Authors:
Wendy Oude Nijeweme-d'Hollosy
1
;
Lex van Velsen
2
;
Remko Soer
3
and
Hermie Hermens
2
Affiliations:
1
University of Twente, Netherlands
;
2
University of Twente, Roessingh Research and Development and Telemedicine cluster, Netherlands
;
3
University of Groningen, University Medical Center Groningen and Saxion University of Applied Science, Netherlands
Keyword(s):
Classification of Patients, Clinical Decision Support Systems (CDSS), Decision Tree, Low Back Pain (LBP), Ontology, Primary Care, Self-referral, Triage.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Clinical Problems and Applications
;
Cloud Computing
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
e-Health
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Healthcare Management Systems
;
Knowledge-Based Systems
;
Platforms and Applications
;
Symbolic Systems
;
Telemedicine
Abstract:
Low back pain (LBP) is the most common cause for activity limitation and has a tremendous socioeconomic impact in Western society. In primary care, LBP is commonly treated by general practitioners (GPs) and physiotherapists. In the Netherlands, patients can opt to see a physiotherapist without referral from their GP (so called ‘self-referral’). Although self-referral has improved the choice of care for patients, it also requires that a patient knows exactly how to select the best next step in care for his or her situation, which is not always evident. This paper describes the design of a web-based clinical decision support system (CDSS) that guides patients with LBP in making suitable choices on self-referral. We studied literature and guidelines on LBP and conducted semi-structured interviews with 3 general practitioners and 5 physiotherapists on the classification of LBP with respect to the best next step in care: visit a GP, visit a physiotherapist or perform self-care. The interv
iew results were validated by means of an online survey, which resulted in a select group of key classification factors. Based on the results, we developed an ontology and a decision tree that models the decision making process of the CDSS.
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