In the health care setting, and especially in cancer patients nearing the end of life, administrators, medical staff, patients, and families face challenges of a social, legal, religious, and cultural nature in the process of care. The present study aimed to perform a metasynthesis of qualitative studies conducted on providing end-of-life care for cancer patients. The present metasynthesis was conducted using Sandelowski and Barroso's method. A literature search was performed in PubMed, Scopus, Web of Science, and Embase databases, from the inception to date, and a total of 21 articles were identified as eligible for inclusion in the study. Critical Appraisal Skills Programme (CASP) criteria were used for assessing the articles, and data were analyzed by the subject review. Six themes were extracted for end-of-life care including psychological support, palliative support, educational-counseling support, spiritual support, preferential support, and supportive interactions, each comprising a number of categories. The most frequently mentioned categories were high-value care (67%) and adaptive acceptance (57%). The findings of this metasynthesis support the view that nurses are moral agents who are deeply invested in the moral integrity of end-of-life care involving assisted death. The present study showed that providing high-value care and facilitating adaptive acceptance are important constituents of a holistic strategy for providing end-of-life care to cancer patients.
Cancer is the second cause of death worldwide. The World Health Organization reported that 9.6 million people died of cancer in 2018 (World Health Organization Cancer, 2019), with the median age at death being 72 years (National Cancer Institute, 2019). This disease affects not only the patient, but also their family and society. Cancer patients, especially those in late stages, are confronted with many challenges, both because of the disease and complications of treatment (Saberzadeh-Ardestani et al ., 2019; Wong et al., 2018). Healthcare providers need to support older cancer patients with a palliative care plan that involves symptom management and psychosocial adaptation (
The integration of Electronic Health Records (EHRs) with Machine Learning (ML) models has become imperative in examining patient outcomes due to the vast amounts of clinical data they provide. However, critical information regarding social and behavioral factors that affect health, such as social isolation, stress, and mental health complexities, is often recorded in unstructured clinical notes, hindering its accessibility. This has resulted in an over-reliance on clinical data in current EHR-based research, potentially leading to disparities in health outcomes. This study aims to evaluate the impact of incorporating patient-specific context from unstructured EHR data on the accuracy and stability of ML algorithms for predicting mortality, using the MIMIC III database. Results from the study confirmed the significance of incorporating patient-specific information into prediction models, leading to a notable improvement in the discriminatory power and robustness of the ML algorithms. Furthermore, the findings underline the importance of considering non-clinical factors related to a patient's daily life, in addition to clinical factors, when making predictions about patient outcomes. These results have significant ramifications for improving ML in clinical decision support and patient outcome predictions.
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