An innovative prescription transcription service for assessing workload and prescription variability

Objectives: To evaluate the feasibility and utility of an AI-enabled prescription transcription and translation service delivered through WhatsApp in the Neurology, Neurosurgery, and Cardiology outpatient departments at a public-funded tertiary care hospital. Materials and Methods: This retrospective study analyzed prospectively collected data over a 1-month period (July 2023) at the All India Institute of Medical Sciences (AIIMS), New Delhi. Patients uploaded photographs of their prescriptions via WhatsApp to the GUDMED chatbot (Gud Medicare Private Limited, Gurgaon, Haryana), which used artificial intelligence and machine learning algorithms for prescription transcription. The platform provided optional services including local language translation, dosage reminders, digital health record storage, vital sign tracking, and generic medicine alternatives. Transcribed and translated prescription PDFs were returned to patients through WhatsApp. A total of 500 voluntarily submitted prescriptions were included for analysis. Results: A total of 500 prescriptions were analyzed: 42.8% from Neurosurgery, 33.6% from Cardiology, and 23.6% from Neurology. Male patients predominated across all departments. The median age of patients was 31 years (interquartile range [IQR]: 24.5–48.5) in Neurosurgery, 34 years (IQR: 26–69.25) in Cardiology, and 34 years (IQR: 22.5–42.5) in Neurology. Overall, 47.2% of patients requested Hindi translations, with the highest proportion from Neurosurgery (58.1%), followed by Cardiology (25.4%) and Neurology (16.5%). Diagnostic tests were frequently advised: in Neurosurgery, magnetic resonance imaging (MRI) (26.2%), CT (4.7%), and X-ray (9.3%); in Cardiology, MRI (1.2%), computed tomography (CT) (4.2%), X-ray (8.9%), and echocardiography (ECHO) (18.5%); in Neurology, MRI (9.3%), CT (11.9%), X-ray (2.5%), and ECHO (4.2%). Diabetes and hypertension were most common in Cardiology (20.2%). Antiseizure was the most prescribed drug class, accounting for 17% of prescriptions, mainly in Neurology and Neurosurgery. Conclusion: The AI-based prescription transcription and translation service effectively improved communication, patient engagement, and digital record-keeping in public hospitals. Its scalability makes it a promising solution for advancing digital healthcare, especially in low-resource settings.