Stefania Arasi

and 14 more

Background: Allergen immunotherapy(AIT) is the only disease-modifying treatment with long-term effects in patients with seasonal allergic rhinoconjunctivitis(SAR). Its efficacy depends on the precise identification of the pollen triggering symptoms. However, a diagnostic approach based on retrospective clinical history and sensitization to extracts often does not lead to unequivocal results. Objectives: To assess the usability and impact of a recently established algorithm for a potential clinical decision support system (@IT.2020-DSS) for pollen allergy and its diagnostic steps (including anamnesis, SPT, component resolved diagnosis, CRD, and real-time digital symptom recording, eDiary) on doctor’s AIT prescription decisions. Methods: After a concise educational training on the @IT.2020-DSS algorithm, 46 doctors (18allergy specialists, AS, and 28general practitioners, GP) expressed their hypothetical AIT prescription for 10 clinical index cases. Decisions were recorded repeatedly based on different steps of the support algorithm. The usability and perceived impact of the algorithm on individual clinical performance were evaluated. Results: The combined use of CRD and an eDiary increased the hypothetical AIT prescriptions, both among AS and GP (p<.01). AIT prescription based on anamnesis and SPT were heterogeneous but converged towards a consensus after the integration of CRD and eDiary information. Doctors considered the algorithm useful and recognized its potential in enhancing traditional diagnostics. Conclusions: The implementation of CRD and eDiary in the @IT2020-DSS algorithm improved consensus on hypothetical AIT prescription for SAR among AS and GP. The hypothesis, that a CDSS for etiological SAR diagnosis and AIT prescription may be useful in real-life clinical practice deserves further investigations.