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Could prioritisation by emergency medicine dispatchers be improved by using computer-based decision support?: A cohort of patients with chest pain
University West, Department of Economics and IT, Divison of Law, Economics, Statistics and Politics. (LINA)ORCID iD: 0000-0002-0575-4309
Department of Emergency Medicine, University of Gothenburg, Göteborg, Sweden.
The Pre-hospital Research Centre of Western Sweden, Prehospen, University College of Borås, Borås, Sweden, Department of Medicine, University of Gothenburg, Göteborg, Sweden.
The Pre-hospital Research Centre of Western Sweden, Prehospen, University College of Borås, Borås, Sweden.
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2016 (English)In: International Journal of Cardiology, ISSN 0167-5273, E-ISSN 1874-1754, Vol. 220, 734-738 p.Article in journal (Refereed) Published
Abstract [en]

Background: To evaluate whether a computer-based decision support system could improve the allocation of patients with acute coronary syndrome (ACS) or a life-threatening condition (LTC). We hypothesised that a system of this kind would improve sensitivity without compromising specificity. Methods: A total of 2285 consecutive patients who dialed 112 due to chest pain were asked 10 specific questions and a prediction model was constructed based on the answers. We compared the sensitivity of the dispatchers' decisions with that of the model-based decision support model. Results: A total of 2048 patients answered all 10 questions. Among the 235 patients with ACS, 194 were allocated the highest prioritisation by dispatchers (sensitivity 82.6%) and 41 patients were given a lower prioritisation (17.4% false negatives). The allocation suggested by the model used the highest prioritisation in 212 of the patients with ACS (sensitivity of 90.2%), while 23 patients were underprioritised (9.8% false negatives). The results were similar when the two systems were compared with regard to LTC and 30-day mortality. This indicates that computer-based decision support could be used either for increasing sensitivity or for saving resources. Three questions proved to be most important in terms of predicting ACS/LTC, [1] the intensity of pain, [2] the localisation of pain and [3] a history of ACS. Conclusion: Among patients with acute chest pain, computer-based decision support with a model based on a few fundamental questions could improve sensitivity and reduce the number of cases with the highest prioritisation without endangering the patients.

Place, publisher, year, edition, pages
Amsterdam: Elsevier, 2016. Vol. 220, 734-738 p.
Keyword [en]
Prehospital, Chest pain, ACS; Mortality, Decision support model
National Category
Information Systems, Social aspects Cardiac and Cardiovascular Systems
Research subject
SOCIAL SCIENCE, Informatics
Identifiers
URN: urn:nbn:se:hv:diva-10009DOI: 10.1016/j.ijcard.2016.06.281ISI: 000381582000139ScopusID: 2-s2.0-84979074167OAI: oai:DiVA.org:hv-10009DiVA: diva2:1037678
Available from: 2016-10-17 Created: 2016-10-17 Last updated: 2016-12-16Bibliographically approved

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Gellerstedt, Martin
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Divison of Law, Economics, Statistics and Politics
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