Abstract

Modern clinical practices for treating pathologies are underpinned by evidence-based clinical knowledge, which is usually defined and formalized in a textual format using clinical practice guidelines (CPGs). This textual formalization causes a certain level of ambiguity, subjective interpretation of the clinical recommendations to be suggested and actions to be performed, and variability in clinical practice by different healthcare professionals facing similar clinical circumstances. This paper presents an industrial experience (GIMO-PD project) that proposes to improve formalization and reduce the clinical variability that exists when CPGs are followed by healthcare professionals during their professional activity. Specifically, this paper proposes a domain specific language for modelling CPGs without ambiguity, as well as model-driven and tool-supported mechanisms for the development of Web applications from a CPG model. At present, our proposal is being developed and validated in a real scenario of patients with Parkinson's disease.

Recommended Citation

Enamorado-Díaz, E., García García, J. A., de la Riva, C., & Ruiz, M. (2022). A Model-driven and Tool-supported Proposal for Defining Automatic Clinical Practice Guidelines . In R. A. Buchmann, G. C. Silaghi, D. Bufnea, V. Niculescu, G. Czibula, C. Barry, M. Lang, H. Linger, & C. Schneider (Eds.), Information Systems Development: Artificial Intelligence for Information Systems Development and Operations (ISD2022 Proceedings). Cluj-Napoca, Romania: Babeș-Bolyai University.

COinS
 

A Model-driven and Tool-supported Proposal for Defining Automatic Clinical Practice Guidelines

Modern clinical practices for treating pathologies are underpinned by evidence-based clinical knowledge, which is usually defined and formalized in a textual format using clinical practice guidelines (CPGs). This textual formalization causes a certain level of ambiguity, subjective interpretation of the clinical recommendations to be suggested and actions to be performed, and variability in clinical practice by different healthcare professionals facing similar clinical circumstances. This paper presents an industrial experience (GIMO-PD project) that proposes to improve formalization and reduce the clinical variability that exists when CPGs are followed by healthcare professionals during their professional activity. Specifically, this paper proposes a domain specific language for modelling CPGs without ambiguity, as well as model-driven and tool-supported mechanisms for the development of Web applications from a CPG model. At present, our proposal is being developed and validated in a real scenario of patients with Parkinson's disease.