DECISION MAKING IN CHILDHOOD ASTHMA EXACERBATION – A CLINICAL JUDGMENT ANALYSIS
John Jenkins, MD1, Mike Shields1, Chris Patterson2, and Frank Kee2. (1) Queen's University Belfast, Antrim BT41 2RL, United Kingdom, (2) Queen's University Belfast, Belfast BT12 6BJ, United Kingdom
Purpose: Important clinical decisions are made for children during acute asthma attacks by individual doctors on the basis of their knowledge and experience. These include the administration of systemic corticosteroids (CS) or oral antibiotics, and admission to hospital. Clinical judgment analysis provides a methodology for examining such decisions and comparing them between practitioners with different training and experience, thus providing a basis for improved decision-making. Methods: Stepwise linear regression analysis was used to select clinical cues based on visual analogue score assessments of the propensity of 62 clinicians to prescribe a short course of oral CS (decision 1), a course of antibiotics (decision 2) and/or admit to hospital (decision 3) for 60 ‘paper' patients. Results: For decision 1, when compared by specialty, the models derived for pediatricians were significantly more likely to include the presence of cyanosis (54% v. 16%). For decision 2, the models derived for pediatricians were significantly more likely to include presence of crepitations as a cue (49% v. 16%) and significantly less likely to include inhaled CS (8% v. 40%), respiratory rate (0% v. 24%) and air entry (70% v. 100%). For decision 3, compared to other grades, the models derived for consultants/general practitioners were significantly more likely to include wheeze severity as a cue (39% v. 6%). Conclusions: Clinicians differed both in their use of individual cues and in the number of cues included in their models. Specialist training and the effectiveness of guidelines and care pathways will benefit from clarification of these issues both as general learning points, and also by enabling individuals to develop self-awareness of their own decision-making preferences.