Authors of a new study argue that silo’d data is hindering the NHS’s efforts to reduce waiting lists
"Data collection and analysis are crucial and should be performed across our entire health system, providing robust intelligence that we can act on to address pinch points in the system. Yet currently much of these data are in silos or inaccessible, which is not in the best interest of the patient.” Amitava Banerjee, professor at UCL and honorary consultant cardiologist at Barts Health NHS Trust
Data intelligence is key to tackling the lengthy NHS waiting lists caused by the Covid-19 pandemic, a new study concludes.
The paper, published by researchers from London, Belfast and Edinburgh in the Journal of the Royal Society of Medicine, emphasises the urgent need to address the backlogs in cardiovascular disease and cancer in particular. There are now more than six million people on the NHS waiting list for elective procedures.
The authors argue that Covid-19 has had a disastrous impact on waiting lists. In the first wave of the pandemic, admissions and emergency department attendances for heart disease dropped by over 50%, urgent referrals for cancer dropped by over 70% and chemotherapy attendances dropped by over 40%. “Similar declines have been shown across surgical and endoscopic procedural activity,” the paper says. The authors note that while health care data is already gathered as part of standard care or performance metrics, “disease communities are collecting, mapping and reporting their own data separately – these silos are not in the public health interest of the population.”
Better collection and analysis of data can help the NHS address the problem, the authors say.
Lead author Amitava Banerjee, a professor at UCL and honorary consultant cardiologist at Barts Health NHS Trust, said: “We need to get to grips with the current crisis. Data collection and analysis are crucial and should be performed across our entire health system, providing robust intelligence that we can act on to address pinch points in the system. Yet currently much of these data are in silos or inaccessible, which is not in the best interest of the patient.”
The indirect effects of the Covid-19 pandemic are predictable, the paper says, and should be “informed by data intelligence.” It adds: “Initially, researchers could use pre-pandemic data to create predictions of direct and indirect effects based on demography, assumptions about viral dynamics and underlying risk.”
The authors note that in the last two years, the landscape and regulation of health data use have been transformed to enable “urgent, policy-relevant analyses.” The CVD-COVID-UK/ COVID-IMPACT project, for example, has collated and linked data from different national datasets, including Covid-19 surveillance data, disease-specific registries, cardiovascular risk factors and patients’ history of medications and vaccination.
Co-author Cathie Sudlow, a professor at the University of Edinburgh and director of the BHF cardiovascular data science centre, said: “Deploying health intelligence to inform health policy and its implementation must become the new normal. We need to respond to the crisis through action that is evidence-based and driven by insights derived from the data, rather than by supposition.”
We welcome this valuable paper led by researchers at University College London’s Institute of Health Informatics. The NHS was already struggling at the start of the Covid-19 pandemic, but the backlog created as a result of the pandemic has put an impossible burden on staff and resources. The UK has some of the richest health datasets in the world, and FCC has been making the case for the intelligent use of it in helping to address problems across health and care systems since 2017 – but to make that possible, it will involve moving data out of silos and working collaboratively. HDRUK’s CVD-COVID-UK / COVID-IMPACT project has made an encouraging start in breaking down those silos, and we need many more to follow its lead.