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Recent Publications of Note

Health Care Disparities

Patient Reports of Care Quality Vary Among Racial and Ethnic Groups

This Harvard School of Public Health/Robert Wood Johnson Foundation survey, of 4,334 randomly selected U.S. adults, has sufficient sample size to examine differences in patient-reported quality of care between 14 racial and ethnic groups and whites. The authors found that, for each measure examined, at least five and as many as 11 subgroups "perceived their care to be significantly worse than care for whites"—in many instances by at least 15 percentage points. Many of these differences remained after controlling for patients' socioeconomic characteristics and language skills. R. J. Blendon et al. (2008). Disparities in Physician Care: Experiences and Perceptions of a Multi-Ethnic America. Health Affairs 27, 507–517.

Within-Hospital Disparities in Care Limited
Using three years of inpatient discharge data from 13 states, the study authors examined whether hospitals provide lower-quality care to minority patients than to white patients. Although they found racial disparities in the overall mortality and adverse event rates, there were no major differences in the quality of care across racial categories within hospitals. As these disparities were "isolated to a relatively small number of hospitals and appear to be for certain specific conditions," they recommend that policymakers focus on improving care for all patients at low-performing hospitals. D. J. Gaskin et al. (2008) Do Hospitals Provide Lower-Quality Care to Minorities than to Whites? Health Affairs 27, 518–527.

Asian Americans Often Receive Poorer Quality of Care
Although there is a large Asian population in the United States, few studies have focused on their health care experiences. This study demonstrates that Medicare data can be used to examine disparities between Asians and whites. The authors also found that such disparities are present in many metropolitan statistical areas (MSAs) with large Asian populations, though these disparities differ among MSAs. E. Moy et al. (2008) Community Variation: Disparities in Health Care Quality between Asian and White Medicare Beneficiaries. Health Affairs 27, 538–549.

Provider Resources Influence Ability to Provide Quality Care
This study "builds on a new line of research that goes beyond assessing an individual patient's characteristics to also examine the contribution to racial disparities from the aggregate socioeconomic and insurance composition of the provider's entire patient base." Using national physician survey data, the authors found that the constrained resources of practices serving racial and ethnic minority populations help to explain the greater quality-related difficulties delivering care reported by physicians working in these practices. J. D. Reschovsky and A. S. O'Malley (2008) Do Primary Care Physicians Treating Minority Patients Report Problems Delivering High-Quality Care? Health Affairs Web Exclusive April 22, 2008, w222–230.
Quality Tools in Practice

A Blueprint for Putting Disparities Recommendations into Practice
Racial and ethnic minorities receive lower quality and intensity of health care compared with whites, and these disparities in health care contribute to continuing racial and ethnic disparities in the burden of illness and death, the authors write. As physicians often struggle to translate various recommendations for reducing disparities into specific interventions in their practices, this article outlines a series of specific actions for individual clinical practices to implement. D. L. Washington et al. (2008) Transforming Clinical Practice to Eliminate Racial–Ethnic Disparities in Healthcare. Journal of General Internal Medicine 23, 685–691.

EHRs Improve Integrated Practice Network's Diabetes Care
Geisinger Health System implemented a multifaceted intervention within its network of 38 practice sites to improve compliance with recommended diabetes performance measures. An electronic registry, derived from a fully integrated electronic health record (EHR), was used to track a "bundle" of diabetes best practice measures created by a multidisciplinary group of physicians, along with audit and feedback, computerized reminders, and financial incentives. All measures of diabetes care improved over the 12-month study period, leading the authors to suggest that further work should focus on the effect of such improvements on patients' health outcomes. V. Weber et al. (2008) Employing the Electronic Health Record to Improve Diabetes Care: A Multifaceted Intervention in an Integrated Delivery System. Journal of General Internal Medicine 23, 379–382.

Electronic Lab Viewing Improves Quality
This cross-sectional study of Taconic IPA (New York) primary care physicians examined whether electronic laboratory result viewing is associated with higher ambulatory care quality. Using generalized estimating equations, 15 quality measures were analyzed to determine associations between portal usage and quality; the authors found that electronic laboratory result viewing use was associated with high quality overall. L. M. Kern et al. (2008) Electronic Result Viewing and Quality of Care in Small Group Practices. Journal of General Internal Medicine 23, 405–410.

