TY - JOUR
T1 - Clinical decision support systems for chronic diseases
T2 - A Systematic literature review
AU - Souza-Pereira, Leonice
AU - Pombo, Nuno
AU - Ouhbi, Sofia
AU - Felizardo, Virginie
AU - Garcia, Nuno
N1 - Funding Information:
The first author likes to acknowledge to IFTM - Instituto Federal do Triângulo Mineiro.
Funding Information:
This work was funded by FCT/MEC through national funds and when applicable co-funded by FEDER—PT2020 partnership agreement under the project UIDB/EEA/50008/ 2020. This article is based upon work from COST Action IC1303-AAPELE-Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA162 26-SHELD-ON-Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). COST (European Cooperation in Science and Technology) is a funding agency for research and innovation networks. Our Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation. More information in www.cost.eu . The first author was supported by incentive grant “Bolsa BID / UBI-Santander Universidades / 2018”.
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Chronic disease
KW - Clinical decision support systems
KW - Software engineering
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U2 - 10.1016/j.cmpb.2020.105565
DO - 10.1016/j.cmpb.2020.105565
M3 - Review article
C2 - 32480191
AN - SCOPUS:85085524486
SN - 0169-2607
VL - 195
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 105565
ER -