Written by:
Head of Commercial Digital & IT, International & Japan, AstraZeneca
VP Medical, International, AstraZeneca
As new technologies and online capabilities continue to advance, it’s becoming increasingly clear that innovation and digital tools have an important role to play in the future of healthcare. This is particularly true when we look to address unmet patient need in a post-pandemic world, ensuring that our health systems are both resilient and future-proof.
At AstraZeneca, our ambition is to make health happen for people, society and the planet, and one of the ways we are working towards this is by driving the development of more innovative, patient-centric solutions – harnessing the potential of innovative tools within the healthcare space.
Leading the way in early-stage detection with artificial intelligence (AI)
Through our A.Catalyst Network – an interconnected and dynamic, global collective of more than 20 health innovation hubs – we are partnering with diverse stakeholders, including healthtech start-ups, local governments and cross-industry partners, to address current challenges and increase affordable and equitable access to healthcare. The network embodies our commitment to advancing cutting-edge science, building a sustainable healthcare ecosystem and developing holistic, life-changing solutions for patients.
One such solution was created by our network partner, Qure.ai, developers of deep learning algorithms for the interpretation of radiology images. Their chest X-ray interpretation tool, ‘qXR', is able to automatically detect and localise up to 29 abnormalities, including those indicative of possible lung cancer.1,2 There are several features of chest radiographs (such as sharply circumscribed nodules or masses, those with irregular margins, and those with ill-defined lesions) that can indicate the presence of lung cancer.1 The CE-marked qXR algorithm can not only detect lung nodules with these features with high levels of accuracy, but also mark out the position and size of these nodules.1,2
Globally, chest X-rays are the most commonly ordered diagnostic imaging test, with millions of scans performed every year.1, 3-5 Although chest X-rays are easily performed in primary care and referral settings, the interpretation of these X-rays requires significant skill and experience, and lack of expertise in reading the imaging can result in missed or delayed diagnosis.4,5,6 A study conducted by Qure.ai demonstrated a 17% improvement in sensitivity when using AI to interpret chest X-rays, compared to radiologist readings.1 Such aids in early detection can have considerable long-term benefits for medical professionals in their efforts to tackle lung cancer.7 It can also mean lower cost per-life-year saved.1
As part of a strategic collaboration across multiple hubs, the A.Catalyst Network will work with Qure.ai to further explore the application of deep learning algorithms to identify patients with suspicious radiographic lung abnormalities and support their referral to arrive at a firm diagnosis. The collaboration will also focus on overcoming barriers that limit access to diagnostic tools to support early lung cancer detection, and thereby reduce mortality rates and improve patient outcomes.
Our partnership with Qure.ai aims to harness and scale-up the use of this technology to improve early-stage detection of lung cancer in low- and middle-income countries. Activities such as these represent our wider, global commitment to patients with lung cancer, formalised through our partnership with the Lung Ambition Alliance, which was established with the goal of eliminating lung cancer as a cause of death. In this, we continue to work toward the development of transformative new possibilities, so that we may better support the patient’s journey through diagnosis, treatment and disease management.
Improving vaccine confidence through digital engagement
As scientific leaders in our respective markets, we believe we have a duty of care to the patients and people living in our communities, not only in terms of diagnosis and treatment, but also with regard to disease awareness and education. By empowering patients to become more active participants in their own health, our aim is to create a more sustainable healthcare model that both positions them at the centre and enables them to make informed decisions that help to ensure their wellbeing.
Our 360º iVaccine Confidence Building programme established a holistic framework for building greater understanding and awareness of COVID-19 within the medical community and among the general population in a number of focus countries – principally, developing nations in Latin America and East Asia. We harnessed our digital capabilities and delivered tailored content to establish a foundation on which to build a greater understanding of COVID-19 at a time of great need.
Rolled out in phases, the initiative was structured around three strategic pillars: a social-media-based strategy that sought to identify and gather insights that would shape our next steps; a medical education programme aimed at key external experts and healthcare professionals (HCPs) that would allow them to better educate others about COVID-19; and a digital awareness campaign that would promote greater awareness and understanding by utilising a range of bespoke, educational resources to support communication and engagement. To date, more than 150 digital external experts have been identified and engaged with, addressing concerns that they may have.
Delivering the healthcare of tomorrow
Both our partnership with Qure.ai and the 360º iVaccine Confidence Building campaign were recently recognised at the Reuters Pharma Awards 2022, and are representative of our enduring ambition to revolutionise the healthcare space, putting patients at the centre of our focus.
As leaders in the pharmaceutical industry, we understand that it is our responsibility to establish a robust, sustainable healthcare ecosystem that serves people, society and the planet, both now and into the future. Taking bold action through initiatives and partnerships such as these, we are reimaging and reshaping approaches to healthcare, ensuring patient experiences and outcomes are considered – and indeed are viewed as a priority across the entire ecosystem.
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References
1. Mahboub B, Shuri H, Al Bastaki U, et al. Comparing AI system to identify nodules on chest X-rays against radiologists. 2020. Submitted manuscript.
2. Qure,ai. Automated Chest X-ray Interpretation – qXR. 2020. Available at:
https://www.qure.ai/qxr.html. Accessed 19th November 2020.
3. World Health Organization. Ionizing radiation. Chapter 1: Scientific background. 2016. Available at: https://www.who.int/ionizing_radiation/pub_meet/chapter1.pdf?ua=1. Accessed 19th November 2020.
4. Raoof S, Feigin D, Sung A, et al. Interpretation of plain chest roentgenogram. Chest. 2012;141(2):545-58.
5. Coche EE, Ghaye B, de May J, Duyck P (eds.). Comparative Interpretation of CT and Standard Radiography of the Chest. 2011. Springer-Verlag Berlin Heidelberg.
6. RSNA. Radiologist Shortage in the U.K. Continues to Deepen. 24th May 2019. Available at: https://www.rsna.org/en/news/2019/May/uk-radiology-shortage. Accessed 19th November 2020.
7. Gossner J. Lung cancer screening-don't forget the chest radiograph. World J Radiol. 2014;6(4):116-8.
Veeva ID: Z2-3580
Date of preparation: December 2022