Parents/legal guardians are medical decision-makers for their minor children. Lack of parental capacity to appreciate the implications of the diagnosis and consequences of refusing recommended treatment may impede pediatric patients from receiving adequate medical care. Child and adolescent psychiatrists (CAPs) need to appreciate the ethical considerations relevant to overriding parental medical decision-making when faced with concerns for medical neglect. Two de-identified cases illustrate the challenges inherent in clinical and ethical decision-making reflected in concerns for parental capacity for medical decision-making. Key ethical principles are reviewed. Treatment of an adolescent with an eating disorder ethically complex due to the legal guardian's inability to adhere with treatment recommendations leading to the patient's recurrent abrupt weight loss. Questions of parental decisional capacity amid treatment of an adolescent with schizoaffective disorder raised due to parental mistrust of diagnosis, disagreement with treatment recommendations, and lack of appreciation of the medical severity of the situation with repeated discharges against medical advice and medication nonadherence. Decisions to question parental capacity for medical decision-making when risk of imminent harm is low but concern for medical neglect exists are controversial. Systematic review of cases concerning for medical neglect benefits from the assessment of parental decisional capacity, review of ethical standards and principles. Recognition of the importance of parental decision-making capacity as relates to parental autonomy and medical neglect and understanding key ethical principles will enhance the CAP's capacity in medical decision-making when stakes are high and absolute recommendations are lacking.
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http://dx.doi.org/10.3389/fpsyt.2020.559263 | DOI Listing |
Am J Hosp Palliat Care
January 2025
Department of Emergency Medicine, Medical College of Wisconsin, Milwaukee, WI, USA.
Objectives: To explore American Muslims' perceptions and experiences regarding hospice care within the United States.
Methods: A qualitative descriptive study of 11 participants, including one patient and ten family caregivers. Data was collected through semi-structured interviews and analyzed using a framework approach to identify key themes related to perceptions, ethical concerns, and experiences with hospice care.
EClinicalMedicine
January 2025
School of Translational Medicine, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia.
Background: Discrete choice experiments (DCEs) are increasingly used to inform the design of health products and services. It is essential to understand the extent to which DCEs provide reliable predictions outside of experimental settings in real-world decision-making situations. We aimed to compare the prediction accuracy of stated preferences with real-world choices, as modelled from DCE data.
View Article and Find Full Text PDFCureus
December 2024
Acute Medicine, Portsmouth Hospitals University NHS Trust, Portsmouth, GBR.
Cardiology, a high-acuity medical specialty, has traditionally emphasised technical expertise, often overshadowing the critical role of non-technical skills (NTS). This imbalance stems from the historical focus on procedural competence and clinical knowledge in cardiology training and practice, leaving a significant gap in the development of crucial interpersonal and cognitive abilities. However, emerging evidence highlights the significant impact of NTS on patient outcomes, team dynamics, and overall healthcare efficiency.
View Article and Find Full Text PDFCureus
December 2024
Department of Technology and Clinical Trials, Advanced Research, Deerfield Beach, USA.
This paper investigates the potential of artificial intelligence (AI) and machine learning (ML) to enhance the differentiation of cystic lesions in the sellar region, such as pituitary adenomas, Rathke cleft cysts (RCCs) and craniopharyngiomas (CP), through the use of advanced neuroimaging techniques, particularly magnetic resonance imaging (MRI). The goal is to explore how AI-driven models, including convolutional neural networks (CNNs), deep learning, and ensemble methods, can overcome the limitations of traditional diagnostic approaches, providing more accurate and early differentiation of these lesions. The review incorporates findings from critical studies, such as using the Open Access Series of Imaging Studies (OASIS) dataset (Kaggle, San Francisco, USA) for MRI-based brain research, highlighting the significance of statistical rigor and automated segmentation in developing reliable AI models.
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