Using an Electronic Clinical Decision Support System to Reduce the Risk of Epidural Hematoma

2014 ◽  
Vol 21 (5) ◽  
pp. 327-330 ◽  
Author(s):  
Rajnish K. Gupta
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Epidural Hematoma ◽  
Clinical Decision ◽  
Electronic Clinical Decision Support

scholarly journals Results from one-year use of an electronic Clinical Decision Support System in a post-conflict context: An implementation research

PLoS ONE ◽  
2019 ◽  
Vol 14 (12) ◽  
pp. e0225634 ◽  
Author(s):  
Andrea Bernasconi ◽  
Francois Crabbé ◽  
Ajibola Margret Adedeji ◽  
Attahiru Bello ◽  
Torsten Schmitz ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Implementation Research ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Post Conflict ◽  
One Year ◽  
Electronic Clinical Decision Support

scholarly journals Electronic Clinical Decision Support System for allergic rhinitis management: MASK e‐CDSS

2018 ◽  
Vol 48 (12) ◽  
pp. 1640-1653 ◽  
Author(s):  
Anne‐Lise Courbis ◽  
Ruth Brigid Murray ◽  
Sylvie Arnavielhe ◽  
Davide Caimmi ◽  
Anna Bedbrook ◽  
...  
Keyword(s):  
Allergic Rhinitis ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Electronic Clinical Decision Support

1309-P: Utilizing a Clinical Decision Support System for Hypoglycemia Management

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision

A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh
Keyword(s):  
Decision Support ◽  
Cardiovascular Diseases ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
K Value ◽  
Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

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