Evaluating the Impact of Using GRASP Framework on Clinicians and Healthcare Professionals Decisions in Selecting Clinical Predictive Tools
Mohamed Khalifa, Farah Magrabi, Blanca Gallego

TL;DR
This study demonstrates that the GRASP framework significantly improves clinicians' decision-making accuracy, objectivity, confidence, and satisfaction when selecting clinical predictive tools, streamlining the process and reducing decisional conflict.
Contribution
The paper introduces and validates the GRASP framework, an evidence-based tool for grading and assessing predictive tools, demonstrating its positive impact on decision quality among healthcare professionals.
Findings
GRASP increased correct decision rate by 64%.
GRASP reduced decisional conflict and increased confidence.
Participants rated GRASP's usability very high.
Abstract
Background. When selecting predictive tools, clinicians and healthcare professionals are challenged with an overwhelming number of tools, most of which have never been evaluated for comparative effectiveness. To overcome this challenge, the authors developed and validated an evidence-based framework for grading and assessment of predictive tools (GRASP), based on the critical appraisal of published evidence. Methods. To examine GRASP impact on professionals decisions, a controlled experiment was conducted through an online survey. Randomising two groups of tools and two scenarios; participants were asked to select the best tools; most validated or implemented, with and without GRASP. A wide group of international participants were invited. Task completion time, rate of correct decisions, rate of objective vs subjective decisions, and level of decisional conflict were measured. Results.…
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Taxonomy
TopicsArtificial Intelligence in Healthcare and Education · Cardiac, Anesthesia and Surgical Outcomes · Machine Learning in Healthcare
