Enhanced QR – Based Emergency Medical Information Retrieval with AI – Assisted Medical Record Summarization

In emergency healthcare, getting the right patient information fast can make all the difference. Doctors and nurses need details like blood type, allergies, current medications, past surgeries, lab results you name it. When a patient has a long and complicated medical history, it can take precious minutes just to find the most important facts. This paper introduces an Enhanced QR-Based Emergency Medical Information Retrieval System that uses AI to help summarize patient records. Here’s how it works: Each patient gets a unique QR code linked to their secure digital profile. When needed, authorized doctors simply scan the code with a web app and instantly access the patient’s medical information. If the records are bulky and full of complex details no problem. The system includes an AI-assisted module that lets doctor input large blocks of text. The AI processes all that data and spits out a summary highlighting what really matters. As for medical images like X-rays, CTs, or MRIs they’re stored and available for viewing, but the AI doesn’t attempt to analyze or summarize them. The paper also compares this QR code-based approach to the usual, slower methods of retrieving medical records. It evaluates how well the system works by looking at the accuracy, completeness, relevance, and clarity of the summaries it generates Keywords— Emergency Healthcare, QR Code Retrieval, AI Summarization, Electronic Health Records, Natural Language Processing, Patient Profiles

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Publication Details

Journal
International Journal of Creative and Open Research in Engineering and Management
Published
2026-10-28
DOI
https://doi.org/10.55041/ijcope.v2i10.184
Primary Topic
Electronic Health Records Systems
Type
article
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article

Enhanced QR – Based Emergency Medical Information Retrieval with AI – Assisted Medical Record Summarization

Prashant Kori, Vedant Tarade, Aditya Sail, Harshal Shelke
1 links
International Journal of Creative and Open Research in Engineering and Management
Electronic Health Records Systems
article

Enhanced QR – Based Emergency Medical Information Retrieval with AI – Assisted Medical Record Summarization

Prashant Kori, Vedant Tarade, Aditya Sail, Harshal Shelke
article en

Abstract

In emergency healthcare, getting the right patient information fast can make all the difference. Doctors and nurses need details like blood type, allergies, current medications, past surgeries, lab results you name it. When a patient has a long and complicated medical history, it can take precious minutes just to find the most important facts. This paper introduces an Enhanced QR-Based Emergency Medical Information Retrieval System that uses AI to help summarize patient records. Here’s how it works: Each patient gets a unique QR code linked to their secure digital profile. When needed, authorized doctors simply scan the code with a web app and instantly access the patient’s medical information. If the records are bulky and full of complex details no problem. The system includes an AI-assisted module that lets doctor input large blocks of text. The AI processes all that data and spits out a summary highlighting what really matters. As for medical images like X-rays, CTs, or MRIs they’re stored and available for viewing, but the AI doesn’t attempt to analyze or summarize them. The paper also compares this QR code-based approach to the usual, slower methods of retrieving medical records. It evaluates how well the system works by looking at the accuracy, completeness, relevance, and clarity of the summaries it generates Keywords— Emergency Healthcare, QR Code Retrieval, AI Summarization, Electronic Health Records, Natural Language Processing, Patient Profiles

International Journal of Creative and Open Research in Engineering and ManagementVol. 02(10)
Good health and well-being
Openalex Percentile: Top 8%
Electronic Health Records Systems
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Enhanced QR – Based Emergency Medical Information Retrieval with AI – Assisted Medical Record Summarization — Prashant Kori, Vedant Tarade, et al. · International Journal of Creative and Open Research in Engineering and Management (2026) | TGRS Research Map | TGRS