AI Article Synopsis
- The study focuses on helping general medical practitioners (GMPs) accurately diagnose and refer dental pain patients to avoid antibiotic overuse and resistance.
- Five focus groups of GMPs in urban Queensland discussed the challenges of recognizing various dental issues and found that about 90% appreciated the practicality of a newly developed treatment algorithm.
- The algorithm aims to streamline the referral process and prescription practices, ensuring GMPs efficiently manage dental complaints without unnecessary complications.
Article Abstract
Objective. The differential diagnosis of pain in the mouth can be challenging for general medical practitioners (GMPs) as many different dental problems can present with similar signs and symptoms. This study aimed to create a treatment algorithm for GMPs to effectively and appropriately refer the patients and prescribe antibiotics. Design. The study design is comprised of qualitative focus group discussions. Setting and Subjects. Groups of GMPs within the Gold Coast and Brisbane urban and city regions. Outcome Measures. Content thematically analysed and treatment algorithm developed. Results. There were 5 focus groups with 8-9 participants per group. Addressing whether antibiotics should be given to patients with dental pain was considered very important to GMPs to prevent overtreatment and creating antibiotic resistance. Many practitioners were unsure of what the different forms of dental pains represent. 90% of the practitioners involved agreed that the treatment algorithm was useful to daily practice. Conclusion. Common dental complaints and infections are seldom surgical emergencies but can result in prolonged appointments for those GMPs who do not regularly deal with these issues. The treatment algorithm for referral processes and prescriptions was deemed easily downloadable and simple to interpret and detailed but succinct enough for clinical use by GMPs.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4947654 | PMC |
| http://dx.doi.org/10.1155/2016/4760672 | DOI Listing |
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