JOURNAL ARTICLE

A Lightweight AI-Powered Doctor's Assistant for Unified Healthcare Data Management and Analytics.

  • Published In: Grenze International Journal of Engineering & Technology (GIJET), 2026, v. 12, n. Part2. P. 2369 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: S., Jenita; K., Kabilan; Sailesh, Gagan; S., Chirag Shastry; G., Prajwal Krishna 3 of 3

Abstract

The digital transformation of healthcare has accelerated in recent years, yet significant challenges remain in integrating medical data, ensuring real-time analytics, and providing effective decision support for clinicians. Conventional hospital information systems (HIS) often operate in silos, leading to inefficiencies, delays, and reduced quality of care. This work presents CareTrack, an AI-powered doctor's assistant and patient management platform designed to integrate multiple healthcare functions into a single lightweight solution. The system includes electronic patient record management, appointment scheduling, analytics dashboards, machine learning-based risk stratification, and conversational chatbot interfaces. Built on a Flask backend, SQLite storage, and modular web frontend, CareTrack is designed for scalability, IoT integration, and cloud readiness. Experimental evaluation using a dataset of 100 patients demonstrates improved performance over baseline HIS, including reduced latency in record retrieval, higher classification accuracy, and better usability for clinicians. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Grenze International Journal of Engineering & Technology (GIJET). 2026/01, Vol. 12, Issue Part2, p2369
  • Document Type:Article
  • Subject Area:Social Sciences and Humanities
  • Publication Date:2026
  • ISSN:23955287
  • Accession Number:192272917
  • Copyright Statement:Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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