Quality Improvement in the Preoperative Evaluation: Accuracy of an Automated Clinical Decision Support System to Calculate CHA2DS2-VASc Scores
| Autor: | Chantal van Giersbergen, Hendrikus H. M. Korsten, Ashley. J. R. De Bie Dekker, Eveline H. J. Mestrom, R. Arthur Bouwman |
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| Přispěvatelé: | EAISI Health, Eindhoven MedTech Innovation Center, Biomedical Diagnostics Lab, Signal Processing Systems, Center for Care & Cure Technology Eindhoven, Electrical Engineering, MUMC+: MA AIOS Anesthesiologie (9), RS: FHML non-thematic output |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
decision support system
Decision Support Systems General Medicine SDG 3 – Goede gezondheid en welzijn Decision Support Systems Clinical CHADS-VASc Risk Assessment Quality Improvement Decision Support Techniques Clinical CHA2DS2-VASc quality perioperative screening SDG 3 - Good Health and Well-being Predictive Value of Tests Risk Factors Atrial Fibrillation Humans Stroke/prevention & control Retrospective Studies |
| Zdroj: | Medicina, 58(9):1269. Multidisciplinary Digital Publishing Institute (MDPI) Medicina; Volume 58; Issue 9; Pages: 1269 Medicina-Lithuania, 58(9):1269. Kauno Medicinos Universitetas |
| ISSN: | 1010-660X |
| Popis: | Background and Objectives: Clinical decision support systems are advocated to improve the quality and efficiency in healthcare. However, before implementation, validation of these systems needs to be performed. In this evaluation we tested our hypothesis that a computerized clinical decision support system can calculate the CHA2DS2-VASc score just as well compared to manual calculation, or even better and more efficiently than manual calculation in patients with atrial rhythm disturbances. Materials and Methods: In n = 224 patents, we calculated the total CHA2DS2-VASc score manually and by an automated clinical decision support system. We compared the automated clinical decision support system with manually calculation by physicians. Results: The interclass correlation between the automated clinical decision support system and manual calculation showed was 0.859 (0.611 and 0.931 95%-CI). Bland-Altman plot and linear regression analysis shows us a bias of −0.79 with limit of agreement (95%-CI) between 1.37 and −2.95 of the mean between our 2 measurements. The Cohen’s kappa was 0.42. Retrospective analysis showed more human errors than algorithmic errors. Time it took to calculate the CHA2DS2-VASc score was 11 s per patient in the automated clinical decision support system compared to 48 s per patient with the physician. Conclusions: Our automated clinical decision support system is at least as good as manual calculation, may be more accurate and is more time efficient. |
| Databáze: | OpenAIRE |
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