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Published on: September 20, 2018
Clinical data extraction and feedback in general practice: a case study from Australian primary care
Peter Schattner1, Mary Saunders, Leslie Stanger
1Monash Division of General Practice, East Bentleigh, Victoria, Australia. Peter.Schattner@monash.edu
Informatics in Primary Care
|March 15, 2011
Summary
General practitioners (GPs) can use clinical data extraction tools to identify areas for practice improvement. External support from divisions is crucial for overcoming technical barriers and improving data quality in general practice.
Area of Science:
- General Practice
- Health Informatics
- Quality Improvement
Background:
- Quality improvement in general practice increasingly relies on clinical database analysis.
- General practitioners (GPs) require motivation and skills for effective clinical data extraction and review.
- This study explores the initial experiences of 15 practices in data extraction and management with divisional support.
Purpose of the Study:
- To investigate the adoption of data extraction tools in general practice settings.
- To understand the role of general practice divisions in supporting tool uptake.
- To identify challenges and facilitators in using clinical data for quality improvement.
Main Methods:
- A single division of general practice in Melbourne, Australia, participated.
- Fifteen self-selected practices received a free data extraction tool and ongoing divisional support.
- Practice staff received training, and divisional staff collated extracted clinical data.
Main Results:
- Participating practices focused on data entry, patient file management, demographics, diabetes, and coronary heart disease (CHD) care.
- Data recording was often incomplete; for example, many diabetes patients lacked HbA1c records, and CHD patients had suboptimal aspirin/statin use.
- Smoking status was not recorded for nearly half of recent attendees.
Conclusions:
- Data extraction tools are valuable for GPs to identify clinical practice issues.
- External support from divisions significantly aids the uptake and effectiveness of these tools.
- Technical barriers and incomplete data entry impede progress, yet interest in using clinical data for improvement remains high.
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