Pulikottil Wilson Vinny1, Aastha Takkar2, Vivek Lal2, Madakasira Vasantha Padma3, P N Sylaja4, Lakshmi Narasimhan5, Sada Nand Dwivedi6, Pradeep P Nair7, Thomas Iype8, Anu Gupta9, Venugopalan Y Vishnu10. 1. Neurology, Indian Naval Hospital Ship, Asvini, Mumbai, Maharashtra, India. 2. Neurology, Postgraduate Institute of Medical Education and Research, Chandigarh, India. 3. Neurology; Neurosciences Centre, All India Institute of Medical Sciences, New Delhi, India. 4. Neurology, Sree Chitra Tirunal Institute of Medical Sciences and Technology, Thiruvananthapuram, Kerala, India. 5. Neurology, Madras Medical College, Chennai, Tamil Nadu, India. 6. Biostatistics, All India Institute of Medical Sciences, New Delhi, India. 7. Neurology, Jawaharlal Nehru Institute of Postgraduate Medical Education and Research, Puducherry, India. 8. Neurology, Government Medical College Trivandrum, Kerala, India. 9. Neurology, Govind Ballabh Pant Institute of Postgraduate Medical Education and Research, New Delhi, India. 10. Neurology, All India Institute of Medical Sciences, New Delhi, India.
Abstract
Purpose: Drawing differential diagnoses to a Neuro-ophthalmology clinical scenario is a difficult task for a neurology trainee. The authors conducted a study to determine if a mobile application specialized in suggesting differential diagnoses from clinical scenarios can complement clinical reasoning of a neurologist in training. Methods: A cross-sectional multicenter study was conducted to compare the accuracy of neurology residents versus a mobile medical app (Neurology Dx) in drawing a comprehensive list of differential diagnoses from Neuro-ophthalmology clinical vignettes. The differentials generated by residents and the App were compared with the Gold standard differential diagnoses adjudicated by experts. The prespecified primary outcome was the proportion of correctly identified high likely gold standard differential diagnosis by residents and App. Results: Neurology residents (n = 100) attempted 1500 Neuro-ophthalmology clinical vignettes. Frequency of correctly identified high likely differential diagnosis by residents was 19.42% versus 53.71% by the App (P < 0.0001). The first listed differential diagnosis by the residents matched with that of the first differential diagnosis adjudicated by experts (gold standard differential diagnosis) with a frequency of 26.5% versus 28.3% by the App, whereas the combined output of residents and App scored a frequency of 41.2% in identifying the first gold standard differential correctly. The residents correctly identified the first three and first five gold standard differential diagnosis with a frequency of 17.83% and 19.2%, respectively, as against 22.26% and 30.39% (P < 0.0001) by the App. Conclusion: A ruled based app in Neuro-ophthalmology has the potential to complement a neurology resident in drawing a comprehensive list of differential diagnoses.
Purpose: Drawing differential diagnoses to a Neuro-ophthalmology clinical scenario is a difficult task for a neurology trainee. The authors conducted a study to determine if a mobile application specialized in suggesting differential diagnoses from clinical scenarios can complement clinical reasoning of a neurologist in training. Methods: A cross-sectional multicenter study was conducted to compare the accuracy of neurology residents versus a mobile medical app (Neurology Dx) in drawing a comprehensive list of differential diagnoses from Neuro-ophthalmology clinical vignettes. The differentials generated by residents and the App were compared with the Gold standard differential diagnoses adjudicated by experts. The prespecified primary outcome was the proportion of correctly identified high likely gold standard differential diagnosis by residents and App. Results: Neurology residents (n = 100) attempted 1500 Neuro-ophthalmology clinical vignettes. Frequency of correctly identified high likely differential diagnosis by residents was 19.42% versus 53.71% by the App (P < 0.0001). The first listed differential diagnosis by the residents matched with that of the first differential diagnosis adjudicated by experts (gold standard differential diagnosis) with a frequency of 26.5% versus 28.3% by the App, whereas the combined output of residents and App scored a frequency of 41.2% in identifying the first gold standard differential correctly. The residents correctly identified the first three and first five gold standard differential diagnosis with a frequency of 17.83% and 19.2%, respectively, as against 22.26% and 30.39% (P < 0.0001) by the App. Conclusion: A ruled based app in Neuro-ophthalmology has the potential to complement a neurology resident in drawing a comprehensive list of differential diagnoses.
Entities:
Keywords:
Clinical decision support system; differential diagnosis; medical education; neuro-ophthalmology
Authors: Leonard Knoedler; Helena Baecher; Martin Kauke-Navarro; Lukas Prantl; Hans-Günther Machens; Philipp Scheuermann; Christoph Palm; Raphael Baumann; Andreas Kehrer; Adriana C Panayi; Samuel Knoedler Journal: J Clin Med Date: 2022-08-25 Impact factor: 4.964