TY - JOUR
T1 - Quality Improvement in the Preoperative Evaluation
T2 - Accuracy of an Automated Clinical Decision Support System to Calculate CHA2DS2-VASc Scores
AU - van Giersbergen, Chantal
AU - Korsten, Hendrikus H M
AU - De Bie Dekker, Ashley J R
AU - Mestrom, Eveline H J
AU - Bouwman, R Arthur
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/9/13
Y1 - 2022/9/13
N2 - 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.
AB - 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.
KW - Atrial Fibrillation
KW - Decision Support Systems, Clinical
KW - Decision Support Techniques
KW - Humans
KW - Predictive Value of Tests
KW - Quality Improvement
KW - Retrospective Studies
KW - Risk Assessment
KW - Risk Factors
KW - Stroke/prevention & control
U2 - 10.3390/medicina58091269
DO - 10.3390/medicina58091269
M3 - Article
C2 - 36143945
SN - 1010-660X
VL - 58
JO - Medicina-Lithuania
JF - Medicina-Lithuania
IS - 9
M1 - 1269
ER -