An Offline Smartphone Check for Drug Interactions and Past Reactions in Handwritten Prescriptions: A Technical Feasibility Study in Rural Bangladeshi Outpatient Care
TL;DR
An offline Android application that photographs a handwritten prescription, detects and reads each medicine line, resolves brand names to active ingredients, and retrieves, but never predicts, documented interactions and the patient's recorded reactions is built and evaluated.
Abstract
Rural Bangladeshi outpatient clinics keep no patient records, prescriptions are handwritten, and physicians have under a minute per patient, so they easily miss a known adverse drug reaction (ADR) or drug-drug interaction (DDI). We built and evaluated an offline Android application that photographs a handwritten prescription, detects and reads each medicine line, resolves brand names to active ingredients, and retrieves, but never predicts, documented interactions and the patient's recorded reactions. This quantitative technical-feasibility study, with qualitative error analysis, involved no patients or clinicians. Reference data combine two Bangladeshi drug registries (27,880 brands) with 190,421 DDI pairs. A YOLOv8n line detector reached mAP@0.5 = 0.687 on held-out prescriptions but recalled only 60.6% of lines at the deployed cutoff. On-device ML Kit read 11.8% of 780 benchmark handwritten words exactly. We trained a CRNN-CTC reader on public handwriting data, decoded it against the brand lexicon, and fine-tuned it on 660 author-labelled prescription crops. On benchmark words, the reader accepted the correct ingredient for 41.5% and a wrong one for 4.2%, versus 21.0% and 5.3% for ML Kit at its preset cutoff (McNemar p<10^-20). On scorable lines from held-out real prescriptions, the reader accepted the correct ingredient for 36.0% [26.8, 46.3] and a wrong one for 2.2% [0.6, 7.8]. In 62 simulated runs on a mid-range phone, median time per patient was 26-31 s and 59 of 62 runs finished within 60 s, but 62% of medicine lines still needed a manual check. A cross-visit medication history added after the evaluation passed seven automated tests and one two-visit functional test but was not evaluated. The pipeline is feasible within the clinic workflow; reading handwriting, not retrieval, is the bottleneck. Nothing here demonstrates a clinical benefit.
