What we need to look at in the coming months and years is how to recover equitably from the pandemic.
The NHS waiting list in England is currently around 4.7million and is likely to double by the end of 2021. Treating all these people fairly will be a greater challenge than merely a first-come-first-served approach. Inequality runs deep – those who already received inequitable health are more likely to present later, with more acute and progressed disease than others. A health and care system seriously committed to tackling systemic unfairness and inequity needs to look at the person, not simply the length of their wait.
There are now many people to be seen – more than ever before on the RTT waitlists – with NHS Providers believing this will require a multi-year strategy to recover.
Addressing unprecedented demand will need different thinking and prioritisation measurements to consider individuals’ needs beyond their presenting conditions if we are to improve outcomes. The reasons for these causes must be understood – tackling inequalities has never been an easy undertaking. If we are serious about building a better, fairer health system that combats unwarranted variation, we must work together to reduce the waiting lists through a population health management approach.
This will require health and care systems to work together to better understand their populations, so they can use the information they hold about people and places to plan and commission smarter services that use data to predict, identify and manage risks. Central to the approach are the four Is: infrastructure, intelligence, intervention and incentives. A longitudinal record that brings data together every day is the infrastructure to build your intelligence and interventions upon.
To address waitlists, we can use this infrastructure as the intelligence platform. At the system level, the population can be moved away from acute Trust sites with each place’s diagnostic capacity mapped against the need with people receiving their diagnostics in non-acute spaces, community hospitals or advanced GP practices. The information would be available instantly via Health Information Exchange, or in a near-real-time longitudinal population health record, so an MDT can review a person’s RTT pathway virtually and take the next clinical steps. This approach is seen in new MSK pathways that use longitudinal records to screen hip and knee referrals, directing patients to exercise and prehabilitation services before surgery, with prioritisation also based on selfreported pain and quality of life.
Combining the data allows the health and care system, the place and the neighbourhood to understand how people use resources, and the inequity that exists with their groups and neighbours. Cerner clients have used data linked to HealtheIntent® to identify those with learning disabilities are 6.36 times more likely to be admitted with flu, and those with chronic kidney disease 2.5 times more likely to be admitted with COVID as priority cohorts for vaccination, and multi-morbidity increases risk. Those with fouror-more long-term conditions are at 40% risk compared to those with one, who are at 15% risk of admission.
Data science can begin to segment the figures to show people with mental health conditions are the highest users of urgent care, with the greatest levels of smoking and alcohol dependency. Some 60% of schizophrenia patients have more than one condition – most often asthma or diabetes – to which fewer than half meet the best practice guides.
The population can be analysed by age, sex, risk, condition, first language spoken, deprivation, ethnicity, social care package, as well as RTT priority group, and weeks waited. Finding the people who have historically been underserved, presented late and have the poorest outcomes, and addressing their issues is a priority.
The difference that population health makes is the data is person-focused. You can identify those most at-risk within the ICS to the neighbourhood – to the person – and involve them in the decision-making process. You can prioritise waiting lists, prioritise those in greatest need and put the intelligence into the redesigned workflows of the staff treating them so that they become outcomes-focused, not activity-driven.
At Cerner, we believe this is our part to play: to reveal the unwarranted variation hidden within the data and signpost best practice to provide clients with the tools for recovery and the learning to share, and putting it to practical use.
It is not acceptable to leave inequality unaddressed in the recovery – change is possible, better is possible, so let’s build on the best in health and care we have experienced during the pandemic.
