Care models are changing all around us. The significant advancement of digital health technologies and artificial intelligence (AI) capabilities have the potential to revolutionise health and social care. A project within Gloucestershire Hospitals Trust is an excellent example of this. They used machine learning to identify patients at risk of avoidable long-term hospital stays.
But we also know that there are risks associated with reliance on algorithms to guide healthcare decisions. In 2019, researchers found evidence of racial bias in one algorithm widely used in the U. S. healthcare system. This meant that black patients assigned the same level of risk by the algorithm were sicker than white patients.
Our evidence standards framework
NICE is uniquely placed to help the UK health and care system release value from digital and AI technologies, while helping to mitigate risk. Our evidence standards framework describes the evidence requirements of different types of digital health technologies. It helps innovators understand what kind of evidence they need to generate to prove the effectiveness and value of their product when selling to the NHS. It also helps commissioners to make more informed and consistent decisions when purchasing digital health technologies.
Our new draft version of the framework includes some important updates. It:
- specifies evidence requirements for data-driven technologies such as AI with adaptive algorithms
- aligns classification with regulatory requirements
- is clearer and easier to use.
We are currently consulting on our proposed changes and value your input. Whether you are a developer or an adopter, a patient or a clinician using AI tools, this is your opportunity to help shape the framework. Does the level of evidence required strike the right balance? If we set standards too high, we could create barriers for patient and system access to potential innovation. But, if standards are too low, it will be difficult to identify those products that are truly transformational for patients and offer value to the system.
We’ve designed the tool for those working in the system to use. This could be commissioners, or innovation leads in the integrated care systems. Have we pitched the framework correctly? Is it user-friendly enough so that people who may not be technical experts in digital and AI technology can use it?
The consultation is open until Monday 25 April 2022. So, please take the time to share your views before then.
Following your feedback and any changes we may make as a result, we plan to launch the updated framework in June 2022.
Real world evidence framework
We are also currently inviting feedback on our draft real world evidence framework. The framework describes best practices for planning, conducting and reporting real world evidence studies, including the assessment of data suitability. We’ve designed it to help life science companies and other stakeholders when developing their evidence generation plans and our committees when evaluating such evidence. Share your views before Friday 29 April 2022.
There are no comments