Introduction: Clinical reasoning encompasses the process of data collection, synthesis, and interpretation to generate a working diagnosis and make management decisions. Situated cognition theory suggests that knowledge is relative to contextual factors, and clinical reasoning in urgent situations is framed by pressure of consequential, time-sensitive decision-making for diagnosis and management. These unique aspects of urgent clinical care may limit the effectiveness of traditional tools to assess, teach, and remediate clinical reasoning.
Methods: Using two validated frameworks, a multidisciplinary group of clinicians trained to remediate clinical reasoning and with experience in urgent clinical care encounters designed the novel Rapid Evaluation Assessment of Clinical Reasoning Tool (REACT). REACT is a behaviorally anchored assessment tool scoring five domains used to provide formative feedback to learners evaluating patients during urgent clinical situations. A pilot study was performed to assess fourth-year medical students during simulated urgent clinical scenarios. Learners were scored using REACT by a separate, multidisciplinary group of clinician educators with no additional training in the clinical reasoning process. REACT scores were analyzed for internal consistency across raters and observations.
Results: Overall internal consistency for the 41 patient simulations as measured by Cronbach's alpha was 0.86. A weighted kappa statistic was used to assess the overall score inter-rater reliability. Moderate reliability was observed at 0.56.
Discussion: To our knowledge, REACT is the first tool designed specifically for formative assessment of a learner's clinical reasoning performance during simulated urgent clinical situations. With evidence of reliability and content validity, this tool guides feedback to learners during high-risk urgent clinical scenarios, with the goal of reducing diagnostic and management errors to limit patient harm.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9202973 | PMC |
http://dx.doi.org/10.1007/s11606-022-07513-5 | DOI Listing |
Alzheimers Dement
December 2024
Tilburg University, Tilburg, Netherlands.
Background: Developing culture-fair tests that measure constructs equivalently across different ethno-lingual groups is challenging, given the diverse cultural variations that impact neurocognitive measurement. Multi-level measurement invariance must be established before interpreting scores similarly across groups, both within and between cultures for meaningful comparisons.
Method: We set out to test whether a neurocognitive tool (BENCI) behaves the same way across the males (n = 311) and the females group (n = 291) using measurement invariance testing with multigroup confirmatory factor analysis.
BMC Med Educ
January 2025
Deakin Optometry, School of Medicine, Deakin University, 75 Pigdons Road, Waurn Ponds , VIC, 3216, Australia.
Background: Clinical reasoning is a professional capability required for clinical practice. In preclinical training, clinical reasoning is often taught implicitly, and feedback is focused on discrete outcomes of decision-making. This makes it challenging to provide meaningful feedback on the often-hidden metacognitive process of reasoning to address specific clinical reasoning difficulties.
View Article and Find Full Text PDFNat Med
January 2025
State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
The delivery of accurate diagnoses is crucial in healthcare and represents the gateway to appropriate and timely treatment. Although recent large language models (LLMs) have demonstrated impressive capabilities in few-shot or zero-shot learning, their effectiveness in clinical diagnosis remains unproven. Here we present MedFound, a generalist medical language model with 176 billion parameters, pre-trained on a large-scale corpus derived from diverse medical text and real-world clinical records.
View Article and Find Full Text PDFNat Med
January 2025
Google Research, Mountain View, CA, USA.
Large language models (LLMs) have shown promise in medical question answering, with Med-PaLM being the first to exceed a 'passing' score in United States Medical Licensing Examination style questions. However, challenges remain in long-form medical question answering and handling real-world workflows. Here, we present Med-PaLM 2, which bridges these gaps with a combination of base LLM improvements, medical domain fine-tuning and new strategies for improving reasoning and grounding through ensemble refinement and chain of retrieval.
View Article and Find Full Text PDFZhonghua Yi Xue Yi Chuan Xue Za Zhi
January 2025
Center of Prenatal Diagnosis, Lianyungang Maternal and Child Health Care Hospital, Lianyungang, Jiangsu 222000, China.
Objective: To explore the clinical significance of trisomy 7 signaled by non-invasive prenatal testing (NIPT).
Methods: Pregnant women with high risk for trisomy 7 by NIPT from January 2017 to December 2023 were selected as the study subjects, and the results of prenatal diagnosis and follow-up were analyzed. Literature related to pregnant women with a high risk for trisomy 7 by NIPT from January 2016 to July 2024 was retrieved from China Biomedical Literature Database, Wanfang Database, China National Knowledge Infrastructure and PubMed database.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!