Family Practice Vol. 19, No. 5, 442-447
© Oxford University Press 2002
Feasibility of AsthmaCritic, a decision-support system for asthma and COPD which generates patient-specific feedback on routinely recorded data in general practice
a Department of Medical Informatics,
b Family Practice,
c Pediatrics and
d Pulmonology, Erasmus Medical Center, Rotterdam, The Netherlands.
MM Kuilboer, Erasmus Medical Center Rotterdam, Ee2100, PO Box 1738, 3000 DR Rotterdam, The Netherlands; E-mail: kuilboer{at}mi.fgg.eur.nl
Background. Introducing decision-support systems as a tool to stimulate the dissemination of clinical guidelines in daily practice has been disappointing. Researchers have argued that integration of such systems with clinical practice is a prerequisite for acceptance. The big question concerns the feasibility of a true integration—if only routinely recorded data are used for such a system, can patient-specific feedback be produced?
Objective. The aim of this study was to assess the feasibility of generating patient-specific feedback based on routinely recorded data in general practice by AsthmaCritic, a decision-support system for asthma and chronic obstructive pulmonary disease (COPD).
Methods. We built the decision-support system AsthmaCritic and assessed its ability to detect asthma and COPD patient records and generate patient-specific feedback by retrospective analysis of routinely recorded data in 103 713 electronic patient records from primary care practices. We grouped feedback into categories of comments by age group (<12 years and 
12 years). The main outcome measures were the number and percentage of ‘triggered’ (selected) asthma and COPD patient records, and the number and percentage of records on which AsthmaCritic produced at least one feedback comment during the 1-year study period, by category of comments.
Results. AsthmaCritic detected 8784 (8.5%) asthma and COPD patient records. During the study period, AsthmaCritic generated 255 664 feedback comments (mean 3.4 per patient visit). The most frequently generated category of comments in the case of patients aged 12 years was ‘non-compliant prescription’ (23.7%), whereas the most frequent category in the case of patients <12 years was ‘non-compliant route’ (31.1%).
Conclusions. This study shows that, using routinely recorded data only, AsthmaCritic is able to detect asthma and COPD patient records for further analysis and to produce patient-specific feedback.
Keywords. Asthma, COPD, decision-support systems, family practice, guideline adherence.
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