98%
921
2 minutes
20
Purpose: This study aims to capture the complex clinical reasoning process during tailoring of exercise and dietary interventions to adverse effects and comorbidities of patients with ovarian cancer receiving chemotherapy.
Methods: Clinical vignettes were presented to expert physical therapists ( = 4) and dietitians ( = 3). Using the think aloud method, these experts were asked to verbalize their clinical reasoning on how they would tailor the intervention to adverse effects of ovarian cancer and its treatment and comorbidities. Clinical reasoning steps were categorized in raised to obtain additional information; anticipated ; and to be taken. Questions and actions were labeled according to the evidence-based practice model.
Results: to obtain additional information were frequently related to the patients' capacities, safety or the etiology of health issues. Various hypothetical were proposed which led to different actions. Suggested by the experts included extensive monitoring of symptoms and parameters, specific adaptations to the exercise protocol and dietary-related patient education.
Conclusions: Our study obtained insight into the complex process of clinical reasoning, in which a variety of patient-related variables are used to tailor interventions. This insight can be useful for description and fidelity assessment of interventions and training of healthcare professionals.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1080/09638288.2023.2265820 | DOI Listing |
SAGE Open Nurs
September 2025
Department of Public Health Nursing, School of Nursing and Midwifery, University of Ghana, Legon, Ghana.
Introduction: The world is in an era where healthcare professionals require training in soft skills to improve their caring ability. Regrettably, a concise compilation of nursing soft skills remains empirically unclassified.
Objectives: This study described a perceived list of soft skills necessary in nursing, as itemized by nurses and midwives in Ghana.
Int J Gen Med
September 2025
Betty and Guy Beatty Center for Integrated Research, Inova Health System, Falls Church, VA, USA.
Purpose: The diagnosis of post-acute SARS-CoV-2 infection (PASC) is broad, referring to new or persistent health problems >four weeks after being infected with SARSCoV-2. The aim of this study was to determine whether cytokines, chemokines or catecholamine levels could specify the clinical condition.
Patients And Methods: Seventy-nine participants participated in person to study PASC.
JAMIA Open
October 2025
Division of Pulmonary and Critical Care, Brigham and Women's Hospital, Boston, MA, United States.
Objectives: Unstructured data, such as procedure notes, contain valuable medical information that is frequently underutilized due to the labor-intensive nature of data extraction. This study aims to develop a generative artificial intelligence (GenAI) pipeline using an open-source Large Language Model (LLM) with built-in guardrails and a retry mechanism to extract data from unstructured right heart catheterization (RHC) notes while minimizing errors, including hallucinations.
Materials And Methods: A total of 220 RHC notes were randomly selected for pipeline development and 200 for validation from the Pulmonary Vascular Disease Registry.
Front Artif Intell
August 2025
Department of Biomedical Sciences, School of Health Sciences, State University of Rio Grande do Norte, Mossoró, Brazil.
Introduction: ChatGPT, a generative artificial intelligence, has potential applications in numerous fields, including medical education. This potential can be assessed through its performance on medical exams. Medical residency exams, critical for entering medical specialties, serve as a valuable benchmark.
View Article and Find Full Text PDFIEEE Conf Artif Intell
May 2025
Potentia Analytics Inc, IL, USA.
The shift toward patient-centric healthcare requires understanding comprehensive patient journeys. Current healthcare data systems often fail to provide holistic representations, hindering coordinated care. Patient Journey Knowledge Graphs (PJKGs) solve this by integrating diverse patient information into unified, structured formats.
View Article and Find Full Text PDF