scholarly journals Pediatricians’ Understanding and Experiences of an Electronic Clinical-Decision-Support-System

2017 ◽  
Vol 9 (3) ◽  
Author(s):  
Per Nydert ◽  
Anikó Vég ◽  
Pia Bastholm-Rahmner ◽  
Synnöve Lindemalm
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Dose Range ◽  
Dose Calculation ◽  
Clinical Decision ◽  
Structured Interviews ◽  
Range Check

Objectives: Subsequent dosing errors after implementing an Electronic Medical Record (EMR) at a pediatric hospital in Sweden led to the development, in close collaboration with the clinical profession, of a Clinical Decision Support System (CDSS) with Dose Range Check and Weight Based Dose Calculation integrated directly in the EMR. The aim of this study was to explore the understanding and experiences of the CDSS among Swedish pediatricians after one year of practice.Methods: Semi-structured interviews with physicians at different levels of the health care system were performed with seventeen pediatricians working at three different pediatrics wards in Stockholm County Council. The interviews were analysed with a thematic analysis without pre-determined categories.Results: Six categories and fourteen subcategories emerged from the analysis. The categories included the use, the benefit, the confidence, the situations of disregards, the misgivings/risks and finally the development potential of the implemented CDSS with Weight Based Dose Calculation and Dose Range Check.  Conclusions:  A need for CDSS in the prescribing for children is evident but also the need for further development based on the practical knowledge of the clinical profession.

Non-user physician perspectives about an oncology clinical decision-support system: A qualitative study.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e14061-e14061
Author(s):  
Hermano Alexandre Lima Rocha ◽  
Srinivas Emani ◽  
Carlos Andre Moura Arruda ◽  
Rubina Rizvi ◽  
Pamela Garabedian ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Social Contexts ◽  
Lessons Learned ◽  
Structured Interviews ◽  
Extensive Experience

e14061 Background: Advances in artificial intelligence (AI) continue to expand capabilities within the healthcare domain, particularly in the discipline of oncology. Watson For Oncology (WfO) is an AI-enabled clinical decision support system that presents potential therapeutic options for cancer-treating physicians. The objectives of this study were to identify non-user physicians’ expectations, perceived challenges and benefits of WfO use in Brazil. Methods: The study took place at Instituto do Câncer do Ceará (ICC), a Brazilian oncology hospital that implemented WfO in December 2017, but not all physicians adopted the tool. Physicians who had not used WfO (n = 5) were recruited through purposive sampling identified with the assistance of local research personnel. Semi-structured interviews were conducted in Portuguese and later de-identified and transcribed into English. A thematic analysis of interview data based on grounded theory by two members of the research team with extensive experience in qualitative data analysis was conducted. Results: Non-user physicians had positive perceptions about WfO, along with several concerns and uncertainties. They expected that WfO would be easy to learn, useful, and helpful. Physicians perceived that WfO would provide a more standardized approach to treatment than care without it. They also believed that WfO would play a supportive and not a substitute role in care especially for complex cases in which the physicians had more in-depth knowledge of a patient and already had an established patient-provider relationship. Physicians did expect WfO use to negatively impact productivity, specifically through longer office times per patient because of the need to enter data and review recommendations. Physicians questioned whether the use of WfO would negatively impact their autonomy and role in providing care. Finally, physicians also questioned whether the treatment suggested by WfO would fit the social context of a low-middle income country such as Brazil with limited technological and economic resources. Conclusions: The implementation of US-developed AI technologies, such as WfO, should be further explored in different social and economic contexts. Physician concerns about productivity and autonomy need to be assessed and addressed in AI implementation; one strategy is to leverage previous lessons learned from electronic health record (EHR) implementations. This study is a critical step in understanding potential user perspectives in adopting a new AI tool in different social contexts.

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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