A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools
A Microservice Architecture for the Design of
Computer-Interpretable Guideline Processing
Martin Chapman and Vasa Curcin
King’s College London
Clinical Practice Guidelines (CPGs)
• ‘Systematically developed statements to assist practitioner and
patient decisions about appropriate health care for speciﬁc
clinical circumstances’ .
Computer-Interpetable Guidelines (CIGs)
• Representations of CIGs that are computer processable.
Decision-support systems (DSSs) and CIG processing tools.
A (clinical) decision support system (DSS) is designed to provide a
clinician, or patient, with recommendations in order to assist with
clinical decision making .
The output of CIG processing tools are a natural data source for DSSs.
• First, formalise medical knowledge.
• Autonomously invoke processing on guidelines, and use the
Geared for human interaction.
• No autonomous interaction.
No standard communication between CIG formalisms (stores) and
• Data redundancy.
• Limited interactivity across different tools.
Difﬁcult technical deployment and limited resilience and scalability.
A microservice architecture separates the features of a system into
individual services .
Microservices are often also RESTful (Representational State
Transfer), which means that the functionality of the systems they
contain can be invoked using URIs that are deﬁned as a part of that
service’s Application Programming Interface (API) .
A Microservice Architecture for Guideline Processing Tools i
1. Interaction: exposes the functionality offered by the tool.
2. Reasoner: encapsulates the tool’s reasoner.
3. Store: encapsulates the software required to store CPG
1. Create guideline sets.
2. Add or delete new guideline information or knowledge.
3. Retrieve guideline information.
4. Invoke processing.
A Microservice Architecture for Guideline Processing Tools ii
Clinician GUI Decision-making Interaction Reasoner Store
Addresses these issues. How?
Tools designed under this architecture...
Increase interoperability through well-deﬁned services and
Facilitate autonomous communication by providing RESTful
endpoints with machine-processable responses.
Are resilient by having an interaction proxy to the reasoner.
Scale, by allowing components of the system, such as the reasoner,
that may receive heavy load to be replicated.
Integrate with other processing tools also designed under the
• Same store, multiple reasoners; reusability.
• Different stores, same reasoner; technological heterogeneity.
Case Study: Interaction tool redesign. i
Redesigning Zamborlini et al’s guideline interaction tool.
Web Interface Prolog Reasoner GuidelinesService
Store Store API API Guidelines
Apache Jena Fuseki
Case Study: Interaction tool redesign. ii
Table 1: Endpoints for the reconﬁgured interaction tool
Microservice Endpoints Instantiation
Reasoner /guidelines/.+ -
Store /guideline/.+ -
Case Study: DSS Integration
The redesigned guideline interaction tool has been integrated with
1. CONSULT, assisting stroke patients in self-managing their
2. ROAD2H, providing access to healthcare in low and
middle-income countries: http://www.road2h.org/.
Calls are made to the tool by the DSS’s decision-making component.
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