---
# System prepended metadata

title: AIRRLOCK - Vision
tags: [airrlock]

---

---
section_title: vision
word_limit: 550
images_accepted: true
references_accepted: true
guidance:  >
    Explain how your proposed work:
        - is of excellent quality and importance within or beyond the field(s) or area(s)
        - has the potential to advance current understanding, or generate new knowledge, thinking or discovery within or beyond the field or area 
        - is timely given current trends, context, and needs 
        - impacts world-leading research, society, the economy, or the environment
    Within the Vision section we also expect you to:
        - identify the potential direct or indirect benefits and who the beneficiaries might be
        - clearly articulate how your work will increase coherence with and across current UK and international DRI investments
        - explain how you will meet the strategic aims of the funders or government set out in the opportunity and consider other stakeholders’ needs and views as relevant
        - demonstrate a clear commitment to collaboration and pro-actively seek to work with others so that the whole becomes greater than the sum of the parts
notes: >
    - Multi-modal technical networks
    - Practitioner 8000 GPU safe and secure
    Unique leadership model NHS + Academia
    Clear guiding purpose peace time/war time
    Canary in the mine
    With one singular mission ... the first mile
    Using a socio-technical solution
    > When new threats emerge, well established public health systems rapidly identify cases and evaluate sources, clinicians provide early descriptive case reports, and laboratories develop diagnostics and characterise pathogens. Clinical science is markedly less agile. We lack the tools to answer key questions rapidly. Who is susceptible, and why? What are the mechanisms of disease? What are the sites and dynamics of pathogen replication? How can early cases be identified and stratified? What is the clinical utility of new diagnostics? What treatments might work?
    - ?Dunning JW, Merson L, Rohde GGU, Gao Z, Semple MG, Tran D, et al. Open source clinical science for emerging infections. Lancet Infect Dis. 2014;14: 8–9. doi:10.1016/S1473-3099(13)70327-X



---
# Vision
We bring together more than 15 hospitals, including academic and community, women’s and children’s serving a population of more than 10 million people, connecting to 6 Sub-National Secure Data Environments (SNSDE). 

We have assembled technical excellence in multi-modal, multi-scale, and multi-morbid data encompassing electronic health records, free text, imaging, digital pathology, pathogen and host genomics, and more. 

We have already come together to bid for Practitioner: a £150m proposal for the UKRI/DSIT Artificial Intelligence Research Resource (AIRR) to safely provide thousands of GPUs for research using sensitive personal data. We come together again to connect our hospitals to that compute and other national research infrastructure. 

We meet the challenge of building sustainable Digital Research Infrastructure through an urgent research question of pressing importance: pandemic preparedness. But, we do this aware that productive activity between pandemics is essential to maintain effective systems. So we focus, in ‘peacetime’, on infection in cancer and anti-microbial resistance, a problem relevant to all populations, and all age groups.

We are a team that has learned a hard lesson: that neither interoperability nor federation alone can unlock this problem. The scientific victories of the recent pandemic relied on data collected by hand. [ISARIC4C](https://isaric4c.net/) relied on more than “2,648 frontline NHS ... staff and volunteer medical students”. National assets such as the 25,000+ human genomes in the [GenOMICC](https://genomicc.org/) study are paired with fewer than 30 clinical data points. Endeavours such as the NIHR Health Informatics Collaborative have struggled to scale and recruitment to clinical trials is dwindling, [costing the NHS around £360m](https://www.gov.uk/government/publications/commercial-clinical-trials-in-the-uk-the-lord-oshaughnessy-review/commercial-clinical-trials-in-the-uk-the-lord-oshaughnessy-review-final-report).

This is because we have all underestimated the challenge of the **first mile** of the data journey: the connection to the hospital. This time our solution is **socio-technical**. 

Rather than seeing the movement of data as a single action, we interpose a staging zone inside the NHS that connects NHS Digital, Data and Technology (DDaT) teams with their research partners. This zone is comprised of AIRRLOCK (a transit hub for data) and a TRE (connecting communities by enabling the researcher-to-data paradigm). This allows research teams to work with DDaT specialists on data extraction, algorithm deployment, standardisation, concept mapping, and iteratively improving pipelines. The same infrastructure supports rapid testing and re-calibration of AI/ML models against local populations. Together, we create a 'digital third space' for community collaboration within and beyond the NHS: a knowledge repository for best practices, a code repository for software-defined infrastructure, and a communication forum for all.

We see NHS hospitals as more than sources of data. They provide the knowledge and context that gives raw data its value. They are the natural place to develop and evaluate the ‘human-AI’ teams that will run the algorithms supporting future healthcare. AIRRLOCK prepares us for the latter, whilst delivering the former. 

By explicitly linking these endeavours, system efficiency is improved, and opportunities to test and trial interventions are unlocked. NIHR clinical trials facilities embedded in hospitals can partner with academic and industry teams to build evidence of safety, efficacy and impact, with models retrained and recalibrated in situ for the local patient population. 

By integrating rather than separating, we build a national data and algorithm stewardship community through technology and team science that will position NHS hospitals and UK health data researchers to be internationally competitive in deploying not just developing algorithms.




