin A1c measurements that were recorded, serum LDL cholesterol levels, systolic blood pressure, current medications, and comorbid conditions. Interrater reliability () for the main quality measures ranged from 0.86 to 0.94. All 10 health plan directors and 52 of 68 physician group directors completed surveys. Surveys assessed organizational age, size, structure, profit status, insurance products, contracting arrangements, history of involvement with managed care, and detailed information on the organizations use of diabetes disease management strategies. Of the physician groups that did not respond (443 participants), 11 existed solely for the purpose of contracting with plans and had no diabetes disease management. These physician groups were assumed to have no care management strategies and were included in the analyses, as were patient groups (1150 participants) from the 4 health plans that contracted directly with physicians. The remaining 5 groups (159 participants) did not respond and were excluded from analyses (Figure). Consequently, the resulting sample included a total of 8661 survey respondents with charts available for review and data from 63 physician groups and 4 additional health plans (Figure). Mean duration of diabetes, body mass index, and health status did not meaningfully differ between persons whose medical records and physician group variables were available to the study team and those whose records were unavailable. Predictors, End Points, and Covariates The primary predictors were 3 measures of the intensity of disease management strategies: physician reminders, performance feedback, and structured care. These were calculated for physician groups and health plans from multiple survey items. A detailed description of the methods used to calculate composite intensity scores is provided in Appendix 2. Selected item-level responses for physician groups in the most intense versus least intense tercile of each strategy are displayed in Table 1. Table 1. Selected Item-Level Responses for Provider Groups for Each Disease Management Strategy* The physician reminders intensity score was derived from 2 questions, which detailed the types and content of the reminders physicians received. Groups whose use of reminders represented the upper tercile of intensity were found to have reminded physicians about 4 care processes on average. Most groups in the upper tercile delivered reminders electronically at the point of care. Performance feedback intensity was obtained by tallying responses to a checklist of possible diabetes process and outcome feedback items. A total of 86% and 82% of groups in the upper tercile included levels of hemoglobin A1c and serum LDL cholesterol, respectively, in feedback to physicians (Table 1). Physician feedback focused on many of the same elements of care as reminders. The use of formal case management, diabetes guidelines, patient reminders, and diabetes education correlated highly in physician groups (Pearson correlation coefficients ranged from 0.63 to 0.88); therefore, we could not look at these approaches independently. Consequently, we combined the 4 approaches into a single composite score for structured diabetes care management. Use of formal case management was assessed by the proportion of patients with diabetes who were enrolled, the number of case managers per 10000 patients, the extent to which the program targeted high-risk patients, and a checklist of case management activities. The clinical guidelines were scored to reflect the extent of implementation. The highest score was assigned to physician groups that incorporated guidelines into automated physician or patient reminders. Patient reminder intensity incorporated the number, type, and frequency of reminders sent. On average, physician groups in the upper tercile had diabetes education as a covered benefit, whereas those in the lowest tercile generally did not have these programs. Because of the differing numbers of questions and wide range of possible values within each intensity score, each question was z-transformed to a mean of 0.0 and standard deviation near or equal to 1.0 to facilitate comparison. Sco

The impact of financial incentives on quality of health care.

The veterans health care system administered by the U.S. Department of Veterans Affairs (VA) was established after World War I to provide health care for veterans who suffered from conditions related to their military service. It has grown to be the nations largest integrated health care system. As the system grew, a number of factors contributed to its becoming increasingly dysfunctional. By the mid-1990s, VA health care was widely criticized for providing fragmented and disjointed care of unpredictable and irregular quality, which was expensive, difficult to access, and insensitive to individual needs. Between 1995 and 1999, the VA health care system was reengineered, focusing especially on management accountability, care coordination, quality improvement, resource allocation, and information management. Numerous systemic changes were implemented, producing dramatically improved quality, service, and operational efficiency. VA health care is now considered among the best in America, and the VA transformation is viewed as a model for health care reform.

