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Updated: Jan 5, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Microservice chatbot architecture for chronic patient support.

Surya Roca¹, Jorge Sancho¹, José García¹

¹Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain.

Journal of Biomedical Informatics

|October 18, 2019

Summary

This study introduces a scalable chatbot architecture for chronic disease management, utilizing microservices, HL7 FHIR, and AIML for improved patient support and data integration. The system enhances virtual assistant development for long-term health conditions.

Keywords:

Artificial Intelligence Markup Language (AIML)Chronic patient support Fast Healthcare Interoperability Resources (FHIR)Medical chatbot Messaging platforms Microservice architecture

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Last Updated: Jan 5, 2026

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Software Architecture

Background:

Chatbots offer significant potential for supporting patients with chronic diseases and comorbidities.
Effective chatbot functionality requires a robust and adaptable software architecture.
Existing solutions may lack scalability, interoperability, or flexible conversation modeling.

Purpose of the Study:

To introduce a novel chatbot architecture designed for chronic patient support.
To enhance patient data management and interaction through standardized models.
To demonstrate the flexibility and dynamic improvement capabilities of the proposed architecture.

Main Methods:

Developed a microservices-based architecture for scalability.
Integrated HL7 FHIR for standard data sharing and AIML for conversation modeling.
Created an automated FHIR-to-AIML conversion mechanism and utilized standard messaging platforms for interaction.

Main Results:

The architecture supports scalability, interoperability, and flexible conversation design.
An automated FHIR-to-AIML conversion facilitates data gathering for patient health records.
A prototype for psoriasis demonstrated dynamic and flexible chatbot component management.

Conclusions:

The proposed architecture offers a flexible and scalable solution for developing chronic patient support chatbots.
Standardized data and conversation models improve chatbot integration and functionality.
This approach significantly enhances the development of virtual assistants for chronic disease management.