Illustration of Clinical Decision Support System Development Complexity.

Clinical decision support systems software development process software engineering software lifecycle

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
26 Jun 2020
Historique:
entrez: 2 7 2020
pubmed: 2 7 2020
medline: 15 7 2020
Statut: ppublish

Résumé

The dissemination of Clinical Decision Support Systems (CDSS) in the medical field is slow due to various reasons such as lacking comprehensibility, low user acceptance, specific problem settings and unique application environments. This paper presents an illustration of the complexity of the CDS development process. Guided by procedural software development models already known from the field of software engineering, we developed a CDS-specific software lifecycle and a CDS development complexity illustration. We based the results on literature research of publicated field reports about successfully developed CDSS. We identified important CDS peculiarities related to the development and documentation process of CDSS and later merged them with generic software engineering models. We then created a CDS complexity illustration that can be used to structure future research in the CDS field as well as any standardisation processes.

Identifiants

pubmed: 32604651
pii: SHTI200544
doi: 10.3233/SHTI200544
doi:

Types de publication

Journal Article

Langues

eng

Pagination

261-264

Auteurs

Jendrik Richter (J)

Department of Medical Informatics, University Medical Center Göttingen, Germany.

Stefan Vogel (S)

Department of Medical Informatics, University Medical Center Göttingen, Germany.

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages

Exploring blood-brain barrier passage using atomic weighted vector and machine learning.

Yoan Martínez-López, Paulina Phoobane, Yanaima Jauriga et al.
1.00
Blood-Brain Barrier Machine Learning Humans Support Vector Machine Software
Primary Health Care Electronic Health Records Humans Tanzania Surveys and Questionnaires
Cephalometry Humans Anatomic Landmarks Software Internet

Classifications MeSH