Development of medical diagnostic decision support systems and their economic efficiency

Кравченко, Ольга Віталіївна

doi:10.15587/2312-8372.2018.128455

Cited by 1 publication

(1 citation statement)

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“…The basic construct includes a box that defines the process and a set of flows useful for giving information about input, output, resources and constraints along the process. IDEFx is also used for clinical pathway process design [25].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Design and Execution of Integrated Clinical Pathway: A Simplified Meta-Model and Associated Methodology

et al. 2020

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Integrated clinical pathways (ICPs) are task-oriented care plans detailing the essential steps of the therapeutic pathway referring to a specific clinical problem with a patient’s expected clinical course. ICPs represent an effective tool for resource management in the public and private health domains. To be automatically executed, the ICP process has to be described by means of complex general purpose description language (GPDL) formalisms. However, GPDLs make the process model difficult to grasp by a human. On the other hand, the adoption of a reduced set of graphical constructs prevents a fully automated process execution due to the lack of information required by a machine. Unfortunately, it is difficult to find a balance between modelling language expressiveness and the automated execution of the modelled processes. In this paper, we present a meta-model based on a GPDL to organize the ICP process knowledge. This meta-model allows the management of ICP information in a way that is independent from the graphic representation of the adopted modelling standard. We also propose a general framework and a methodology that aim to guarantee a high degree of automation in process execution. In particular, the corresponding execution engine is implemented as a chatbot (integrated with social media), which plays a two-fold role: during the actual execution of the entire ICP, it acts as a virtual assistant and gathers the patient’s health data. Tests performed on a real ICP showed that, thanks to the proposed solution, the chatbot engine is able to engage in a dialogue with the patient. We provide discussion about how the system could be extended and how it could be seen as an alternative to Artificial Intelligence (AI) and Natural Language Processing (NLP)-based approaches.

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Section: Theoretical Backgroundmentioning

confidence: 99%