University Hospitals of Coventry and Warwickshire

Dr Penny Kechagioglou

Digital Health and Cancer

Digital technology adoption and diffusion in healthcare, involves whole systems change, including robust clinical, organisational and system leadership, training and education of the whole healthcare workforce.

In the case of people with cancer, digital health impacts all stages in the cancer patient journey, from prevention (SMS screening reminders) to diagnosis (Artificial Intelligence use for imaging reporting), through to treatment (Electronic Health Records) and survivorship (Remote Patient Monitoring - RPM, Patient Initiated Follow Up – PIFU).

As such, real world digital data capture and analysis should seamlessly follow the patient pathway from diagnosis through to survivorship.

Clinicians and researchers specialising in cancer need to ensure that all populations of cancer sufferers are equally represented in research, cancer registries and audits, as well as other tools of real-world data capture.

The Role of Digital Data in the NHS Long Term Plan for Cancer

Treating the right patient, with the right treatment at the right time, every time, is the ultimate goal for our healthcare system. Digital data driven decision-making, enabled through digital technologies can fulfil the goals of the NHS Long Term Plan for cancer, which includes:

• faster diagnosis (AI reporting, access to EHR);

• secure and accurate data sharing between Rapid Diagnostic Centres and GPs/Cancer Multi-Disciplinary Teams (interoperability of systems);

• data (AI) - driven personalised care planning and

• incorporation of genomic, imaging and other clinical information in one digital space to empower informed decision-making.

Real world data gathering and analysis, enabled through digital technology (cancer registries, national audits, hospital datasets) can support clinical leaders individualise healthcare and improve population health. It’s important that we address the needs of all patients, maximise access to digital care for all and upskill people to use digital tools to access self-management information and other services (20 percent of the population lack basic digital skills and do not use technology at all).

We can also learn more about individual cancer patients, groups and communities and target care better, through linking key datasets together, including genomics, imaging, liquid biopsies, clinical presentations and clinical outcomes.

How can digital data help us improve the health of individual patients and the diverse populations we serve?

Many healthcare providers have introduced shared electronic care records which enable patient care to be delivered anywhere, safely and effectively. Patients are also able to access their records and schedule their care remotely, through patient portals linked with electronic health records. As such, patients are better informed and more involved in their care, leading to self-management optimisation and better patient experience.

Data analytics enable us to understand patient outcome measures and use those to improve care in a continuous circle of learning and improvement. As we learn about patient presentations and outcomes, we can use those to develop algorithms and decision support tools. However, these tools are only as good as the data we use to create them and therefore, data exclusion from people and populations with protected social characteristics, ethnic minorities or even elderly, could render those decision-making tools inaccurate. This can have catastrophic consequences in terms of population health and long-term outcomes including morbidity and mortality.

“Clinicians and researchers specialising in cancer need to ensure that all populations of cancer sufferers are equally represented in research, cancer registries and audits, as well as other tools of real-world data capture.”

In the case of cancer, we need to ensure that disadvantaged populations and ethnic minority populations are represented well in digital real-world data analyses and clinical research. These populations tend to be under-represented in cancer screening programmes as well as research programmes and they often have worse cancer outcomes than affluent populations and white populations.

Why Data Diversity is Important in Cancer

There are currently more than 9.5 million deaths from cancer annually and the majority occur in low and medium income countries.

In the UK, the COVID pandemic has severely affected cancer services with thousands of patients with cancer not being diagnosed on time and 30 percent of clinical trials being stopped. Black and ethnic minority populations with a higher risk of complications and death from COVID were also under-represented in the cohort of people diagnosed with cancer during the pandemic in the UK. What’s more, black women are almost twice as likely to experience mortality from breast cancer than white women. By ensuring diversity in cancer data and including diverse populations in cancer clinical trials, we can improve outcomes for the most vulnerable populations.

Data analytics are now more important than ever in order to understand the extent of the impact from the pandemic and not only restore cancer services to the pre-covid levels, but also ensure a responsive and personalised care for everyone. The use of genomic testing is important in this regard, as it enables clinicians to give the right treatment to the right patient, can improve progression free survival in some cancer patients and can reduce costs from unnecessary treatments. One example is liquid biopsies which look for tumour DNA in the blood in people who suffer from cancer, to enable clinicians personalise their patients’ treatments.

Looking into the future and using liquid biopsies as a population screening tool, we can potentially prevent cancer spreading and treat cancer when it is still in the early (and curable) stages. In addition, the knowledge of specific genes causing disease, the presentation of such diseases and their risk factors can be used to develop risk scores (polygenic scores) and decision-making tools for the management of all diseases. Finally, we can apply preventive measures to stop disease processes at a very early (perhaps sub-clinical) stage.

What Measures can be Put in Place to

• Strong leadership at the organisation as well as the integrated care system level is essential to drive data diversity and equality.

• Clinicians as well as end users (patients and the public) need to be engaged in digital design thinking, in partnership.

• National and regional champions of digital health can lead on the data equality and the research diversity agenda.

• More can be achieved and quickly, with effective partnerships between the NHS and commercial sector, primary, social and voluntary care providers, clinicians, patients and the public.

• The continuous education of the healthcare workforce, patients and the public on digital health and data protection, needs significant investment. This will ensure the acceptability and scale-up of the systematic real-world data collection, the quality and equality of data collected, as well as the lawful use of such data.