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Personalized Assistance for Patients with Chronic Diseases Through Multi-Level Distributed Healthcare Process

Liubov Elkhovskaya1, Maxim Kabyshev1, Anastasia Funkner1

  • 1ITMO University, Saint Petersburg, Russia.

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Summary
This summary is machine-generated.

This study improves remote patient monitoring for chronic diseases using a unified medical information system. Predictive modeling and a mobile app enable early detection of patient health changes.

Keywords:
Chronic diseasedata integrationmedical information systemmobile applicationout-patientstelemedicine

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Area of Science:

  • Health Informatics
  • Digital Health
  • Medical Information Systems

Background:

  • Managing chronic diseases requires continuous monitoring and efficient healthcare delivery in distributed environments.
  • Existing healthcare systems face challenges in large-scale, population-level patient data management.
  • The need for integrated solutions to improve patient outcomes and healthcare processes is critical.

Purpose of the Study:

  • To assess and enhance complex informational, healthcare, and medical processes for chronic disease management in distributed settings.
  • To develop a large-scale, unified medical information system for population-level healthcare.
  • To evaluate the efficacy of predictive modeling and mobile applications in remote patient monitoring.

Main Methods:

  • Development of a complex unified medical information system for population-level data.
  • Implementation of predictive modeling for remote patient state detection.
  • Design and utilization of an experimental mobile application for data collection and user interaction.

Main Results:

  • Experimental studies demonstrate that predictive modeling can unify the detection of remote patients' states.
  • Self-measuring devices and distributed electronic health records are effective data sources.
  • The mobile application facilitates data collection and enhances user-system interaction for chronic disease management.

Conclusions:

  • Unified medical information systems integrated with predictive modeling significantly improve remote chronic disease monitoring.
  • Mobile health applications are valuable tools for patient engagement and data acquisition in chronic care.
  • The developed system offers a scalable solution for population-level health management and early intervention.