Clinical decision support systems for chronic diseases: A Systematic literature review. (October 2020) - Souza-Pereira, Leonice; Pombo, Nuno; Ouhbi, Sofia; Felizardo, Virginie; Garcia, Nuno
Highlights: Most of studies focus on diagnosis of chronic diseases. The representativeness of software engineering stages is scarce and focused on structured and behaviour diagrams. Accuracy was the metric most found in the studies. We suggest a large use of questionnaires as an evaluation technique of user acceptance and satisfaction. We recommend focussing on solving the problem of interoperability of CDSS. Abstract: A Clinical Decision Support System (CDSS) aims to assist physicians, nurses and other professionals in decision-making related to the patient's clinical condition. CDSSs deal with pertinent and critical data, and special care should be taken in their design to ensure the development of usable, secure and reliable tools. Objective: This paper aims to investigate existing literature dealing with the development process of CDSSs for monitoring chronic diseases, analysing their functionalities and characteristics, and the software engineering representation in their design. Methods: A systematic literature review (SLR) is conducted to analyse the literature on CDSSs for monitoring chronic diseases and the application of software engineering techniques in their design. Results: Fourteen included studies revealed that the most addressed disease was diabetes (42.8%) and the most commonly proposed approach was diagnostic (85.7%). Regarding data sources, the studies show a predominance on the use of databases (85.7%), with other data sources such as sensors (42.8%) and self-report (28.6%) also being considered. Analysing the representation for engineering techniques, we found Behaviour diagrams (42.8%) to be the most frequent, closely followed by Structural diagrams (35.7%) and others (78.6%) being largely mentioned. Some studies also approached the requirement specification (21.4%). The most common target evaluation was the performance of the system (64.2%) and the most common metric was accuracy (57.1%). Conclusion : We conclude that software engineering, in its completeness, has scarce representation in studies focused on the development of CDSSs for chronic diseases.