Clinical decision support systems for chronic diseases: A Systematic literature review.

Chronic disease Clinical decision support systems Software engineering,

Journal

Computer methods and programs in biomedicine
ISSN: 1872-7565
Titre abrégé: Comput Methods Programs Biomed
Pays: Ireland
ID NLM: 8506513

Informations de publication

Date de publication:
Oct 2020
Historique:
received: 17 02 2020
revised: 24 04 2020
accepted: 21 05 2020
pubmed: 2 6 2020
medline: 15 5 2021
entrez: 2 6 2020
Statut: ppublish

Résumé

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. 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. 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. 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%). We conclude that software engineering, in its completeness, has scarce representation in studies focused on the development of CDSSs for chronic diseases.

Identifiants

pubmed: 32480191
pii: S0169-2607(20)30396-5
doi: 10.1016/j.cmpb.2020.105565
pii:
doi:

Types de publication

Journal Article Review Systematic Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

105565

Informations de copyright

Copyright © 2020 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they do not have any financial or nonfinancial conflict of interests

Auteurs

Leonice Souza-Pereira (L)

Instituto Federal do Triângulo Mineiro - Campus Uberlândia Centro, Brasil; Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal. Electronic address: leonice.pereira@ubi.pt.

Nuno Pombo (N)

Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal.

Sofia Ouhbi (S)

Computer Science and Software Engineering Department, CIT, UAE University, UAE.

Virginie Felizardo (V)

Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal.

Nuno Garcia (N)

Instituto de Telecomunicações, Lisboa, Portugal; Universidade da Beira Interior, Covilhã, Portugal.

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Classifications MeSH