Data Led Prioritisation
Helping healthcare organisations to manage their wait lists through effective clinical prioritisation and service optimisation
Across the NHS, waiting lists are growing
Since the beginning of the COVID-19 pandemic, NHS waiting lists for new appointments have grown from 4.2 to 6.6 million, caused by a perfect storm of increasing demand, staff shortages and reduced availability of inpatient beds.
The time that patients wait for an outpatient appointment has also increased significantly, and the challenge of meeting follow-up demand on existing patients with chronic conditions (e.g. diabetes) reduces the capacity for services to accept new patient referrals.
In response to these mounting problems, NHS England have produced guidance that outlines a strategy to reduce wait times for elective care with a key goal being to prioritise vulnerable patients within waiting lists.
But how is this to be achieved under tight funding and resource constraints?
Our data-led methodology can help
Data-led prioritisation is a methodology that enables effective prioritisation of patients on a clinical list. This is achieved by the automated and periodic application of established clinical rulesets.
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These rulesets are based upon clinician practice, making them simple and intuitive to use and apply. Once defined, they can be applied quickly across the whole list at scale, taking the latest data into account, and gives an up-to-date assessment of each patient’s clinical risk.
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The automated risk assessments can easily be embedded within service design and management to ensure the efficient allocation of clinical resource to patients with the greatest clinical need, while simultaneously identifying means of freeing up capacity by flagging patients who may be suitable for lower intensity pathways.
Our approach is already working in diabetes
Our methodology was applied to outpatients in the diabetes service follow-up list at Guy’s and St. Thomas’ NHS Foundation Trust in London, after working with clinicians to define their diabetes-specific risk criteria.
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Applying these criteria to their full list identified patients with new and concerning information who were not scheduled to be seen for at least 3 months, and who should therefore be prioritised. It also identified a similar sized cohort of patients with new but clinically reassuring information who were scheduled to be seen sooner, even though their condition may not warrant it.
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Following these results, the data-led risk assessments are starting to become embedded within the diabetes service with a view to more efficiently identifying and managing these groups of patients.
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Conversations are already underway to expand our approach into other specialties beyond diabetes.
Recent coverage
Our work has been covered in a number of articles, including this recent story in the Evening Standard about how it helped patient Goerge Brown.