Effective Use of Clinical Decision Support in Critical Care: Using Risk Assessment Framework for Evaluation of a Computerized Weaning Protocol

2016 ◽  
pp. 217-232 ◽  
Author(s):  
Sahiti Myneni ◽  
Debra McGinnis ◽  
Khalid Almoosa ◽  
Trevor Cohen ◽  
Bela Patel ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Critical Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Assessment Framework ◽  
Effective Use

Effects of clinical decision support on venous thromboembolism risk assessment, prophylaxis, and prevention at a university teaching hospital

2010 ◽  
Vol 67 (15) ◽  
pp. 1265-1273 ◽  
Author(s):  
William L. Galanter ◽  
Mathew Thambi ◽  
Holly Rosencranz ◽  
Bobby Shah ◽  
Suzanne Falck ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Support ◽  
Venous Thromboembolism ◽  
Clinical Decision Support ◽  
Teaching Hospital ◽  
Clinical Decision ◽  
University Teaching

scholarly journals Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1728
Author(s):  
Goran Medic ◽  
Melodi Kosaner Kließ ◽  
Louis Atallah ◽  
Jochen Weichert ◽  
Saswat Panda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Respiratory Distress ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Hemodynamic Instability ◽  
Evidence Based

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

Effective use of a clinical decision-support system to advance antimicrobial stewardship

2012 ◽  
Vol 69 (18) ◽  
pp. 1543-1544 ◽  
Author(s):  
Jean Patel ◽  
John S. Esterly ◽  
Marc H. Scheetz ◽  
Maureen K. Bolon ◽  
Michael J. Postelnick
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Antimicrobial Stewardship ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Effective Use

scholarly journals Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

2016 ◽  
Vol 25 (S 01) ◽  
pp. S103-S116 ◽  
Author(s):  
D. F. Sittig ◽  
A. Wright ◽  
B. Middleton
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Technology Implementation ◽  
Information Technologies ◽  
Healthcare Delivery ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Clinical Workflow ◽  
Health Maintenance ◽  
Effective Use

Summary Objective: The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040. Method: Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.Result: In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery. Conclusion: CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.

scholarly journals Provider Survey on Automated Clinical Decision Support for Cardiovascular Risk Assessment

2019 ◽  
Vol 3 (1) ◽  
pp. 23-29
Author(s):  
Alisha P. Chaudhry ◽  
Sujith Samudrala ◽  
Francisco Lopez-Jimenez ◽  
Jane L. Shellum ◽  
Rick A. Nishimura ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Decision Support ◽  
Cardiovascular Risk ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Cardiovascular Risk Assessment ◽  
Provider Survey

scholarly journals A New Real Time Clinical Decision Support System Using Machine Learning for Critical Care Units

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 185676-185687
Author(s):  
Noha Ossama El-Ganainy ◽  
Ilangko Balasingham ◽  
Per Steinar Halvorsen ◽  
Leiv Arne Rosseland
Keyword(s):  
Machine Learning ◽  
Critical Care ◽  
Decision Support ◽  
Decision Support System ◽  
Real Time ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Critical Care Units

scholarly journals Reinforcement Learning for Clinical Decision Support in Critical Care: Comprehensive Review (Preprint)

2020 ◽  
Author(s):  
Siqi Liu ◽  
Kay Choong See ◽  
Kee Yuan Ngiam ◽  
Leo Anthony Celi ◽  
Xingzhi Sun ◽  
...  
Keyword(s):  
Critical Care ◽  
Reinforcement Learning ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Evaluation Metrics ◽  
Comprehensive Review ◽  
Drug Dosing ◽  
Personalized Care ◽  
Clinical Environments

BACKGROUND Decision support systems based on reinforcement learning (RL) have been implemented to facilitate the delivery of personalized care. This paper aimed to provide a comprehensive review of RL applications in the critical care setting. OBJECTIVE This review aimed to survey the literature on RL applications for clinical decision support in critical care and to provide insight into the challenges of applying various RL models. METHODS We performed an extensive search of the following databases: PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, Web of Science, Medical Literature Analysis and Retrieval System Online (MEDLINE), and Excerpta Medica Database (EMBASE). Studies published over the past 10 years (2010-2019) that have applied RL for critical care were included. RESULTS We included 21 papers and found that RL has been used to optimize the choice of medications, drug dosing, and timing of interventions and to target personalized laboratory values. We further compared and contrasted the design of the RL models and the evaluation metrics for each application. CONCLUSIONS RL has great potential for enhancing decision making in critical care. Challenges regarding RL system design, evaluation metrics, and model choice exist. More importantly, further work is required to validate RL in authentic clinical environments.

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