Association Between Clinical Decision Support System Use and Rural Quality Disparities in the Treatment of Pneumonia

2013 ◽  
Vol 30 (2) ◽  
pp. 186-195 ◽  
Author(s):  
Jordan Mitchell ◽  
Janice Probst ◽  
Amy Brock-Martin ◽  
Kevin Bennett ◽  
Saundra Glover ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
System Use

scholarly journals Retrospective analysis of clinical decision support system use in patients with hypertension and atrial fibrillation (INTELLECT)

2021 ◽  
Vol 26 (4) ◽  
pp. 4406
Author(s):  
D. V. Losik ◽  
S. N. Kozlova ◽  
Yu. S. Krivosheev ◽  
A. V. Ponomarenko ◽  
D. N. Ponomarev ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Primary Endpoint ◽  
Clinical Guidelines ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
System Use

Aim. To evaluate the relationship between the clinical decision support system use (CDSS) and adherence to clinical guidelines.Materials and methods. Medical records of 300 patients with atrial fibrillation and hypertension from the electronic medical database of the Almazov National Medical Research Center were analyzed. Demographic and clinical data, as well as information on anticoagulant, antiarrhythmic and antihypertensive prescriptions were analyzed. The primary endpoint was adherence of prescribed treatment to current clinical guidelines for each of the three therapies. Firstly, a group of independent clinical experts assessed primary endpoint for retrospective prescriptions. Secondly, new prescriptions were simulated by another group of clinical experts using CDSS and blinded to previous therapy. Primary endpoint at the second step was analysed by independent experts. We compared adherence to relevant clinical guidelines with and without use of CDSS. Additionally, we analyzed predictors of failing to meet the current recommendations in the retrospective records.Results. Out of 300 patients, only 291 (97%) had all characteristics and were included in the analysis. In 26 patients (18%), all three treatment strategies were in accordance with current clinical guidelines. Anticoagulant therapy was adherent to the guidelines in 92% of cases. Experts who used CDSS were 15% (95% confidence interval [CI], 10-21%) more likely to prescribe novel oral anticoagulants and 14% (95% CI, 10-19%) less likely to prescribe warfarin compared to baseline. Antiarrhythmic therapy was adherent to the guidelines in 69% of cases. When the CDSS platform was applied, experts were 14% (95% CI 4-19%) more likely to prefer antiarrhythmic drug (AAD) monotherapy and 32% (95% CI 26-37%) more often prescribed radiofrequency ablation (RFA) of left atrium. At baseline, antihypertensive therapy combinations were adherent clinical guidelines in 28% of cases. The use of the CDSS platform by experts was significantly associated with an increase in the frequency of prescribing dual and triple antihypertensive therapy.Conclusion. CDSS use is associated with improved adherence to current clinical guidelines. Prospective randomized trials are needed to evaluate the CDSS effectiveness in the prevention of cardiovascular events.

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.

Massive transfusion experience, current practice and decision support: A survey of Australian and New Zealand anaesthetists

2021 ◽  
pp. 0310057X2097403
Author(s):  
Brenton J Sanderson ◽  
Jeremy D Field ◽  
Lise J Estcourt ◽  
Erica M Wood ◽  
Enrico W Coiera
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Response Rate ◽  
Massive Transfusion ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Transfusion Practice

Massive transfusions guided by massive transfusion protocols are commonly used to manage critical bleeding, when the patient is at significant risk of morbidity and mortality, and multiple timely decisions must be made by clinicians. Clinical decision support systems are increasingly used to provide patient-specific recommendations by comparing patient information to a knowledge base, and have been shown to improve patient outcomes. To investigate current massive transfusion practice and the experiences and attitudes of anaesthetists towards massive transfusion and clinical decision support systems, we anonymously surveyed 1000 anaesthetists and anaesthesia trainees across Australia and New Zealand. A total of 228 surveys (23.6%) were successfully completed and 227 were analysed for a 23.3% response rate. Most respondents were involved in massive transfusions infrequently (88.1% managed five or fewer massive transfusion protocols per year) and worked at hospitals which have massive transfusion protocols (89.4%). Massive transfusion management was predominantly limited by timely access to point-of-care coagulation assessment and by competition with other tasks, with trainees reporting more significant limitations compared to specialists. The majority of respondents reported that they were likely, or very likely, both to use (73.1%) and to trust (85%) a clinical decision support system for massive transfusions, with no significant difference between anaesthesia trainees and specialists ( P = 0.375 and P = 0.73, respectively). While the response rate to our survey was poor, there was still a wide range of massive transfusion experience among respondents, with multiple subjective factors identified limiting massive transfusion practice. We identified several potential design features and barriers to implementation to assist with the future development of a clinical decision support system for massive transfusion, and overall wide support for a clinical decision support system for massive transfusion among respondents.

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

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