Assessing methods for measurement of clinical outcomes and quality of care in primary care practices
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
BMC Health Services Research 2012, 12:214 doi:10.1186/1472-6963-12-214Published: 23 July 2012
To evaluate the appropriateness of potential data sources for the population of performance indicators for primary care (PC) practices.
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.
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.
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.