Introduction
Seasonal rhinoconjunctivitis due to pollen allergy (SAR) affects millions of people around the globe and is particularly prevalent among children1. Symptom-relieving drugs can control the disease, but the only disease-modifying treatment with long-term effects is an allergen-specific immunotherapy (AIT)2,3. The efficacy of AIT depends on the precise identification of the eliciting pollen inducing IgE sensitization and triggering the patient’s symptoms4-6. Unfortunately, pinning down the causing allergen is often difficult, especially in Southern European countries, as patients are frequently sensitized to multiple, often cross-reactive, allergenic sources with overlapping pollination seasons7.
This diagnostic challenge can be confronted with the use of component resolved diagnostics (CRD) in order to identify the eliciting allergen and thereby choose the proper agent for an allergen-specific immunotherapy. Corresponding algorithms on the molecular diagnosis of allergies have been published8-10. However, a traditional approach, based exclusively on anamnesis and the use of pollen extracts, is still the most frequently used worldwide3 and the implementation of molecular diagnostic algorithms – still considered a complex matter by most doctors - is infrequent10. Expert systems and software solutions have been proposed as tools to make the adoption of diagnostic algorithms for CRD easier11. However, to our knowledge, no informatics tool dedicated to support the implementation of internationally validated algorithms is yet available.
In contrast, a great variety of mobile phone applications has flooded the market, aiming at an improved disease control and quality of life for allergic patients. Unfortunately, only a small number of applications has been clinically validated, but especially in the area of real-time symptom monitoring, the usefulness of mobile devices has been proven12-16. Though in the daily clinical practice still most of the patient´s history is being assessed retrospectively, the electronic clinical diary (eDiary) enables the doctor to evaluate individual symptoms and the need for medication prospectively. With the help of software systems, clinical scores can be automatically generated, graphically matching patients´ SMS trajectories with those of the local pollen counts16,17.
The opportunity of mobile Health (mHealth) technology is being used not only to record patients´ data, but also as part of clinical decision support systems (CDSS), created to assist patients, clinicians and pharmacists at the point of care18-21. In allergology, several support systems have been created, related to symptom monitoring and a facilitated diagnosis of respiratory allergies21-22. In order to create a support tool for the precise prescription of AIT, we identified a diagnostic algorithm based on the use of CRD and eDiaries in combination with local pollen counts as potentially efficient and user-friendly tools to be included in a future clinical decision support system. The purpose of the present study is to assess the effectiveness and usability of this algorithm and its individual tools between two groups of allergy specialists (AS) and general practitioners (GP) in order to facilitate their clinical decision taking with regard to AIT prescription.