Integrating a Patented AI OCR Technology into Clinical Workflows: Accuracy, Efficiency, and User Satisfaction

Sai Teja Boppiniti , Manaswini Davuluri

Artificial Intelligence; Optical Character Recognition; Human-Centered Design; Healthcare Applications; Accessibility; Case Studies; Intellectual Property; Digital Transformation.

This study evaluated the performance of an AI-powered Optical Character Recognition (OCR) device (UK Design No. 6466471) in digitizing handwritten prescriptions at a mid-sized, 300-bed urban hospital. A prospective observational design was adopted to compare the device’s efficiency, accuracy, and usability against traditional manual data entry. Fifty hospital staff members, including 25 pharmacists, 15 nurses, and 10 administrative personnel, participated in the trial. Over a three-month period, 1,000 handwritten prescriptions were processed through two workflows: manual transcription by pharmacists and digitization using the OCR device integrated with the hospital’s Electronic Health Record (EHR) system. Key parameters assessed included accuracy of digitization, processing time per prescription, error rates, and user satisfaction. The OCR device achieved an accuracy of 95.2%, outperforming manual entry at 87.6%. Median processing time was significantly reduced from 62 seconds manually to 14 seconds with OCR, resulting in a net saving of 13.3 staff hours per 1,000 prescriptions. Error rates decreased markedly, with drug name errors reduced from 6% to 2%, dosage errors from 4% to 1.5%, and missed entries from 2% to 1.3%. User satisfaction was higher with OCR across all staff groups, with pharmacists rating it 4.8/5 compared to 3.1/5 for manual transcription. Qualitative feedback indicated reduced workload burden and greater confidence in patient safety. These findings demonstrate that the AI-powered OCR device significantly enhances efficiency, accuracy, and staff satisfaction in hospital prescription management. Its integration into clinical workflows has the potential to reduce transcription errors, improve patient safety, and support hospital digital transformation.

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"Integrating a Patented AI OCR Technology into Clinical Workflows: Accuracy, Efficiency, and User Satisfaction", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 9, page no.b410-b422, September-2025, Available :https://ijsdr.org/papers/IJSDR2509150.pdf

Volume 10 Issue 9, September-2025

Pages : b410-b422

Paper Reg. ID: IJSDR_305056

Published Paper Id: IJSDR2509150

Downloads: 00053

Research Area: Science and Technology

Country: -, -, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2509150

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2509150

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

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