scholarly journals Strategies nurse educators use to integrate computer -assisted instruction into their courses to teach clinical decision-making

2005 ◽  
Author(s):  
Beverly J. Schaefer
Keyword(s):  
Decision Making ◽  
Nurse Educators ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Assisted Instruction

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.

Computer-Assisted Clinical Decision-Making

1973 ◽  
Vol 12 (01) ◽  
pp. 45-51 ◽  
Author(s):  
G. A. GOBRY
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Assisted ◽  
New Approaches ◽  
Computer Aided ◽  
Clinical Problems

This paper discusses some research in computer-aided! clinical decision-making. Experience with formal (decision theory) models is discussed and the limitations of these models are noted.It is argued that new approaches are needed to solve complex clinical problems, and one such approach is discussed.

Intuition in Clinical Decision Making: Differences Among Practicing Nurses

2017 ◽  
Vol 36 (4) ◽  
pp. 318-329 ◽  
Author(s):  
Elizabeth M. Miller ◽  
Pamela D. Hill
Keyword(s):  
Decision Making ◽  
Nurse Educators ◽  
Clinical Decision Making ◽  
Design Method ◽  
Medical Center ◽  
Clinical Decision ◽  
Cross Sectional ◽  
Proficiency Levels ◽  
Surgical Nurses ◽  
Development Professionals

Purpose: To examine the relationships and differences in the use of intuition among three categories of practicing nurses from various clinical units at a medical center in the Midwest. Design: Descriptive, correlational, cross-sectional, prospective design. Method: Three categories of nurses were based on the clinical unit: medical/surgical nurses ( n = 42), step-down/progressive care nurses ( n = 32), and critical care nurses ( n = 24). Participants were e-mailed the Rew Intuitive Judgment Scale (RIJS) via their employee e-mail to measure intuition in clinical practice. Participants were also asked to rate themselves according to Benner’s (novice to expert) proficiency levels. Findings: Nurses practicing at higher self-reported proficiency levels, as defined by Benner, scored higher on the RIJS. More years of clinical experience were associated with higher self-reported levels of nursing proficiency and higher scores on the RIJS. There were no differences in intuition scores among the three categories of nurses. Conclusion: Nurses have many options, such as the nursing process, evidence-based clinical decision-making pathways, protocols, and intuition to aid them in the clinical decision-making process. Nurse educators and development professionals have a responsibility to recognize and embrace the multiple thought processes used by the nurse to better the nursing profession and positively affect patient outcomes.

Computer Assisted Clinical Decision Making

1983 ◽  
Vol 6 (3-4) ◽  
pp. 119-132 ◽  
Author(s):  
Leonard E. Gibbs ◽  
David J. Johnson
Keyword(s):  
Decision Making ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Assisted
Sign in / Sign up

Export Citation Format

Share Document