Deborah Richards
Head of Commercial UK & Ireland, Alexion, AstraZeneca Rare Disease
Find out how AI and various schemes are revolutionising rare disease diagnosis, bridging healthcare gaps and offering hope for patients facing barriers in access to care.
People living with rare diseases face many hurdles, including lengthy diagnostic journeys, few treatment options, and limited awareness of their conditions.
Rare disease diagnostic challenges
On average, it takes nearly five years — and often longer — to receive a rare disease diagnosis, a journey that often involves multiple doctors, specialists and misdiagnoses.1
Living with a rare condition can make nearly every aspect of life difficult. Patients and caregivers often experience economic hardship, greater social isolation, significant delays in treatment and poorer health outcomes.
Delayed diagnosis can also cause downstream costs to health services. In the past decade, undiagnosed rare diseases have cost the NHS over £3.4 billion. Patients with rare diseases attend hospitals twice as frequently as others, costing the NHS four times more on average.2
Accelerating diagnosis with AI
The emergence of AI holds vast potential for all aspects of healthcare, revolutionising patient care through improving earlier diagnosis, accelerating drug development and proactive digital monitoring for better outcomes.
The complexities of rare diseases lend themselves to the digital revolution we live in. We initiated Project Find and commissioned Mendelian, the UK-based digital health company, to build software to help doctors identify and diagnose rare disease patients faster.
Our ultimate ambition is to reimagine healthcare. By harnessing emerging technologies and digital health, we have the potential to drive the step-change in healthcare to take more patient-centric approaches and better serve patients living with rare diseases.
Undiagnosed rare diseases have cost the NHS over £3.4 billion.
Ensuring access to care and medicines
Advances in science have enabled new life-changing and potentially curative treatments for rare and ultra-rare diseases. However, the unique characteristics of rare diseases, such as smaller patient populations, higher treatment development costs and uncertainty around the evidence base generated from clinical trials, create challenges for healthcare systems when assessing the value of these medicines.
Various schemes aim to enable early access to new medicines for rare diseases. The Early Access to Medicines Scheme (EAMS) gives patients with life-threatening or seriously debilitating conditions access to new medicines that do not yet have a marketing authorisation when there is a clear medical need.
The Innovative Medicines Fund (IMF) was created for patients with non-cancer conditions, including rare diseases, to benefit from access to new medicines while additional evidence is collected on their clinical effectiveness. However, since its 2022 launch, the £340 million fund has not yet been used for its primary intended purpose, due to overly restrictive criteria. Evolving the IMF design could allow patients access to new medicines earlier than anywhere else globally.
Hope for improved rare disease care
Championing policy initiatives, overcoming diagnostic hurdles, bridging gaps in access to care and medicines, as well as fostering collaborative strategies in genomic medicine, will help us unlock the diagnostic journey for patients. Together, we can pave the way for a brighter future for people with a rare disease in the UK and beyond.
[1] Zhang Z. Diagnosing rare diseases and mental well-being: a family’s story. Orphanet J Rare Dis. 2023;18(1):45. Published 2023 Mar 6. doi:10.1186/s13023-023-02648-y
[2] Department of Health and Social Care, NHS England. Thousands of patients to benefit from quicker diagnosis and more accurate tests from ground-breaking AI research. Available at: https://www.gov.uk/government/news/thousands-of-patients-to-benefit-from-quicker-diagnosis-more-accurate-tests-from-ground-breaking-ai-research#:~:text=In%20the%20past%20decade%20undiagnosed,times%20as%20much%20on%20average. Last accessed: February 2024.
Date of preparation: February 2024
Document number: UK/NP/0153
