Development of a Cloud-Based Clinical Decision Support System for Ophthalmology Triage Using Decision Tree Artificial Intelligence.

Purpose: Clinical decision support systems (CDSS) are an emerging frontier in teleophthalmology, drawing on heuristic decision making to augment processes such as triage and referral. We describe the development and implementation of a novel cloud-based decision tree CDSS for on-call ophthalmology consults. The objective was to standardize the triage and referral process while providing a more accurate provisional diagnosis and urgency. Design: Prospective comparative cohort study. Subjects: On-call referrals to a Canadian community ophthalmology clinic. Methods: A web-based decision tree algorithm was developed using current guidelines and expert opinion. The algorithm collected tailored information on the patient's ophthalmic concern, and outputted a provisional diagnosis and urgency before sending an electronic referral to the on-call ophthalmology clinic. Data were described using descriptive statistics. Spearman-rho correlations and Cohen's kappa coefficient were used to characterize the observed relationships. Post hoc analysis was conducted using analysis of contingency tables and adjusted residuals. Main Outcome Measures: Diagnostic category, provisional diagnosis, and urgency for the referring provider, CDSS, and ophthalmologist. Results: Ninety-six referrals were processed. Referring providers included medical doctors (76.0%, n = 73), optometrists (20.8%, n = 20), and nurse practitioners (3.1%, n = 3). The CDSS (κ = 0.5898; 95% confidence interval [CI], 0.4868-0.6928; P < 0.0001) performed equally well with 66.7% agreement in determining category when compared with referring providers (κ = 0.5880; 95% CI, 0.4798-0.6961; P < 0.0001). The CDSS (agreement = 53.1%; κ = 0.4999; 95% CI, 0.4021-0.5978; P < 0.0001) performed better than referring providers (agreement = 43.8%; κ = 0.4191; 95% CI, 0.3194-0.5188; P < 0.0001) in determining a diagnosis. The CDSS (ρ = 0.5014; 95% CI, 0.3092-0.6935; P < 0.0001) also performed better than referring providers (ρ = 0.4035; 95% CI, 0.2406-0.5665; P < 0.0001) in determining urgency. The CDSS assigned a lower level of urgency in 22 cases (22.9%) compared with referring providers in 6 cases (6.3%). Conclusions: To our knowledge, this is the first cloud-based CDSS in ophthalmology designed to augment the triage and referral process. The CDSS achieves a more accurate diagnosis and urgency, standardizes information collection, and overcomes antiquated paper-based consults. Future directions include developing a random forest model or integrating convolutional neural network-based machine learning to refine the speed and accuracy of triage and referral processes, with emphasis on increasing sensitivity of the CDSS.

Creation and cost-evaluation of a student-run podcast in ophthalmology.

Podcasts are an increasingly popular medical education modality, especially in surgical fields. However, the cost of developing a high-quality medical education podcast presents a barrier to many content creators. The authors developed the podcast series 'The Lenspod,' designed to be a cost-efficient but high-quality education resource in ophthalmology. The REC financial framework has been previously used to estimate the financial costs of technology-based medical education. Using this framework, costs were competitive with other medical education podcasts. It is our hope that similar methodology may be used to create and disseminate future podcasts for medical education.

Les balados sont une modalité d'enseignement médical de plus en plus populaire, en particulier dans les domaines chirurgicaux. Cependant, le coût de création d'un balado éducatif de qualité en médecine constitue un obstacle pour de nombreux créateurs de contenu. Les auteurs sont les créateurs de la série de balados The Lenspod, qui se veut une ressource éducative à la fois rentable et de qualité en ophtalmologie. Appliquant le cadre financier REC, déjà utilisé pour estimer les coûts financiers de modes d'enseignement médical basés sur la technologie, nous avons constaté que les coûts de notre balado sont compétitifs par rapport à d'autres en éducation médicale. Nous espérons qu'une méthode similaire sera utilisée pour créer et diffuser davantage de balados éducatifs en médecine.