5.Use of complementary and alternative medicine in paediatric oncology patients in Singapore.
Joeanne LIM ; Manzhi WONG ; Mei Yoke CHAN ; Ah Moy TAN ; Vasanthi RAJALINGAM ; Lillian P N LIM ; June LOU ; Cheng Lim TAN
Annals of the Academy of Medicine, Singapore 2006;35(11):753-758
INTRODUCTIONComplementary and alternative medicine (CAM) is garnering increasing interest and acceptance among the general population. Although usage is thought to be widespread among paediatric cancer patients, local studies have not been done. We aimed to investigate the prevalence and predictors of CAM usage in paediatric cancer patients in a single institution.
MATERIALS AND METHODSParents of 73 paediatric cancer patients treated at KK Women's & Children's Hospital completed an interviewer-administered questionnaire. Data about the types of CAM therapies used, motivations for use, adverse effects, costs and discussion of usage with the patient's physician were obtained. General perceptions towards CAM and conventional medicine were explored. A subsequent telephone survey enquired about spirituality, benefits of CAM use and overall satisfaction with the therapies.
RESULTSTwo-thirds of patients used at least 1 CAM treatment, mainly as supportive adjuncts to conventional cancer treatment. Dietary changes, health supplements, herbal tea and bird's nest were the most common therapies used. Few patients (8.1%) consulted a CAM practitioner. Positive predictors of CAM usage included being of Chinese race, the practice of Buddhism or Taoism, the use of CAM prior to diagnosis, perception of CAM effectiveness and dissatisfaction with conventional treatment. Significantly, 55.1% of the parents had not discussed their CAM usage with their child's physician.
CONCLUSIONSA substantial proportion of paediatric cancer patients utilises CAM therapies, often without their physician's knowledge. Healthcare providers need to remain cognisant of the potential implications of CAM usage in order to proactively counsel patients. This would ensure that conventional therapy remains uncompromised.
Adolescent ; Child ; Child, Preschool ; Female ; Humans ; Infant ; Male ; Medical Oncology ; methods ; Medicine, Chinese Traditional ; Neoplasms ; epidemiology ; therapy ; Patient Satisfaction ; Pediatrics ; methods ; Prevalence ; Singapore ; epidemiology
6.Progressive Multifocal Leukoencephalopathy with Immune Reconstitution Inflammatory Syndrome (PML-IRIS): two case reports of successful treatment with mefloquine and a review of the literature.
Barnaby E YOUNG ; Tian Rong YEO ; Hui Ting LIM ; Kiat Yee VONG ; Kevin TAN ; David C LYE ; Cheng Chuan LEE
Annals of the Academy of Medicine, Singapore 2012;41(12):620-624
9.Artificial Intelligence and Radiology in Singapore: Championing a New Age of Augmented Imaging for Unsurpassed Patient Care.
Charlene Jy LIEW ; Pavitra KRISHNASWAMY ; Lionel Te CHENG ; Cher Heng TAN ; Angeline Cc POH ; Tchoyoson Cc LIM
Annals of the Academy of Medicine, Singapore 2019;48(1):16-24
Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. Singapore radiologists have been quick to embrace this technology as part of the natural progression of the discipline toward a vision of how clinical medicine, empowered by technology, can achieve our national healthcare objectives of delivering value-based and patient-centric care. In this article, we consider 3 core questions relating to AI in radiology, and review the barriers to the widespread adoption of AI in radiology. We propose solutions and describe a "Centaur" model as a promising avenue for enabling the interfacing between AI and radiologists. Finally, we introduce The Radiological AI, Data Science and Imaging Informatics (RADII) subsection of the Singapore Radiological Society. RADII is an enabling body, which together with key technological and institutional stakeholders, will champion research, development and evaluation of AI for radiology applications.
Artificial Intelligence
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Humans
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Image Processing, Computer-Assisted
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Machine Learning
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Neural Networks (Computer)
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Radiology
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Singapore
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Societies, Medical
Result Analysis
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