scholarly journals ELECTROPHYSICAL AGENTS - Contraindications And Precautions: An Evidence-Based Approach To Clinical Decision Making In Physical Therapy

2010 ◽  
Vol 62 (5) ◽  
pp. 1-80 ◽  
Keyword(s):  
Decision Making ◽  
Physical Therapy ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Evidence Based

Debates in Dysphagia Management: How Do You Use Evidence-Based Practice in Your Dysphagia Patient Care?

2011 ◽  
Vol 20 (4) ◽  
pp. 121-123
Author(s):  
Jeri A. Logemann
Keyword(s):  
Decision Making ◽  
Patient Care ◽  
Clinical Outcomes ◽  
Clinical Judgment ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Clinical Decision ◽  
Evidence Based ◽  
Clinical Method ◽  
Do So

Evidence-based practice requires astute clinicians to blend our best clinical judgment with the best available external evidence and the patient's own values and expectations. Sometimes, we value one more than another during clinical decision-making, though it is never wise to do so, and sometimes other factors that we are unaware of produce unanticipated clinical outcomes. Sometimes, we feel very strongly about one clinical method or another, and hopefully that belief is founded in evidence. Some beliefs, however, are not founded in evidence. The sound use of evidence is the best way to navigate the debates within our field of practice.

HEALTH TECHNOLOGY ASSESSMENT AND CLINICAL DECISION MAKING:

1999 ◽  
Vol 15 (3) ◽  
pp. 585-592 ◽  
Author(s):  
Alicia Granados
Keyword(s):  
Health Care ◽  
Decision Making ◽  
Health Technology Assessment ◽  
Case Studies ◽  
Technology Assessment ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Health Technology ◽  
Evidence Based ◽  
Systematic Process

This paper examines the rationality of the concepts underlying evidence—based medicineand health technology assessment (HTA), which are part of a new current aimed at promoting the use of the results of scientific studies for decision making in health care. It describes the different approaches and purposes of this worldwide movement, in relation to clinical decision making, through a summarized set of specific HTA case studies from Catalonia, Spain. The examples illustrate how the systematic process of HTA can help in several types of uncertainties related to clinical decision making.

scholarly journals The PICO strategy for the research question construction and evidence search

2007 ◽  
Vol 15 (3) ◽  
pp. 508-511 ◽  
Author(s):  
Cristina Mamédio da Costa Santos ◽  
Cibele Andrucioli de Mattos Pimenta ◽  
Moacyr Roberto Cuce Nobre
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Evidence Based Practice ◽  
Scientific Evidence ◽  
Research Question ◽  
Clinical Decision ◽  
Evidence Based ◽  
Review Of The Literature

Evidence based practice is the use of the best scientific evidence to support the clinical decision making. The identification of the best evidence requires the construction of an appropriate research question and review of the literature. This article describes the use of the PICO strategy for the construction of the research question and bibliographical search.

Oxytocin versus carbetocin administered to cows after uncomplicated calvings and subsequent fertility

2016 ◽  
Vol 179 (7) ◽  
pp. 175-176
Author(s):  
Natalie Robinson ◽  
Marnie Brennan
Keyword(s):  
Decision Making ◽  
Veterinary Medicine ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Evidence Based ◽  
Subsequent Fertility ◽  
The University

BestBETs for Vets are generated by the Centre for Evidence-based Veterinary Medicine at the University of Nottingham to help answer specific questions and assist in clinical decision making. Although evidence is often limited, they aim to find, present and draw conclusions from the best available evidence, using a standardised framework. A more detailed description of how BestBETs for Vets are produced was published in VR, April 4, 2015, vol 176, pp 354-356.

