Using decision support for population tracking of adherence to recommended asthma guidelines

OBJECTIVE Decision support systems linked to administrative databases provide a unique opportunity to monitor adherence to guidelines and target disease management strategies towards patients not receiving guideline-based therapy. The objective of this study was to evaluate the discrepancy between actual asthma treatments prescribed by primary care physicians compared to those recommended by evidence-based guidelines using a decision support tool linked to a provincial health administrative database. DESIGN The drug and medical services information of individuals with asthma was identified from the provincial health database and was pushed through an asthma decision support system (ADSS). Recommendations aimed at optimising asthma treatment were generated on two index dates, 15 September 2007 (index date 1) and 15 March 2008 (index date 2). SETTING Primary care settings in a large Canadian metropolitan area. PARTICIPANTS Individuals with asthma and provincial health insurance primary and secondary outcome measures: well controlled asthma. RESULTS 16 803 eligible individuals were identified on index date 1, and 18 103 on index date 2. The distribution of recommendation categories was similar on both index dates. 94% were classified as well controlled and 7% as not well controlled. Among well-controlled individuals, the largest proportion was in the maintain treatment category (63.8%), followed by the maintain/decrease treatment category (28.2%) and the decrease treatment category (2.7%). Almost all individuals who were not well controlled had the recommendation to increase treatment (88%) with a small proportion in the refer category (1%). CONCLUSIONS The ADSS was able to identify subgroups of patients from an administrative database that could benefit from a medication review and possible change. Decision support systems linked to an administrative database can be used to identify individuals with uncontrolled asthma or prescriptions that deviate from recommended treatment. When connected to the point of care, this can provide an opportunity for physicians to intervene early.