Next-Generation Healthcare: Design and Implementation of a Smart Medicine Vending System

There are several serious issues associated with traditional pharmacy system, such as 24/7 hours availability, accessibility, long waiting time, difficult to maintain accurate records on the medicines, high operational and running cost, human errors in the process of giving out the medicine and restricted hours of operation. These issues call for an effective remedy that would address the complexity of medication dispensing. These challenges are likely to be tackled by the proposed project known as Smart Prescription Medicine Vending Machine (SPMVM). The SPMVM employs OCR for prescription scanning and transcription, voice recognition for users to speak the name of the medication, and an automated intelligent graphical user interface for prescription selection by the user which makes it more efficient and user friendly than traditional technology. A fine motor-spring mechanism is also applied in order to guarantee that medicine is dispense accurately and an intelligent stock check system to manage stock quantity with expiry periods, an indication when more stock is needed. The hardware design contains a separate platform for pill dispensing, huge storage capacity, an intelligent stock replenishment system, a user-friendly and vibrant graphic interface and a vocal coded assistant for customers. With a central microprocessor controlling all components, the SPMVM integrates telemedicine to enable rapid, accurate medication dispensing within seconds, 24/7. The proposed system achieved a 99% accuracy on recognition when distinguishing between textual data gathered from scanned prescription photos with an average of 0.75 seconds to exactly transcribe prescription photos to text and dispensing the accurate medicine within 0.6 seconds.

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Publication Details

Published
2024-10-15
DOI
https://doi.org/10.1109/hite63532.2024.10777201
Citations
5
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
3.54
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article

Next-Generation Healthcare: Design and Implementation of a Smart Medicine Vending System

Aneela Rehman, Mohammad Shahidul Islam, Saqib Javed, Aashir Waleed et al.
5 citations
Artificial Intelligence in Healthcare
3.54
article

Next-Generation Healthcare: Design and Implementation of a Smart Medicine Vending System

Aneela Rehman, Mohammad Shahidul Islam, Saqib Javed, Aashir Waleed, Muhammad Zubair, Farhan Jalees Ahmad
article en
5 citations

Abstract

There are several serious issues associated with traditional pharmacy system, such as 24/7 hours availability, accessibility, long waiting time, difficult to maintain accurate records on the medicines, high operational and running cost, human errors in the process of giving out the medicine and restricted hours of operation. These issues call for an effective remedy that would address the complexity of medication dispensing. These challenges are likely to be tackled by the proposed project known as Smart Prescription Medicine Vending Machine (SPMVM). The SPMVM employs OCR for prescription scanning and transcription, voice recognition for users to speak the name of the medication, and an automated intelligent graphical user interface for prescription selection by the user which makes it more efficient and user friendly than traditional technology. A fine motor-spring mechanism is also applied in order to guarantee that medicine is dispense accurately and an intelligent stock check system to manage stock quantity with expiry periods, an indication when more stock is needed. The hardware design contains a separate platform for pill dispensing, huge storage capacity, an intelligent stock replenishment system, a user-friendly and vibrant graphic interface and a vocal coded assistant for customers. With a central microprocessor controlling all components, the SPMVM integrates telemedicine to enable rapid, accurate medication dispensing within seconds, 24/7. The proposed system achieved a 99% accuracy on recognition when distinguishing between textual data gathered from scanned prescription photos with an average of 0.75 seconds to exactly transcribe prescription photos to text and dispensing the accurate medicine within 0.6 seconds.

University of Engineering and Technology Lahore (PK)
Industry, innovation and infrastructure
Openalex Percentile: Top 6%
Artificial Intelligence in Healthcare
3.54
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