Summary
A key part of healthcare digital transformation is the development and adoption of artificial intelligence technologies. This article, published in BMJ Health & Care Informatics, considers how human factors and ergonomic principles can be applied to the use of artificial intelligence in healthcare.
Content
This article discusses eight human factors and ergonomics principles for healthcare AI, drawn from a white paper published by the Chartered Institute of Ergonomics and Human Factors:
- Situation awareness - Design options need to consider how AI can support, rather than erode, people’s situation awareness.
- Workload - The impact of AI on workload needs to be assessed because AI can both reduce as well as increase workload in certain situations.
- Automation bias - Strategies need to be considered to guard against people relying uncritically on the AI, for example, the use of explanation and training.
- Explanation and trust - AI applications should explain their behaviour and allow users to query it in order to reduce automation bias and to support trust.
- Human–AI teaming - AI applications should be capable of good teamworking behaviours to support shared mental models and situation awareness.
- Training - People require opportunities to practise and retain their skill sets when AI is introduced, and they need to have a baseline understanding of how the AI works. Attention needs to be given to the design of effective training that is accessible and flexible. Staff should be provided with protected time to undertake training during their work hours.
- Relationships between people - The impact on relationships needs to be considered, for example, whether staff will be working away from the patient as more and more AI is introduced.
- Ethical issues - AI in healthcare raises ethical challenges including fairness and bias in AI models, protection of privacy, respect for autonomy, realisation of benefits and minimisation of harm.
Eight human factors and ergonomics principles for healthcare artificial intelligence (4 February 2022)
https://informatics.bmj.com/content/29/1/e100516
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