Comparison of analytic models for estimating the effect of clinical factors on the cost of coronary artery bypass graft surgery

Purchasers of health care could offer financial incentives to plans or providers in order to increase quality. Unfortunately, the current health care market, in which quality is rarely measured and there is no risk adjustment, actively discourages both plans and providers from maximizing quality, resulting in a poor overall level of quality, both in fee-for-service arrangements and health maintenance organizations. Health plans and providers will not focus on quality until mechanisms to correct for risk differences among enrollees can be developed. Although such risk adjustment will be the most important stimulus for quality, it should also be linked to improvements in information systems and agreement on a minimum benefits package, quality reporting standards, and financial solvency requirements.

Managed care in transition.

The cost of treating disease depends on patient characteristics, but standard tools for analyzing the clinical predictors of cost have deficiencies. To explore whether survival analysis techniques might overcome some of these deficiencies in the analysis of cost data, we compared ordinary least square (OLS) linear regression (with and without transformation of the data) and binary logistic regression with two survival models: the Cox proportional hazards model and a parametric model assuming a Weibull distribution. Each model was applied to data from 155 patients undergoing coronary artery bypass grafting. We examined the effects of age, sex, ejection fraction, unstable angina, and number of diseased vessels on univariable and multivariable predictions of costs. The significant univariable predictors of cost were consistent in all models: ejection fraction was significant in all five models, and age and number of diseased vessels were each significant in all but the OLS model, while sex and angina type were significant in none of the models. The significant multivariable predictors of cost, however, differed according to model: ejection fraction was a significant multivariable predictor of cost in all five models, age was significant in three models, and number of diseased vessels was significant in one model. All five models were also used to predict the costs for an average patient undergoing surgery. The Cox model provided the most accurate predictions of mean cost, median cost, and the proportion of patients with high cost. This study shows: (1) lower ejection fraction and older age are independent clinical predictors of increased cost of CABG, and (2) the Cox proportional hazards model shows considerable promise for the analysis of the impact of clinical factors upon cost.

The relationship between commercial website ratings and traditional hospital performance measures in the USA

Managed care now dominates health care in the United States. By 1999, only 8 percent of persons with employer-sponsored health insurance coverage had traditional indemnity insurance.1 This reflects...

Chronic conditions among children admitted to U.S. pediatric intensive care units: Their prevalence and impact on risk for mortality and prolonged length of stay*

Background Our goal was to compare hospital scores from the most widely used commercial website in the USA to hospital scores from more systematic measures of patient experience and outcomes, and to assess what drives variation in the commercial website scores. Methods For a national sample of US hospitals, we compared scores on Yelp.com, which aggregates website visitor ratings (1–5 stars), with traditional measures of hospital quality. We calculated correlations between hospital Yelp scores and the following: hospital percent high ratings (9 or 10, scale 0–10) on the ‘Overall’ item on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey; hospital individual HCAHPS domain scores (eg, nurse communication, pain control); hospital 30-day mortality; and hospital 30-day readmission rates. Results Of hospitals reporting HCAHPS (n=3796), 962 (25%) had scores on Yelp. Among hospitals with >5 Yelp ratings, the correlation of percent high ratings between Yelp and HCAHPS was 0.49 (p<0.001). The percent high ratings within each HCAHPS domain increased monotonically with increasing Yelp scores (p≤0.001 for all domains). Percent high ratings in Yelp and HCAHPS were statistically significantly correlated with lower mortality for myocardial infarction (MI; −0.19 for Yelp and −0.13 for HCAHPS) and pneumonia (−0.14 and −0.18), and fewer readmissions for MI (−0.17 and −0.39), heart failure (−0.31 and −0.39), and pneumonia (−0.18 and −0.27). Conclusions These data suggest that rater experiences for Yelp and HCAHPS may be similar, and that consumers posting ratings on Yelp may observe aspects of care related to important patient outcomes.