Chris Yeowart, general manager, business development at Wellbeing Software, discusses how integrating diagnostic services can address the cancer care crisis.
The COVID-19 pandemic has placed significant strain on the NHS and its services. So much so it is having a negative impact on the diagnosis of critical illnesses, including cancer. Recent research suggested that a 25% backlog in patient referrals caused by the pandemic could lead to 181 additional lives lost as curable diseases become terminal while waiting for treatment.
The same research also suggested that more investment into diagnostic cancer equipment and technologies could significantly improve patient outcomes and ensure earlier diagnosis. The major disruption to treatment has led to there being thousands of ‘missing’ cancer patients, people who would have received a diagnosis in a normal year but in 2021 are walking around with cancer without knowing it.
The latest NHS figures suggest there are currently 16,000 people waiting for more than 62 days to be diagnosed, of whom an estimated 12% will have cancer. Although there has been an increase in referrals experts believe it will be March 2022 before the backlog is fully cleared.
So, how can technology help address the need for faster and more efficient diagnosis?
The case for integrating diagnostic services
It’s clear that to improve patient outcomes and ensure early cancer diagnosis, organisational change within healthcare services is needed. Hospitals and laboratories need to adjust how they operate to increase their diagnostic capacity, in particular speeding up the process between tests and results. This can be achieved by optimising their resources across lab medicine, pathology, and radiology AI.
Patients will typically receive several diagnostic tests to determine whether they have cancer or not, and these results are often reported at different times and through different systems. A number of these processes are carried out and reported back through siloed, legacy platforms which can create bottlenecks in diagnosis. By providing a single integrated view of tests, images, reports and results, clinicians can streamline diagnosis and commence treatment quicker.
For example, with integrated radiology and pathology systems communicating directly into an oncology information platform, any clinician involved in a patient’s case – from radiologists to oncologists - can access all the information they need to make an informed decision from diagnosis to treatment plan. This is particularly powerful for Multi-Disciplinary Teams (MDTs), which are made up of specialists from different departments and meet frequently to discuss complex cases.
With access to better data, there is also the opportunity to deploy business intelligence and clinical workflow optimisation tools to give those in charge of treatment better visibility of cases and the resources available to manage them.
The advantages of AI in cancer care
The impact of the COVID-19 pandemic has also led to Trusts accelerating their adoption of digital solutions which is now more apparent than in diagnostic imaging. AI-based tools can recognise patterns, which could reduce clinical workloads and more importantly, save lives. It’s thought AI can detect upwards of 50 different types of cancer as well as changes to a person’s genetic make-up, which means it can locate places where cancer may develop in the future.
The argument for the use of artificial intelligence is further strengthened by the fact that there is a national shortage of radiologists. According to research from the Royal College of Radiologists only 55% of vacancies will be filled by trained consultants this year. Add to that an estimated 350,000 new NHS cancer patients each year and it demonstrates the need to move away from traditional segmented ways of working.
Improving patient outcomes
Those patients who receive an early diagnosis have the best chance of curative treatment and remission. However, only 55% of cancers are detected early in England and this figure will likely reduce in the wake of the pandemic. For these reasons alone, understanding and enabling efficiencies in the diagnostic process should be top of the NHS agenda.
