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Equity in Medical Devices: Independent Review

By March 13, 2024No Comments

The Government has outlined a set of actions to tackle potential bias in the design and use of medical devices and use of PRS in genomics, following an independent review.

PRS are already available commercially through direct-to-consumer tests but have not yet been adopted by the NHS.

The review acknowledges with concern that data sources upon which PRS draw have a well-established bias against groups with non-European genetic ancestry. In addition, the report noted concern for the potential for misinterpretation of results by the public and health professionals alike, especially in relation to genetic determinism, which may carry wider risks to society at large.

The Government have therefore released the following recommendations:

  • The focus of PRS studies should be widened beyond genetic diversity to include the contribution of the social determinants of health – including lifestyle, living and working conditions and environmental factors such as air pollution – to overall disease risk, and how these affect the predictive potential of PRS among different ethnicities and socio-economic groups. Developments with this wider research focus should aid the refinement of overall risk assessments so they better reflect the role that PRS play alongside non-genetic risk factors.
  • National research funders should commission a broad programme of research and consultation with the public, patients and health professionals to fill the gaps in knowledge and understanding concerning PRS to allay fears due to miscommunication and confusion.
  • UK professional bodies such as the royal colleges and the health education bodies across the UK should develop guidance for healthcare professionals on the equity and ethical challenges and limitations of applying PRS testing in patient care and population health programmes

The importance of diverse data was also highlighted on the section pertaining to AI in medical device use. This is especially important when building algorithms, as a one-size-fits-all approach is insufficient.

For instance, diagnosing and monitoring diabetes could lead to different results by gender and ethnicity if haemoglobin A1c (HbA1c) levels are included, because haemoglobin levels vary along gender and ethnic lines. This highlights the importance of fusing AI knowledge with a broader appreciation of physiology.

AI devices need to be designed and calibrated to take account of clinical impact, not just performance accuracy. A system which is set up to have high specificity – few false-positive results – may be impressive technically but it increases the risk that diagnoses just outside the treatment range are missed. This is where small biases can have a big impact, by making the difference between being treated and not being treated.

Conversely, a system which has high sensitivity – few false-negative results so hardly anyone gets missed – may lead to unnecessary treatments.

The Government has also released recommendations to improve AI use in medical devices to achieve equitable outcomes. The following of which are relevant to our sector:

  • AI-enabled device developers, and stakeholders including the NHS organisations that deploy the devices, should engage with diverse groups of patients, patient organisations and the public, and ensure they are supported to contribute to a co-design process for AI-enabled devices that takes account of the goals of equity, fairness and transparency throughout the product’s lifecycle. Engagement frameworks from organisations such as NHS England can help hold developers and healthcare teams to account for ensuring that existing health inequities affecting racial, ethnic and socio-economic subgroups are mitigated in the care pathways in which the devices are used.
  • The government should commission an online and offline academy to improve the understanding among all stakeholders of equity in AI-assisted medical devices.
  • Researchers, developers and those deploying AI devices should ensure they are transparent about the diversity, completeness and accuracy of data through all stages of research and development. This includes the sociodemographic, racial and ethnic characteristics of the people participating in development, validation and monitoring of product performance.
  • Stakeholders across the device lifecycle should work together to ensure that best practice guidance, assurance and governance processes are coordinated and followed in support of a clear focus on reducing bias, with end-to-end accountability.
  • UK regulatory bodies should be provided with the long-term resources to develop agile and evolving guidance, including governance and assurance mechanisms, to assist innovators, businesses and data scientists to collaboratively integrate processes in the medical device lifecycle that reduce unfair biases, and their detection, without being cumbersome or blocking progress.
  • The NHS should lead by example, drawing on its equity principles, influence and purchasing power, to influence the deployment of equitable AI-enabled medical devices in the health service.
  • Research commissioners should prioritise diversity and inclusion. The pursuit of equity should be a key driver of investment decisions and project prioritisation.