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Distributed application of guideline-based decision support through mobile devices: Implementation and evaluation.

Erez Shalom1, Ayelet Goldstein2, Elior Ariel1

  • 1The Medical Informatics Research Center, Department of Software and Information System Engineering, Ben Gurion University of the Negev, Israel.

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Summary

A new projection-callback model enables distributed decision support systems (DSSs) for remote chronic disease management. This technology allows mobile devices to assist patients at home, improving care accessibility and system robustness.

Keywords:
Clinical decision support systemClinical guidelinesDistributed computingKnowledge engineering

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Area of Science:

  • Health Informatics
  • Medical Decision Support Systems
  • Mobile Health (mHealth)

Background:

  • Traditional guideline-based decision support systems (DSSs) centralize recommendations, limiting patient-centered home care.
  • Home-based patient management offers cost reductions and increased patient empowerment.
  • A distributed DSS architecture is needed to support chronic patients outside clinical settings.

Purpose of the Study:

  • To design, implement, and demonstrate the feasibility of a distributed DSS architecture for mobile devices.
  • To provide patients with evidence-based guidance, personalized alerts, and recommendations for managing chronic conditions at home.
  • To enhance the robustness and distributed application of clinical guidelines (GLs).

Main Methods:

  • Developed a novel projection-callback (PCB) model where guideline knowledge is projected from a central DSS to local DSS on patient mobile devices.
  • Local DSS utilizes mobile resources for personalized therapy plans based on patient preferences and context.
  • Implemented a distributed GL-based DSS within the MobiGuide EU project for automated chronic patient management using mobile sensors.

Main Results:

  • The PCB model proved feasible for specifying and distributing GLs, with significant differences noted between GL specifications for Gestational Diabetes Mellitus and Atrial Fibrillation.
  • The distributed architecture was successfully applied to real-time automated clinical management of patients in Spain and Italy.
  • The system demonstrated robustness, with successful recovery from local DSS crashes and a mean interaction time of 3.95 days (GDM) and 23.80 days (AF).

Conclusions:

  • The projection-callback model is a feasible approach for developing distributed medical DSSs, enabling dynamic delegation of monitoring and treatment decisions to mobile devices.
  • Distributed DSSs facilitate remote chronic patient management, allowing patients to remain at home while retaining benefits of central DSS access.
  • The mechanism ensures access to longitudinal records and up-to-date evidence-based guidelines, enhancing patient care.