scholarly journals Proposal for Development of EBM-CDSS (Evidence-based Clinical Decision Support System) to Aid Prognostication in Terminally Ill Patients

2012 ◽  
Author(s):  
Benjamin Djulbegovic
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Terminally Ill ◽  
Clinical Decision ◽  
Evidence Based ◽  
Terminally Ill Patients

Clinical Decision Support System for the Activity of Evidence based Computation

2020 ◽  
pp. 50-57
Author(s):  
Ali Кhusein ◽  
Urquhart A
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Healthcare Services ◽  
Future Research ◽  
Evidence Based

The application of the Clinical Decision Support Systems (CDSS) in the process of facilitating the activity of the evidence-centred treatment project effect enhances the quality of the healthcare services. The main purpose of this article is to define and illustrate the basis of the processes of the evidencecentred decision support tracking at the two thousand AMIA symposium spring. The analysis has been done on the basis of protocol issues when capturing the evidence-centred practices in machine interpretation and repositories for supporting and developing the CDSS for evidence-centred treatment. As a result, the research recommendations are based on five areas: capturing literature-centered and practice-centred evidence in the interpretation of machine knowledge and bases; creating maintainable methodological and technical elements for computer-centred decision support CDSS; assessing the medical costs and effects for clinical decision support system and the manner in which the systems affect the organizational best practices; disseminating and identifying the works based on work-flow sensitivity approach for the system and creating the public policy which will effectively provide the incentives meant to implement CDSS to enhance the quality of healthcare services. The paper is concluded with an assumption of evidence-based medicine aspect being strong. However, future research is still recommended in CDSS to potentially realize more defined benefits of the systems.

Promoting evidence-based management of anemia in cancer patients: Background and scientific validation of RESPOND, a web-based clinical decision support system

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 19676-19676
Author(s):  
J. Van Erps ◽  
M. Aapro ◽  
K. MacDonald ◽  
P. Soubeyran ◽  
M. Muenzberg ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Discriminant Validity ◽  
Intraclass Correlation ◽  
Clinical Decision ◽  
Evidence Based ◽  
Support Tool

19676 Background: The 2006 European Organisation for Research and Treatment of Cancer (EORTC) guidelines for erythropoietic proteins in cancer-related anemia provide the most up-to-date assessment of the evidence base. To promote clinicians’ adoption of evidence- based (EB) practice guidelines (EBPGs), it is critical to bring guidelines to the point of care. RESPOND is an EB clinical decision support system (CDSS) based on the EORTC guidelines. CDSSs are seldom validated. We describe the methodologies of two studies being conducted to validate RESPOND. Methods. Study 1: descriptive design - accuracy and content validity. Five experts are asked to rate the accuracy of algorithms derived from the guidelines; the objective being an intraclass correlation coefficient =0.90 for each of 27 algorithmic sets. Study 2: hybrid matched pre-post design - concurrent and discriminant validity. Two patient cohorts (n=33 each) matched by type of cancer and similarity of chemotherapy regimen ie, sample 1 (4 months prospective data after RESPOND [post]) and sample 2 (4 months retrospective data prior to RESPOND [pre]) were used to test concurrent validity (congruence scores [CS] of sample 1) and discriminant validity (difference between sample 2 and sample 1 CS). A score is calculated for each patient to quantify the extent to which treatment and outcomes are congruent with the EORTC EBPG. Table 1 . Calculation of the congruence score ESA, erythropoiesis-stimulating agent; Hb, hemoglobin, Q1W, once weekly, Q3W, once every three weeks Conclusions. These studies will provide the necessary validation for RESPOND as an evidence-based clinical support tool. [Table: see text] [Table: see text]

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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