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Development of a framework to structure decision-making in environmental and occupational health: A systematic review and Delphi study.
Environ Int
December 2024
Cochrane Canada and McMaster GRADE Centres & Department of Health Research Methods, Evidence and Impact, McMaster University, Health Sciences Centre, Room 2C14, 1280 Main Street West, Hamilton, ON L8S 4K1, Canada; School of Medicine, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA. Electronic address:
Background: Environmental and occupational health (EOH) assessments increasingly utilize systematic review methods and structured frameworks for evaluating evidence about the human health effects of exposures. However, there is no prevailing approach for how to integrate this evidence into decisions or recommendations. Grading of Recommendations Assessment, Development and Evaluation (GRADE) evidence-to-decision (EtD) frameworks provide a structure to support standardized and transparent consideration of relevant criteria to inform health decisions.
View Article and Find Full Text PDFLeveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.
Drug Saf
January 2025
Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Background: Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.
Objective: To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.
Purpose: Caregivers in pediatric oncology need accurate and understandable information about their child's condition, treatment, and side effects. This study assesses the performance of publicly accessible large language model (LLM)-supported tools in providing valuable and reliable information to caregivers of children with cancer.
Methods: In this cross-sectional study, we evaluated the performance of the four LLM-supported tools-ChatGPT (GPT-4), Google Bard (Gemini Pro), Microsoft Bing Chat, and Google SGE-against a set of frequently asked questions (FAQs) derived from the Children's Oncology Group Family Handbook and expert input (In total, 26 FAQs and 104 generated responses).
A novel acute basic palliation concept for patients without specialised palliative needs.
Dan Med J
November 2024
Department of Emergency Medicine and Trauma Care, Aalborg University Hospital.
Introduction: Among all Danish dying patients, 80% rely on non-specialised palliative care, an area lacking national and international guidelines. In this pilot study, we developed and tested an acute basic palliation concept (ABPC), a structured end-of-life (EOL) care plan for patients discharged from the emergency department to die at home compared with standard care.
Methods: This study compared symptom scores and EOL care statement scores during a standard care period with an ABPC period using unvalidated questionnaires.
A Nurse-Initiated Protocol for Interprofessional Management of Changed Behaviours in Hospital Patients With Dementia and/or Delirium: A Modified e-Delphi Study.
J Adv Nurs
January 2025
Nursing Practice Development Unit, Princess Alexandra Hospital, Woolloongabba, Queensland, Australia.
Aim: To develop a nurse-initiated protocol for early ward-based interprofessional coordination and formulation of person-centred care plans to assist in point-of-care management of behaviour in older patients on general hospital wards.
Design: A modified e-Delphi method was employed to establish expert consensus.
Method: Multidisciplinary acute-care experts experienced in hospital care of patients with dementia and/or delirium in Australia were recruited by email from 35 professional networks.
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