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BACKGROUND: The preparation of the current and future health workforce for the possibility of using artificial intelligence (AI) in health care is a growing concern as AI applications emerge in various care settings and specializations. At present, there is no obvious consensus among educators about what needs to be learned or how this learning may be supported or assessed. OBJECTIVE: Our study aims to explore health care education experts' ideas and plans for preparing the health workforce to work with AI and identify critical gaps in curriculum and educational resources across a national health care system. METHODS: A survey canvassed expert views on AI education for the health workforce in terms of educational strategies, subject matter priorities, meaningful learning activities, desired attitudes, and skills. A total of 39 senior people from different health workforce subgroups across Australia provided ratings and free-text responses in late 2020. RESULTS: The responses highlighted the importance of education on ethical implications, suitability of large data sets for use in AI clinical applications, principles of machine learning, and specific diagnosis and treatment applications of AI as well as alterations to cognitive load during clinical work and the interaction between humans and machines in clinical settings. Respondents also outlined barriers to implementation, such as lack of governance structures and processes, resource constraints, and cultural adjustment. CONCLUSIONS: Further work around the world of the kind reported in this survey can assist educators and education authorities who are responsible for preparing the health workforce to minimize the risks and realize the benefits of implementing AI in health care.
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OBJECTIVE: Identify variables associated with paediatric access to cochlear implants (CIs). DESIGN: Part 1. Trends over time for age at CI surgery (N = 802) and age at hearing aid (HA) fitting (n = 487) were examined with regard to periods before, during, and after newborn hearing screening (NHS). Part 2. Demographic factors were explored for 417 children implanted under 3 years of age. Part 3. Pre-implant steps for the first 20 children to receive CIs under 12 months were examined. RESULTS: Part 1. Age at HA fitting and CI surgery reduced over time, and were associated with NHS implementation. Part 2. For children implanted under 3 years, earlier age at HA fitting and higher family socio-economic status were associated with earlier CI. Progressive hearing loss was associated with later CIs. Children with a Connexin 26 diagnosis received CIs earlier than children with a premature / low birth weight history. Part 3. The longest pre-CI steps were Step 1: Birth to diagnosis/identification of hearing loss (mean 16.43 weeks), and Step 11: MRI scans to implant surgery (mean 15.05 weeks) for the first 20 infants with CIs under 12 months. CONCLUSION: NHS implementation was associated with reductions in age at device intervention in this cohort.