Use of computer assisted learning technology to enhance clinical decision making skills of physical therapist students in acute care settings

Physiotherapy ◽  
2015 ◽  
Vol 101 ◽  
pp. e273-e274
Author(s):  
E. Costello ◽  
M. Birkmeier ◽  
E. Ruckert ◽  
L. Lyons ◽  
L. Cotton
Keyword(s):  
Decision Making ◽  
Acute Care ◽  
Clinical Decision Making ◽  
Physical Therapist ◽  
Clinical Decision ◽  
Computer Assisted ◽  
Learning Technology ◽  
Computer Assisted Learning ◽  
Physical Therapist Students ◽  
Acute Care Settings

scholarly journals Acute care nurses' perceptions of barriers to using research information in clinical decision-making

2002 ◽  
Vol 39 (1) ◽  
pp. 46-60 ◽  
Author(s):  
Dorothy McCaughan ◽  
Carl Thompson ◽  
Nicky Cullum ◽  
Trevor A. Sheldon ◽  
David R. Thompson
Keyword(s):  
Decision Making ◽  
Acute Care ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Research Information ◽  
Acute Care Nurses ◽  
Nurses Perceptions

scholarly journals A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Paul G. M. Knoops ◽  
Athanasios Papaioannou ◽  
Alessandro Borghi ◽  
Richard W. F. Breakey ◽  
Alexander T. Wilson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Reconstructive Surgery ◽  
Clinical Decision Making ◽  
Specific Treatment ◽  
Operation Time ◽  
Clinical Decision ◽  
Patient Specific ◽  
Computer Assisted ◽  
Plastic And Reconstructive Surgery

Abstract Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduce cost, they are often too complex and require extensive manual input, which ultimately limits their use in doctor-patient communication and clinical decision making. Here, we present the first large-scale clinical 3D morphable model, a machine-learning-based framework involving supervised learning for diagnostics, risk stratification, and treatment simulation. The model, trained and validated with 4,261 faces of healthy volunteers and orthognathic (jaw) surgery patients, diagnoses patients with 95.5% sensitivity and 95.2% specificity, and simulates surgical outcomes with a mean accuracy of 1.1 ± 0.3 mm. We demonstrate how this model could fully-automatically aid diagnosis and provide patient-specific treatment plans from a 3D scan alone, to help efficient clinical decision making and improve clinical understanding of face shape as a marker for primary and secondary surgery.

scholarly journals The effect of the quality of vital sign recording on clinical decision making in a regional acute care trauma ward

2017 ◽  
Vol 20 (5) ◽  
pp. 283-287 ◽  
Author(s):  
Claire M. Keene ◽  
Victor Y. Kong ◽  
Damian L. Clarke ◽  
Petra Brysiewicz
Keyword(s):  
Decision Making ◽  
Acute Care ◽  
Vital Sign ◽  
Clinical Decision Making ◽  
Clinical Decision
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