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      Quality of care and health status in Ukraine

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          Abstract

          Background

          We conducted a national level assessment of the quality of clinical care practice in the Ukrainian healthcare system for two important causes of death and chronic disease conditions. We tested two hypotheses: a) quality of care is predicted by physician and facility characteristics and b) health status is predicted by quality of care.

          Methods

          During 2009–2010 in Ukraine, we collected nationally-representative data from clinical facilities, physicians, Clinical Performance and Value (CPV®) vignettes, patient surveys from the facilities, and from the general population. Each physician completed a written CPV® vignette—a simulated case scenario of a typical patient visit—for each of two clinical cases, congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD). CPV® vignette scores, calculated as a percentage of all care criteria completed by the physician, were used as the measure of clinical quality of care. Self-reported health measures were collected from exit and household survey respondents. Regression models were developed to test the two study hypotheses.

          Results

          136 hospitals and 125 polyclinics were surveyed; 1,044 physicians were interviewed and completed CPV® vignettes. On average physicians scored 47.4% on the vignettes. Younger, female physicians provide a higher quality of care—as well as those that have had recent continuing medical education (CME) in chronic disease or health behaviors. Higher quality was associated with better health outcomes.

          Conclusions

          As low- and middle-income countries around the world are challenged by non-communicable diseases, higher quality of care provided to these populations may result in better outcomes, such as improved health status and life expectancy, and overcome regional shortfalls. Policy efforts that serially evaluate quality may improve chronic disease care.

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          Most cited references29

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          Comparison of vignettes, standardized patients, and chart abstraction: a prospective validation study of 3 methods for measuring quality.

          Better health care quality is a universal goal, yet measuring quality has proven to be difficult and problematic. A central problem has been isolating physician practices from other effects of the health care system. To validate clinical vignettes as a method for measuring the competence of physicians and the quality of their actual practice. Prospective trial conducted in 1997 comparing 3 methods for measuring the quality of care for 4 common outpatient conditions: (1) structured reports by standardized patients (SPs), trained actors who presented unannounced to physicians' clinics (the gold standard); (2) abstraction of medical records for those same visits; and (3) physicians' responses to clinical vignettes that exactly corresponded to the SPs' presentations. Setting Outpatient primary care clinics at 2 Veterans Affairs medical centers. Ninety-eight (97%) of 101 general internal medicine staff physicians, faculty, and second- and third-year residents consented to be randomized for the study. From this group, 10 physicians at each site were randomly selected for inclusion. A total of 160 quality scores (8 cases x 20 physicians) were generated for each method using identical explicit criteria based on national guidelines and local expert panels. Scores were defined as the percentage of process criteria correctly met and were compared among the 3 methods. The quality of care, as measured by all 3 methods, ranged from 76.2% (SPs) to 71.0% (vignettes) to 65.6% (chart abstraction). Measuring quality using vignettes consistently produced scores closer to the gold standard of SP scores than using chart abstraction. This pattern was robust when the scores were disaggregated by the 4 conditions (P<.001 to <.05), by case complexity (P<.001), by site (P<.001), and by level of physician training (P values from <.001 to <.05). The pattern persisted, although less dominantly, when we assessed the component domains of the clinical encounter--history, physical examination, diagnosis, and treatment. Vignettes were responsive to expected directions of variation in quality between sites and levels of training. The vignette responses did not appear to be sensitive to physicians' having seen an SP presenting with the same case. Our data indicate that quality of health care can be measured in an outpatient setting by using clinical vignettes. Vignettes appear to be a valid and comprehensive method that directly focuses on the process of care provided in actual clinical practice. Vignettes show promise as an inexpensive case-mix adjusted method for measuring the quality of care provided by a group of physicians.
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            ACC/AHA Guidelines for the Evaluation and Management of Chronic Heart Failure in the Adult: Executive Summary A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Revise the 1995 Guidelines for the Evaluation and Management of Heart Failure): Developed in Collaboration With the International Society for Heart and Lung Transplantation; Endorsed by the Heart Failure Society of America.

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              Health care expenditure prediction with a single item, self-rated health measure.

              Prediction models that identify populations at risk for high health expenditures can guide the management and allocation of financial resources. To compare the ability for identifying individuals at risk for high health expenditures between the single-item assessment of general self-rated health (GSRH), "In general, would you say your health is Excellent, Very Good, Good, Fair, or Poor?," and 3 more complex measures. We used data from a prospective cohort, representative of the US civilian noninstitutionalized population, to compare the predictive ability of GSRH to: (1) the Short Form-12, (2) the Seattle Index of Comorbidity, and (3) the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score. The outcomes were total, pharmacy, and office-based annualized expenditures in the top quintile, decile, and fifth percentile and any inpatient expenditures. Medical Expenditure Panel Survey panels 8 (2003-2004, n = 7948) and 9 (2004-2005, n = 7921). The GSRH model predicted the top quintile of expenditures, as well as the SF-12, Seattle Index of Comorbidity, though not as well as the Diagnostic Cost-Related Groups/Hierarchal Condition Categories Relative-Risk Score: total expenditures [area under the curve (AUC): 0.79, 0.80, 0.74, and 0.84, respectively], pharmacy expenditures (AUC: 0.83, 0.83, 0.76, and 0.87, respectively), and office-based expenditures (AUC: 0.73, 0.74, 0.68, and 0.78, respectively), as well as any hospital inpatient expenditures (AUC: 0.74, 0.76, 0.72, and 0.78, respectively). Results were similar for the decile and fifth percentile expenditure cut-points. A simple model of GSRH and age robustly stratifies populations and predicts future health expenditures generally as well as more complex models.
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                Author and article information

                Contributors
                jpeabody@qurehealthcare.com
                jeff.luck@oregonstate.edu
                ldemaria@qurehealthcare.com
                rmenon@worldbank.org
                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central (London )
                1472-6963
                30 September 2014
                30 September 2014
                2014
                : 14
                : 1
                : 446
                Affiliations
                [ ]QURE Healthcare, 1000 Fourth Street, Ste 300, San Rafael, CA 94901 USA
                [ ]University of California, 50 Beale Street, 12th Floor, San Francisco, CA 94105 USA
                [ ]College of Public Health and Human Sciences, Oregon State University, 401 Waldo Hall, Corvallis, OR USA
                [ ]Tanzania, Uganda and Burundi, Human Development Sector Unit, Africa Region, The World Bank, Room 410, 50 Mirambo Street, P. O. Box 2054, Dar Es Salaam, Tanzania
                Article
                3540
                10.1186/1472-6963-14-446
                4263055
                25269470
                4817cb3a-9d24-4d90-81e3-62a2d85054f7
                © Peabody et al.; licensee BioMed Central Ltd. 2014

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 7 March 2014
                : 11 September 2014
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2014

                Health & Social care
                quality of care,health care delivery,non-communicable diseases,health policy,ukraine,eastern europe

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