Structuring prescribing data into traffic-light categories; a tool for evaluating treatment quality in primary care

Background. Prescribing feedback based on aggregated data alone does not gi ve the information needed to improve treatment quality. Objectives. Our aim was to develop anew method, or tool, of presenting pres cribing feedback which, combined with guideline recommendations, makes it p ossible for doctors to judge their own prescribing as good or bad. Methods. Asthma was chosen as a disease model, as treatment recommendations are readily available published as national and international guidelines. Four mean daily dosage intervals of inhaled short-acting beta -agonists and four mean daily dosage intervals of inhaled steroids were combined into a 4 x 4 matrix. This matrix of 16 combined dosage boxes was presented to 68 N orwegian GPs participating in peer review groups. As a first step, the GPs in the groups reached consensus on what they considered to be appropriate a nd inappropriate combined dosage intervals of these drugs based on national guideline recommendations and their joint clinical experience. Accordingly , traffic-light colours, green and red, were assigned to the combined dosag e boxes in the matrix. Treatments in boxes difficult to judge were coloured yellow. During a 1-year period prior to the consensus meetings, the dispen sed inhaled short-acting beta -agonists and inhaled steroids of each of the doctors' patients were recorded at the local pharmacies. As a second step in developing the new method, the number of patients treated within each of the coloured boxes was presented to the GPs in the peer review groups. The se combined presentations provided an overview to the whole group, and indi vidually to each GP, of how many patients were actually given appropriate o r inappropriate treatment according to their own agreed upon standard. Results. The GPs categorized 34% of 1122 evaluated patients receiving inhal ed short-acting beta -agonists or inhaled steroids as treated inappropriate ly during the 1-year registration period. Appropriate treatment was given t o 47% of the patients, and in 19% of the cases the treatment was difficult to evaluate. Conclusions. A method has been developed enabling GPs to categorize prescri bing information into good (green), bad (red) and difficult to judge (yello w) treatment qualities, based on guideline recommendations and clinical exp erience. The actual prescribing data for each GP were labelled according to the same colour scheme, thus revealing to each GP his or her own actual pr escribing compared with their own treatment standard, yielding information and motivation for quality improvement efforts.