Ethical, legal and regulatory considerations
The framework highlights the ethical concerns that surround the use of AI in medical care. It notes that these tools have the “potential to increase or continue to reflect pre-existing biases and inequalities” and increase the digital divide. To address this, the capability aims to “promote equity, fairness and transparency”.
At the basic level of capability, an individual understands the ethical responsibility of AI and digital technology ensuring that processes and systems are transparent, fair and equitable for patients, staff and the public.
At the highest level of capability, a health and care professional must be aware of data collection tools and system to be created for “patient/user diversity so that the output of data processing/analysis will not cause or increase health inequalities”.
Human factors
This capability covers management and leadership skills and abilities, including change management, culture, and understanding of existing workflows and patient pathways. One of the capability statements is to practice patient and user centred design, acknowledging working with patients and users “to gather and produce requirements, for example, user stories, personas, UML diagrams, etc., to set priorities and evaluate and integrate technologies”.
Health data management / cyber security
Regarding health data management, the framework highlights a capability of understanding “where and how data are stored as well as data-flows and how they are applied to various pathways”.
This part of the framework provides a capability assessment for health and care professionals to be aware of data management/cyber security, to both meet legal requirements and secure public trust, from theft, loss and attacks.
Artificial intelligence
On AI tech, the framework highlights that at the basic level of capability, health and care professionals must be able to understand the umbrella term of AI in defining digital technologies. They must be aware that AI is “common in modern technology and can list uses of AI outside healthcare”.
The highest level of capability highlights that an individual must be able to describe the types of AI biases that can affect systems such as reporting, group attribution, implicit, and selection. As for using and implementing AI systems, the framework highlights the need to see a “closer working relationship between human experts and AI systems”.
