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A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools



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A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools

  1. 1. A Microservice Architecture for the Design of Computer-Interpretable Guideline Processing Tools Paper: 08828 Martin Chapman and Vasa Curcin King’s College London
  2. 2. Terminology i Clinical Practice Guidelines (CPGs) • ‘Systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances’ [3]. Computer-Interpetable Guidelines (CIGs) • Representations of CIGs that are computer processable. 1
  3. 3. Terminology ii CIG formalisms • GLIF3 [5], Proforma [7] and GLARE [8]. CIG reasoners • Interactions [9]. • Patient personalisation [6]. CIG formalism + CIG reasoner = CIG processing tool. 2
  4. 4. 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 [2]. 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 results. 3
  5. 5. Integration challenges Geared for human interaction. • No autonomous interaction. No standard communication between CIG formalisms (stores) and reasoners. • Data redundancy. • Limited interactivity across different tools. Difficult technical deployment and limited resilience and scalability. 4
  6. 6. RESTful Microservices A microservice architecture separates the features of a system into individual services [4]. 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) [1]. 5
  7. 7. A Microservice Architecture for Guideline Processing Tools i Services 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 information. Endpoints 1. Create guideline sets. 2. Add or delete new guideline information or knowledge. 3. Retrieve guideline information. 4. Invoke processing. 6
  8. 8. A Microservice Architecture for Guideline Processing Tools ii Clinician GUI Decision-making Interaction Reasoner Store Input Send Store Confirm Confirm Confirm Trigger Call Retrieve Guidelines Process Response Response Process Addresses these issues. How? 7
  9. 9. Tools designed under this architecture... Increase interoperability through well-defined services and endpoints. 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 architecture • Same store, multiple reasoners; reusability. • Different stores, same reasoner; technological heterogeneity. 8
  10. 10. Case Study: Interaction tool redesign. i Redesigning Zamborlini et al’s guideline interaction tool. Web Interface Prolog Reasoner GuidelinesService SWISH Interaction Interaction API (JS Server) Reasoner Reasoner API (Prolog Server) Prolog Reasoner Store Store API API Guidelines Apache Jena Fuseki 9
  11. 11. Case Study: Interaction tool redesign. ii Table 1: Endpoints for the reconfigured interaction tool Microservice Endpoints Instantiation Interaction /guideline/create/ N /guideline/add/ N /guideline/drug/add Y /guideline/transition/add Y /guideline/belief/add Y /guideline/.+/delete N /guideline/drug/get Y /guideline/drug/effect/get Y /guidelines/interactions Y Reasoner /guidelines/.+ - Store /guideline/.+ - 10
  12. 12. Case Study: DSS Integration The redesigned guideline interaction tool has been integrated with two DSSs: 1. CONSULT, assisting stroke patients in self-managing their treatments: 2. ROAD2H, providing access to healthcare in low and middle-income countries: Calls are made to the tool by the DSS’s decision-making component. 11
  13. 13. References i R. T. Fielding. Architectural styles and the design of network-based software architectures. Ph.d. thesis, University of California Irvine, 2000. D. L. Hunt, R. B. Haynes, S. E. Hanna, and K. Smith. Effects of Computer-Based Clinical Decision Support Systems on Physician Performance and Patient Outcomes. JAMA, 280(15):1339, oct 1998. Institute of Medicine. Clinical Practice Guidelines: Directions for a New Program. Technical Report 8, Institute of Medicine, 1990. 12
  14. 14. References ii S. Newman. Building Microservices. O’Reilly Media, Inc., 1st edition, 2015. M. Peleg, A. A. Boxwala, O. Ogunyemi, Q. Zeng, S. Tu, R. Lacson, E. Bernstam, N. Ash, P. Mork, L. Ohno-Machado, E. H. Shortliffe, and R. A. Greenes. GLIF3: the evolution of a guideline representation format. AMIA Symposium, pages 645–9, 2000. D. Riaño, F. Real, J. A. López-Vallverdú, F. Campana, S. Ercolani, P. Mecocci, R. Annicchiarico, and C. Caltagirone. An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. JBI, 45(3):429–446, 2012. 13
  15. 15. References iii D. R. Sutton and J. Fox. The Syntax and Semantics of the PROforma Guideline Modeling Language. JAMIA, 10(5):433–443, sep 2003. P. Terenziani, P. Terenziani, S. Montani, S. Montani, A. Bottrighi, A. Bottrighi, M. Torchio, M. Torchio, G. Molino, G. Molino, G. Correndo, and G. Correndo. The GLARE approach to clinical guidelines: Main features. In Studies in HTI, volume 101, pages 162–166, 2004. V. Zamborlini, J. Wielemaker, M. Da Silveira, C. Pruski, A. Ten Teije, and F. Van Harmelen. SWISH for prototyping clinical guideline interactions theory. In CEUR, volume 1795, 2016. 14