scholarly journals Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients

2019 ◽  
Vol 26 (4) ◽  
pp. 1224-1234 ◽  
Author(s):  
Giovanni Improta ◽  
Valeria Mazzella ◽  
Donatella Vecchione ◽  
Stefania Santini ◽  
Maria Triassi
Keyword(s):  
Fuzzy Logic ◽  
Renal Function ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Post Transplant ◽  
Transplant Patients

scholarly journals 1074. Evaluation of a Pharmacist-led Antimicrobial and Anticoagulant Monitoring Initiative

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S380-S381
Author(s):  
Wei Hsiang Lin ◽  
Amanda Binkley ◽  
Christo L Cimino ◽  
Naasha J Talati ◽  
Jimish M Mehta ◽  
...  
Keyword(s):  
Renal Function ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Dose Adjustment ◽  
Adverse Drug Events ◽  
Medical Center ◽  
Clinical Decision

Abstract Background Adverse drug events are associated with an increase in hospital stay and cost. Risks from these events are minimized by adjusting a medication’s dose or frequency, and changes in renal function may necessitate adjustments. Currently, there is no formal procedure for a prospective audit of renal function over the weekend at our institution. This pharmacist-driven initiative will evaluate if a prospective review identified by real-time clinical decision support alerts over the weekend will reduce the time from change in renal function to dose adjustment of select antimicrobials and/or anticoagulants. Methods This monitoring initiative is comprised of a pre- and post-cohort population. The pre-cohort population included patients admitted to Penn Presbyterian Medical Center (PPMC) from January to March of 2018 on select antimicrobials and/or anticoagulants, who were identified to have a change in renal function (serum creatinine change of 0.3 mg/dL or greater) over the weekend. The post-cohort population was identified with a clinical decision support system (ILÚM Health Solutions, Kenilworth, NJ) and included patients admitted to PPMC from January to March of 2019. A pharmacy resident reviewed alerts in the clinical decision support system over the weekend and contacted providers with dose adjustment recommendations. The Mann–Whitney U test was used to analyze the primary endpoint while descriptive statistics were used for the secondary endpoints Results Eighteen interventions were completed within the 3-month post-cohort intervention period, with a time to dose adjustment between the pre/post-cohort being reduced by 50 hours (P = 0.0001) resulting in a median time to change of 11 hours in the post-cohort. All pharmacy recommendations were accepted by the provider, and 94% of medication adjustments were antimicrobials. Conclusion The application of this prospective weekend initiative utilizing a clinical decision support system demonstrated a clinically and statistically significant reduction in the time to dose adjustments for antimicrobials and/or anticoagulants. Implementation of this initiative will further establish a role for pharmacist-led evaluations and could potentially be expanded to other clinical areas. Disclosures All authors: No reported disclosures.

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.

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