To determine the usefulness of a personalized tool and its effect on the decision-making process for those with vestibular schwannoma (VS)....
Prospective study....
Single institution, academic tertiary care lateral skull base surgery program....
Patients diagnosed with VS....
A comprehensive clinical decision support (CDS) tool was constructed from a previously published retrospective patient-reported data obtained from members of the Acoustic Neuroma Association from Janu...
Pre- and posttool questionnaires assessing the process of deciding treatment for VS using a decisional conflict scale (DCS) and satisfaction with decision (SWD) scale were compared....
A pilot study of 33 patients evaluated at a single institution tertiary care center with mean ± SD age of 63.9 ± 13.5 years and with average tumor size of 7.11 ± 4.75 mm were surveyed. CDS implementat...
Implementing a decision-making tool after diagnosis of VS reduced decisional conflict and improved satisfaction with decision. Patients considered the tool to be an aid to their medical knowledge, fur...
Defaecating proctogram (DP) studies have become an integral part of the evaluation of patients with pelvic floor disorders. However, their impact on treatment decision-making remains unclear. The aim ...
Four colorectal surgeons were presented with online surveys containing the complete history, examination and investigations of 106 de-identified pelvic floor patients who had received one of three tre...
After the addition of the DP results; treatment choice changed in 219 (52%) of 424 surgical decisions and interrater agreement improved significantly from κ = 0.26 to κ = 0.39. Three of the four surge...
The DP improves interclinician agreement in the management of pelvic floor disorders and enhances the confidence in treatment decisions. Intra-anal rectal prolapse was the most influential DP paramete...
Clinical decision making is an essential cognitive skill in nursing. It is a process undertaken daily by nurses as they make judgements about patient care and manage complex issues as they arise. Virt...
The objective of this integrative review are to synthesise the research findings regarding the impact of virtual reality on clinical decision making in undergraduate nurses....
An integrative review using Whittemore and Knafl's framework for integrated reviews....
An extensive search was conducted of healthcare databases including CINAHL, Medline and Web of Science between 2010 and 2021 using the terms virtual reality, clinical decision and undergraduate nursin...
The initial search located 98 articles. After screening and checking for eligibility, 70 articles were critically reviewed. Eighteen studies were included in the review and were critically appraised u...
Research in the use of VR has demonstrated its potential to improve undergraduate nurses' critical thinking, clinical reasoning, clinical judgement and clinical decision-making skills. Students percei...
Current research on the impact of virtual reality on the development of nursing CDM has demonstrated positive results. VR is one pedagogical approach that could further assist, however, there are no i...
Primary molar teeth that are retained beyond their exfoliation pose a clinical decision-making challenge for dental teams. The retention of these teeth may be due to absence of a permanent successor. ...
Nursing is a profession based on theoretical knowledge and practice, and the clinical decision-making process is important. Many factors affect the fear of negative evaluation, and fear of a negative ...
This descriptive cross-sectional study included undergraduate nursing students (...
Nursing students' fear of a negative evaluation and clinical decision-making scale scores were 31.92 ± 08.51 and 149.18 ± 13.67, respectively. No significant relationship was identified between the sc...
The fear of a negative evaluation was not associated with nursing students' perceptions of clinical decision-making. To reduce nursing students' fear of a negative evaluation and improve their clinica...
Clinical decision-making (CDM) is the ability to make clinical choices based on the knowledge and information available to the physician. It often refers to individual cognitive processes that becomes...
Every day, we must make decisions that range from simple and risk-free to difficult and risky. Our cognitive sources' limitations, as well as the need for speed, can frequently impair the quality and ...
Learning policies for decision-making, such as recommending treatments in clinical settings, is important for enhancing clinical decision-support systems. However, the challenge lies in accurately eva...
We develop counterfactual policy learning algorithms for practical clinical applications to suggest viable treatment for patients. We first design a bootstrap method for counterfactual assessment and ...
The efficacy of our algorithms was validated using both semi-synthetic and real-world clinical datasets. Our method outperforms baseline algorithms, reducing the variance in policy evaluation by 30% a...
This study demonstrates the effectiveness of combining bootstrap and adversarial learning techniques in policy learning for clinical decision support. It not only enhances the accuracy and reliability...
Expected outcomes (e.g., expected survivorship after a cancer treatment) have improved decision-making around treatment options in many clinical fields. Our objective was to evaluate the effect of exp...
The RAND/University of California Los Angeles appropriateness criteria method was used to evaluate the role of the 3 expected outcomes in clinical recommendation of TKA. The expected outcomes were add...
Ratings for the 279 appropriateness scenarios deemed 34.4% of the scenarios as appropriate, 40.1% as inconclusive, and 25.5% as inappropriate. Classification tree analyses showed that expected improve...
Our results showed that clinicians would use expected postoperative outcome factors in determining appropriateness for TKA. These results call for further work in this area to incorporate estimates of...
Machine learning-aided medical decision making presents three major challenges: achieving model parsimony, ensuring credible predictions, and providing real-time recommendations with high computationa...
