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CC BY-NC-ND 4.0 · Appl Clin Inform 2023; 14(04): 725-734
DOI: 10.1055/a-2113-4443
Research Article

The Case Manager: An Agent Controlling the Activation of Knowledge Sources in a FHIR-Based Distributed Reasoning Environment

Giordano Lanzola
1   Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
,
Francesca Polce
1   Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
,
Enea Parimbelli
1   Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
,
Matteo Gabetta
2   Research and Development Division, Biomeris S.r.l, Pavia, Italy
,
Ronald Cornet
3   Medical Informatics, Amsterdam Public Health Institute, Methodology & Digital Health, Amsterdam University Medical Centers, Amsterdam, The Netherlands
,
Rowdy de Groot
3   Medical Informatics, Amsterdam Public Health Institute, Methodology & Digital Health, Amsterdam University Medical Centers, Amsterdam, The Netherlands
,
Alexandra Kogan
4   Department of Information Systems, University of Haifa, Haifa, Israel
,
David Glasspool
5   Deontics Ltd., London, United Kingdom
,
Szymon Wilk
6   Research and Development Division, Institute of Computing Science, Poznan University of Technology, Poznan, Poland
,
Silvana Quaglini
1   Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
› Author Affiliations Funding The work described in this article received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation over the years 2020–2024 under grant agreement no. 875052 to the CAPABLE project (https://capable-project.eu).
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Abstract

Background Within the CAPABLE project the authors developed a multi-agent system that relies on a distributed architecture. The system provides cancer patients with coaching advice and supports their clinicians with suitable decisions based on clinical guidelines.

Objectives As in many multi-agent systems we needed to coordinate the activities of all agents involved. Moreover, since the agents share a common blackboard where all patients' data are stored, we also needed to implement a mechanism for the prompt notification of each agent upon addition of new information potentially triggering its activation.

Methods The communication needs have been investigated and modeled using the HL7-FHIR (Health Level 7-Fast Healthcare Interoperability Resources) standard to ensure proper semantic interoperability among agents. Then a syntax rooted in the FHIR search framework has been defined for representing the conditions to be monitored on the system blackboard for activating each agent.

Results The Case Manager (CM) has been implemented as a dedicated component playing the role of an orchestrator directing the behavior of all agents involved. Agents dynamically inform the CM about the conditions to be monitored on the blackboard, using the syntax we developed. The CM then notifies each agent whenever any condition of interest occurs. The functionalities of the CM and other actors have been validated using simulated scenarios mimicking the ones that will be faced during pilot studies and in production.

Conclusion The CM proved to be a key facilitator for properly achieving the required behavior of our multi-agent system. The proposed architecture may also be leveraged in many clinical contexts for integrating separate legacy services, turning them into a consistent telemedicine framework and enabling application reusability.

Keywords

HL7 FHIR - multi-agent systems - clinical decision support - telemedicine

Protection of Human and Animal Subjects

The system described in this paper will be used in two pilot studies performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects. The study was reviewed by the Institutional Review Boards of the two hospitals involved. For ICSM the study was approved on May, 11th 2022 with protocol number 2640CE. For NKI it was approved on Dec, 28th 2022 with protocol number NL81970.000.22 22-981/H-O.


According to the Regulations on Medical Devices (MDR) CAPABLE falls in Risk Class IIa as a software intended to provide information which is used to take decisions with diagnosis or therapeutic purposes. Thus, it requires regular assessment by a notified body. For this reason, two notifications to the Ministry of Health in Italy and to the Central Committee on Research Involving Human Subjects in The Netherlands have been filed.


Supplementary Material



Publication History

Received: 15 November 2022

Accepted: 12 May 2023

Accepted Manuscript online:
20 June 2023

Article published online:
13 September 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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