Background: Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals.
View Article and Find Full Text PDFIntroduction: A diagnosis of melanoma in situ presents negligible risk to a person's lifespan or physical well-being, but existing terminology makes it difficult for patients to distinguish these from higher risk invasive melanomas. This study aims to explore whether using an alternative label for melanoma in situ may influence patients' management choices and anxiety levels.
Methods And Analysis: This study is a between-subjects randomised online experiment, using hypothetical scenarios.
Background: The aim of this study is to develop a method we call "cost mining" to unravel cost variation and identify cost drivers by modelling integrated patient pathways from primary care to the palliative care setting. This approach fills an urgent need to quantify financial strains on healthcare systems, particularly for colorectal cancer, which is the most expensive cancer in Australia, and the second most expensive cancer globally.
Methods: We developed and published a customized algorithm that dynamically estimates and visualizes the mean, minimum, and total costs of care at the patient level, by aggregating activity-based healthcare system costs (e.
Background: The Australian National Bowel Cancer Screening Program sends an immunochemical faecal occult blood test to Australians aged 50-74 years to screen for bowel cancer, but uptake is low (40.9%). The SMARTscreen trial demonstrated that sending a short messaging services (SMS) prompt from the participant's general practitioner (GP) increased the proportion of kit returns by 16.
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