Evaluation of the Interface Between General Practice and Population Health in Research funded by the General Practice Evaluation Program

Abstract

The purpose of this study was to identify the fields of population health covered by General Practice Evaluation Program (GPEP) research and to evaluate the population health findings and implications of this research. The research method was to conduct a review of 150 GPEP project reports completed between 1992-1998, using a framework developed to identify research with population health content and/or relevance. A total of 65 projects were identified in the following areas: health promotion; disease prevention and screening; systematic illness care; integration of health services; data collection; and research on specific populations. The majority of the research was descriptive in nature (53 out of 65 projects). Strengths of the existent research lie in documenting the use of interventions to improve systematic illness care, and in GP activities directed towards disease prevention and screening. However, the analysis identified a number of problem areas. Recording and collection of information about GP activities is variable, in part due to a lack of consensus about the kind of information that should be collected. Our analysis highlights the need for a national approach to the assessment, adoption, dissemination and implementation of guidelines, and for critical evaluation of this process. There is a lack of evidence for the efficacy of many GP interventions for improving population health. The research demonstrated examples of collaboration between GPs and other health care providers, and of general practice research using a population health approach. It was concluded that GPEP is an innovative funding program which has the potential to make substantial contributions to population health and to strengthen the links between general practice and population health. To date it has focussed on systematic illness care with current practice providing models for future development. However, there is a need for systematic approaches to data set definition, data collection and guideline use.