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      Assessing methods for measurement of clinical outcomes and quality of care in primary care practices

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          Abstract

          Purpose

          To evaluate the appropriateness of potential data sources for the population of performance indicators for primary care (PC) practices.

          Methods

          This project was a cross sectional study of 7 multidisciplinary primary care teams in Ontario, Canada. Practices were recruited and 5-7 physicians per practice agreed to participate in the study. Patients of participating physicians (20-30) were recruited sequentially as they presented to attend a visit. Data collection included patient, provider and practice surveys, chart abstraction and linkage to administrative data sets. Matched pairs analysis was used to examine the differences in the observed results for each indicator obtained using multiple data sources.

          Results

          Seven teams, 41 physicians, 94 associated staff and 998 patients were recruited. The survey response rate was 81% for patients, 93% for physicians and 83% for associated staff. Chart audits were successfully completed on all but 1 patient and linkage to administrative data was successful for all subjects. There were significant differences noted between the data collection methods for many measures. No single method of data collection was best for all outcomes. For most measures of technical quality of care chart audit was the most accurate method of data collection. Patient surveys were more accurate for immunizations, chronic disease advice/information dispensed, some general health promotion items and possibly for medication use. Administrative data appears useful for indicators including chronic disease diagnosis and osteoporosis/ breast screening.

          Conclusions

          Multiple data collection methods are required for a comprehensive assessment of performance in primary care practices. The choice of which methods are best for any one particular study or quality improvement initiative requires careful consideration of the biases that each method might introduce into the results. In this study, both patients and providers were willing to participate in and consent to, the collection and linkage of information from multiple sources that would be required for such assessments.

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          Most cited references21

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          Is primary care essential?

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            The contribution of primary care systems to health outcomes within Organization for Economic Cooperation and Development (OECD) countries, 1970-1998.

            To assess the contribution of primary care systems to a variety of health outcomes in 18 wealthy Organization for Economic Cooperation and Development (OECD) countries over three decades. Data were primarily derived from OECD Health Data 2001 and from published literature. The unit of analysis is each of 18 wealthy OECD countries from 1970 to 1998 (total n = 504). Pooled, cross-sectional, time-series analysis of secondary data using fixed effects regression. Secondary analysis of public-use datasets. Primary care system characteristics were assessed using a common set of indicators derived from secondary datasets, published literature, technical documents, and consultation with in-country experts. The strength of a country's primary care system was negatively associated with (a) all-cause mortality, (b) all-cause premature mortality, and (c) cause-specific premature mortality from asthma and bronchitis, emphysema and pneumonia, cardiovascular disease, and heart disease (p<0.05 in fixed effects, multivariate regression analyses). This relationship was significant, albeit reduced in magnitude, even while controlling for macro-level (GDP per capita, total physicians per one thousand population, percent of elderly) and micro-level (average number of ambulatory care visits, per capita income, alcohol and tobacco consumption) determinants of population health. (1) Strong primary care system and practice characteristics such as geographic regulation, longitudinality, coordination, and community orientation were associated with improved population health. (2) Despite health reform efforts, few OECD countries have improved essential features of their primary care systems as assessed by the scale used here. (3) The proposed scale can also be used to monitor health reform efforts intended to improve primary care.
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              Assessment of chronic illness care (ACIC): a practical tool to measure quality improvement.

              To describe initial testing of the Assessment of Chronic Illness Care (ACIC), a practical quality-improvement tool to help organizations evaluate the strengths and weaknesses of their delivery of care for chronic illness in six areas: community linkages, self-management support, decision support, delivery system design, information systems, and organization of care. (1) Pre-post, self-report ACIC data from organizational teams enrolled in 13-month quality-improvement collaboratives focused on care for chronic illness; (2) independent faculty ratings of team progress at the end of collaborative. Teams completed the ACIC at the beginning and end of the collaborative using a consensus format that produced average ratings of their system's approach to delivering care for the targeted chronic condition. Average ACIC subscale scores (ranging from 0 to 11, with 11 representing optimal care) for teams across all four collaboratives were obtained to indicate how teams rated their care for chronic illness before beginning improvement work. Paired t-tests were used to evaluate the sensitivity. of the ACIC to detect system improvements for teams in two (of four) collaboratives focused on care for diabetes and congestive heart failure (CHF). Pearson correlations between the ACIC subscale scores and a faculty rating of team performance were also obtained. Average baseline scores across all teams enrolled at the beginning of the collaboratives ranged from 4.36 (information systems) to 6.42 (organization of care), indicating basic to good care for chronic illness. All six ACIC subscale scores were responsive to system improvements diabetes and CHF teams made over the course of the collaboratives. The most substantial improvements were seen in decision support, delivery system design, and information systems. CHF teams had particularly high scores in self-management support at the completion of the collaborative. Pearson correlations between the ACIC subscales and the faculty rating ranged from .28 to .52. These results and feedback from teams suggest that the ACIC is responsive to health care quality-improvement efforts and may be a useful tool to guide quality improvement in chronic illness care and to track progress over time.
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                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2012
                23 July 2012
                : 12
                : 214
                Affiliations
                [1 ]Department of Family Medicine, Queen’s University, Kingston, Ontario, Canada
                [2 ]Department of Community Health and Epidemiology, Queen’s University, Kingston, Ontario, Canada
                [3 ]Centre for Health Services and Policy Research, Queen’s University, Abramsky Hall, 3rd Floor, 21 Arch Street, Kingston, Ontario, K7L 3N6, Canada
                [4 ]Centre for Studies in Primary Care, Queen’s University, Kingston, Ontario, Canada
                [5 ]Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
                [6 ]Department of Family Medicine, University of Ottawa, Ottawa, ON, Canada
                [7 ]CT Lamont Primary Care Research Centre, Ottawa, Ontario, Canada
                [8 ]Southern Academic Primary Care Research Unit, School of Primary Health Care, Monash University, Melbourne, Australia
                [9 ]Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada
                [10 ]Centre for Research on Inner City Health and Department of Family and Community Medicine, St. Michael's Hospital, Toronto, Ontario, Canada
                [11 ]Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
                Article
                1472-6963-12-214
                10.1186/1472-6963-12-214
                3431283
                22824551
                a85e25c0-d5fd-4c0a-aa28-5ba0ccd2ca3e
                Copyright ©2012 Green et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 2 November 2011
                : 23 July 2012
                Categories
                Research Article

                Health & Social care
                performance measurement,primary care,evaluation,quality of care
                Health & Social care
                performance measurement, primary care, evaluation, quality of care

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