Purpose: To reduce lung cancer mortality, individuals at high-risk should receive a low-dose computed tomography screening annually. To increase the likelihood of screening, interventions that promote shared decision-making are needed. The goal of this study was to investigate the feasibility, acceptability, usability, and preliminary effectiveness of a computer-based decision aid.
Methods: Thirty-three participants were recruited through primary-care clinics in a small southeastern-US city. Participants used a computer-based decision aid ("Is Lung Cancer Screening for You?") during a clinic appointment. Paper surveys collected self-reported feasibility, acceptability, and usability data. A research coordinator was present to observe each patient's and health-care provider's interactions, and to assess the fidelity of shared decision-making.
Results: The decision aid was feasible, acceptable for use in a clinic setting, and easy for participants to use. Patients had low decisional conflict following use of the decision aid and had high screening intention and actual screening rates. Shared decision-making discussions using the decision aid were nearly 6 min on average.
Conclusion: Computer-based decision aids are feasible for promoting shared lung cancer-screening decisions. A more robust study is warranted to measure the added value of a computer-based version of this aid versus a paper-based aid.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9651125 | PMC |
| http://dx.doi.org/10.1007/s10552-022-01650-2 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Qwen-2.5 Outperforms Other Large Language Models in the Chinese National Nursing Licensing Examination: Retrospective Cross-Sectional Comparative Study.
JMIR Med Inform
January 2025
Department of Science and Education, Shenzhen Baoan Women's and Children's Hospital, Shenzhen, China.
Background: Large language models (LLMs) have been proposed as valuable tools in medical education and practice. The Chinese National Nursing Licensing Examination (CNNLE) presents unique challenges for LLMs due to its requirement for both deep domain-specific nursing knowledge and the ability to make complex clinical decisions, which differentiates it from more general medical examinations. However, their potential application in the CNNLE remains unexplored.
View Article and Find Full Text PDFA hybrid unsupervised machine learning model with spectral clustering and semi-supervised support vector machine for credit risk assessment.
PLoS One
January 2025
College of Business, Southern University of Science and Technology, Shenzhen, China.
In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to challenges such as imbalanced data, local optima, and parameter adjustment complexities. Thus, this paper introduces a novel hybrid unsupervised classification method, named the two-stage hybrid system with spectral clustering and semi-supervised support vector machine (TSC-SVM), which effectively addresses the unsupervised imbalance problem in credit risk assessment by targeting global optimal solutions.
View Article and Find Full Text PDFUnderstanding the independent samples t test in nursing research.
Br J Nurs
January 2025
Associate Professor, Nursing and Midwifery, University of Limerick, Limerick, Ireland.
Critical thinking is required for successful nursing outcomes. For evidence-based practice, there is a need to understand and apply quantitative methods of research and statistical analysis in order to obtain evidence. However, the literature shows that the use of quantitative methods among nurse researchers can be problematic.
View Article and Find Full Text PDFAnalytical Techniques for Supporting Hospital Case Mix Planning Encompassing Forced Adjustments, Comparisons, and Scoring.
Healthcare (Basel)
December 2024
School of Engineering, University of Southern Queensland, Springfield, QLD 4300, Australia.
: This article presents analytical techniques and a decision support tool to aid in hospital capacity assessment and case mix planning (CMP). To date, no similar techniques have been provided in the literature. : Initially, an optimization model is proposed to analyze the impact of making a specific change to an existing case mix, identifying how patient types should be adjusted proportionately to varying levels of hospital resource availability.
View Article and Find Full Text PDFPrognostic Significance of Computed Tomography-Derived Fractional Flow Reserve for Long-Term Outcomes in Individuals With Coronary Artery Disease.
J Am Heart Assoc
January 2025
Department of Cardiology Beijing Anzhen Hospital, Capital Medical University Beijing China.
Background: Data on the predictive value of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) for long-term outcomes are limited.
Methods And Results: A retrospective pooled analysis of individual patient data was performed. Deep-learning-based CT-FFR was calculated.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!