UK NICE updates its Evidence Standards Framework for data-driven digital health technologies – Ropes & Gray LLP

International Office Sites
Browse by last name:
View All Practices
View All Industries
International Office Sites
Sign Up For Alerts
On 22 September 2022, the Department for Business, Energy & Industry Strategy published the Retained EU Law (Revocation and Reform) Bill 2022 (the “Bill”) and introduced it to the UK Parliament.
Read More

Time to Read: 4 minutes Practices: European Life Sciences Industries: Healthcare & Life Sciences
Printer-Friendly Version
In August 2022, the National Institute for Health and Care Excellence (“NICE”), the body responsible for conducting health technology assessments in the UK, updated its Evidence Standards Framework (“ESF”) for digital health technologies (“DHTs”) to include important guidance relating to evidential standards for artificial intelligence (“AI”) and data-driven technologies.
First published in March 2019, the ESF is a set of evidence standards that DHTs should ideally meet before being considered for commissioning or procurement by the National Health Service (“NHS”). It is designed to assist NHS evaluators to identify DHTs that are clinically effective and that offer value to the health and care system. The framework can also be used by developers to understand how the NHS evaluates these technologies to inform a view on commissioning or procurement decisions. Compliance with the ESF does not replace the formal Medical Technologies Evaluation Programme whereby NICE selects and evaluates medical technologies in order to produce recommendations based on the extent to which the adoption of the medical technology by the NHS would offer potential patient and healthcare system benefits.
The ESF includes 21 standards that are arranged across five areas of a DHT lifecycle:
The overarching objective of the ESF is to accelerate the update of high-quality innovations that healthcare professionals can embed in clinical workflows and patient self-care toolkits.
NICE was funded by the NHS AI Lab, which is part of NHS England’s Transformation Directorate, to update the ESF in a way that gives specific consideration to data-driven DHTs. The updated framework seeks to:
The updated ESF recognises that data-driven DHTs with fixed or adaptive machine-learning algorithms might have increased risks not seen for other technologies. For example, information provided by DHTs can be used to assist in treating, diagnosing, triaging or identifying early signs of a disease or condition or guiding follow-up diagnostics or treatment interventions. Accordingly, the updates made to the ESF include aspects that are more relevant to these technologies.
Although the general principles set out in the extant ESF are generally applicable to define the performance characteristics of, and deployment pathways for, DHTs, the updates have highlighted the following considerations as particularly relevant to data-driven DHTs:
Consistent with the UK Government’s Life Sciences Vision and NHS England’s Long Term Plan, digital transformation is critical to the long-term sustainability of health and social care. The goal is to ensure that the health system is equipped to support and foster innovation. The updated ESF could assist efficient uptake of these technologies within the NHS. Moreover, the COVID-19 pandemic has shown that having the right DHTs at the NHS’s disposal can be as critical as having the right therapies to prevent and treat infectious agents. Specifically, it is recognised that whether through underpinning the initial operational planning, clinical research into treatments, and the rapid, highly targeted NHS COVID-19 vaccine roll-out, DHTs have played an essential but largely hidden role in how the health and care service has responded to the biggest public health crisis in a century.
Printer-Friendly Version
Copyright © 2022 Ropes & Gray LLP. All rights reserved. Attorney advertising. Prior results do not guarantee a similar outcome.

source

Leave a Comment

Your email address will not be published.