Multidisciplinary Initiative Reduces Pediatric Central Line Infections
A retrospective, interventional study using an interrupted time-series design was conducted to determine whether staff education, increased awareness, and practice changes would decrease central line–associated bloodstream infection rates in a pediatric cardiac intensive care unit. The researchers found that the estimated mean central line–associated bloodstream infection rate dropped from 7.8 infections per 1,000 catheter-days pre-intervention, to 4.7 infections per 1,000 catheter-days in the partial intervention period, and then to 2.3 infections per 1,000 catheter-days in the full intervention period. J. M. Costello et al. (2008) Systematic Intervention to Reduce Central Line–Associated Bloodstream Infection Rates in a Pediatric Cardiac Intensive Care Unit. Pediatrics 121, 915–923.

PRIDIT: Using Process Measures to Rank Hospital Quality
This retrospective analysis of Medicare hospital data assessed whether PRIDIT methodology, an unsupervised, nonparametric aggregation technique, could be used to determine an aggregate relative measure of hospital quality. The analysis relied on data reported by 4,217 hospitals on 20 quality measures (for heart attack care, heart failure care, pneumonia care, and surgical infection prevention) and five structural measures of hospital type. The authors concluded that PRIDIT enables the use of individual process measures and demographic attributes to rank hospitals with respect to quality of care. R. D. Lieberthal (2008) Hospital Quality: A PRIDIT Approach. Health Services Research 43, 988–1005.

Do Collaboratives Improve Community Health Centers' Quality?
This observational cohort study was designed to determine whether particular features of quality improvement collaboratives for asthma, cardiovascular disease, or diabetes undertaken by community health centers were associated with improvements in health care processes or outcomes. Quality improvement activity reports and clinical data from 40 community health centers participating in the Health Disparities Collaboratives from 2000 to 2002 demonstrated that, while interventions based on the Chronic Care Model were fully implemented in the centers, no relationships between these activities and quality improvement were identified. E. Grossman et al. (2008) Inside the Health Disparities Collaboratives: A Detailed Exploration of Quality Improvement at Community Health Centers. Medical Care 46, 489–496.
Health Care System Performance

Accelerating the Improvement of Systems of Care and Practice
In this commentary, Don Berwick, M.D., president and CEO of the Institute for Healthcare Improvement, writes that improving clinical evidence and improving the process of care are often in "unhappy tension." The gap between science and experience results from the fact that the introduction of a complex, multi-component intervention in hospitals is "sensitive to an array of influences: leadership, changing environments, details of implementation, organizational history, and much more. In such complex terrain, the [randomized control trial] is an impoverished way to learn." He concludes by recommending four changes in the current approach to evidence in health care: first, embrace a wider range of scientific methodologies; second, reconsider thresholds for action on evidence; third, rethink views about trust and bias; and fourth, be careful about mood, affect, and civility in evaluations. D. M. Berwick (2008) The Science of Improvement. Journal of the American Medical Association 299, 1182–1184.

Care Coordination Central to Quality Improvement
Tom Bodenheimer, M.D., of the Department of Family and Community Medicine at the University of California at San Francisco, writes that research "strongly suggests that failures in the coordination of care are common and can create serious quality concerns." The current barriers to the seamless coordination of care include: the lack of a strong primary care foundation, low implementation rates of electronic health records, dysfunctional financing for care, and the lack of integrated systems of care. Bodenheimer concludes by reviewing several models proposed to improve care coordination: using electronic referral and referral agreements to coordinate between primary and specialty care; hospitalist-initiated projects, advanced-practice nursing, and care transitions programs to coordinate care after hospital discharge; using the "teamlet" model to assist primary care practices; and paying for care coordination. T. Bodenheimer (2008) Coordinating Care—A Perilous Journey Through the Health Care System. New England Journal of Medicine 358,106–1071.

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