Title : A personalized precision decision support system for chair side adverse drug interaction prevention in dental clinics
Abstract:
The rapid expansion of the global pharmaceutical market poses significant, multifaceted challenges for clinical prescribing, particularly for geriatric dental patients managing polypharmacy and complex systemic conditions. At the point-of-care, dentists face immense clinical pressure to evaluate vast, fast-evolving drug data to prevent dangerous adverse drug interactions (ADIs) during routine dental procedures or complex surgical interventions. While general drug interaction databases exist, they operate as rigid reference systems that lack real-time customization tailored to an individual patient's comprehensive medical, systemic, and allergy profile. To effectively address this gap, this doctoral work introduces a robust, patient-centric clinical decision support system (CDSS) specifically optimized for dental workflows. The system evaluates prospective dental prescription safety by cross-referencing intended treatments with the patient’s active medications and known allergies. Utilizing advanced data mining and machine learning techniques on a vast biomedical corpus, the system computes the precise likelihood of an ADI based on the textual descriptions characterizing drug pairs.
This mechanism operates seamlessly within a proposed conceptual three-layer framework. First, the Knowledge Layer aggregates foundational drug data from established databases like DrugBank. Second, the Prediction Layer utilizes sophisticated network approaches and word embedding models to extract and determine deep semantic similarities from high-dimensional feature vectors. Finally, the user-friendly Presentation Layer facilitates efficient clinical data entry and displays real-time, actionable risk assessments directly to the practitioner. A primary innovation of this research is its direct integration with existing Electronic Health Records (EHR) and patient databases. By automatically pulling the active drug profiles already stored in the patient's electronic database, the system eliminates time-consuming manual lookups. This creates a personalized precision decision support system that dramatically boosts a dentist's clinical efficiency during chair-side treatment without disrupting the appointment timeline.
Empirical experiments validated the core hypothesis, demonstrating that complex drug interactions strongly correlate with semantic similarities derived from their respective feature vectors, which led to the successful deployment of this framework as a specialized decision support tool in active dental clinics. An evaluative survey conducted among practicing dentists revealed highly positive feedback. Participants reported that the system significantly enhanced prescribing accuracy, maximized chair-side workflow speed, improved overall patient treatment outcomes, and was exceptionally intuitive to integrate into daily dental practice. Ultimately, utilizing drug interaction data mining within a personalized decision support system provides a highly scalable platform to optimize drug prescribing, successfully transforming clinical workflows at the point-of-care within the healthcare domain.