Integrating research and evidence-based guidance into prescribing practice

2021 ◽  
Vol 3 (3) ◽  
pp. 120-123
Author(s):  
Adam Bedson
Keyword(s):  
Decision Making ◽  
Critical Appraisal ◽  
Clinical Decision Making ◽  
Primary Source ◽  
Clinical Decision ◽  
Evidence Base ◽  
Evidence Based ◽  
Multiple Sources ◽  
Prescribing Practice ◽  
Critical Appraisal Skills

The College of Paramedics and the Royal Pharmaceutical Society are clear that they require advanced paramedics, as non-medical prescribers, to review and critically appraise the evidence base underpinning their prescribing practice. Evidence-based clinical guidance such as that published by the National Institute for Health and Care Excellence (NICE) is recommended as the primary source of evidence on which paramedics should base their prescribing decisions. NICE guidance reflects the best available evidence on which to base clinical decision-making. However, paramedics still need to critically appraise the evidence underpinning their prescribing, applying expertise and decision-making skills to inform their clinical reasoning. This is achieved by synthesising information from multiple sources to make appropriate, evidence-based judgments and diagnoses. This first article in the prescribing paramedic pharmacology series considers the importance of evidence-based paramedic prescribing, alongside a range of tools that can be used to develop and apply critical appraisal skills to support prescribing decision-making. These include critical appraisal check lists and research reporting tools

Improving the evidence-based clinical decision-making process: Interactive classification and topic discovery on diabetes-related biomedical literature (Preprint)

2021 ◽  
Author(s):  
Adrian Ahne ◽  
Guy Fagherazzi ◽  
Xavier Tannier ◽  
Thomas Czernichow ◽  
Francisco Orchard
Keyword(s):  
Decision Making ◽  
Active Learning ◽  
Hierarchical Clustering ◽  
Domain Knowledge ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Biomedical Literature ◽  
Decision Making Process ◽  
Evidence Based ◽  
Text Data

BACKGROUND The amount of available textual health data such as scientific and biomedical literature is constantly growing and it becomes more and more challenging for health professionals to properly summarise those data and in consequence to practice evidence-based clinical decision making. Moreover, the exploration of large unstructured health text data is very challenging for non experts due to limited time, resources and skills. Current tools to explore text data lack ease of use, need high computation efforts and have difficulties to incorporate domain knowledge and focus on topics of interest. OBJECTIVE We developed a methodology which is able to explore and target topics of interest via an interactive user interface for experts and non-experts. We aim to reach near state of the art performance, while reducing memory consumption, increasing scalability and minimizing user interaction effort to improve the clinical decision making process. The performance is evaluated on diabetes-related abstracts from Pubmed. METHODS The methodology consists of four parts: 1) A novel interpretable hierarchical clustering of documents where each node is defined by headwords (describe documents in this node the most); 2) An efficient classification system to target topics; 3) Minimized users interaction effort through active learning; 4) A visual user interface through which a user interacts. We evaluated our approach on 50,911 diabetes-related abstracts from Pubmed which provide a hierarchical Medical Subject Headings (MeSH) structure, a unique identifier for a topic. Hierarchical clustering performance was compared against the implementation in the machine learning library scikit-learn. On a subset of 2000 randomly chosen diabetes abstracts, our active learning strategy was compared against three other strategies: random selection of training instances, uncertainty sampling which chooses instances the model is most uncertain about and an expected gradient length strategy based on convolutional neural networks (CNN). RESULTS For the hierarchical clustering performance, we achieved a F1-Score of 0.73 compared to scikit-learn’s of 0.76. Concerning active learning performance, after 200 chosen training samples based on these strategies, the weighted F1-Score over all MeSH codes resulted in satisfying 0.62 F1-Score of our approach, compared to 0.61 of the uncertainty strategy, 0.61 the CNN and 0.45 the random strategy. Moreover, our methodology showed a constant low memory use with increased number of documents but increased execution time. CONCLUSIONS We proposed an easy to use tool for experts and non-experts being able to combine domain knowledge with topic exploration and target specific topics of interest while improving transparency. Furthermore our approach is very memory efficient and highly parallelizable making it interesting for large Big Data sets. This approach can be used by health professionals to rapidly get deep insights into biomedical literature to ultimately improve the evidence-based clinical decision making process.

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