In the last decade, total outpatient appointments in the UK have nearly doubled, rising from 63.2 million to 118.6 million per year. To meet this growth in demand while maintaining Referral to Treatment (RTT) performance, NHS Trusts need to fundamentally change their outpatient models. And while there are many contributing factors to this transformation, one key focus area has to be improving clinic utilisation.
It might seem as though improving clinic utilisation will take the same approach as improving theatre utilisation – addressing scheduling, did-not-attends and patient cancellations. But in our experience, outpatients poses a unique challenge – it’s hard to see clinic utilisation.
That’s because patient administration systems typically include closed clinics, sporadic clinics and overflow clinics. Meanwhile, capacity is often kept on spreadsheets or paper diaries rather than key IT systems. And clinics for some services include walk-in clinics, hot clinics or preoperative assessment clinics which all offer patients flexibility whilst allowing clinicians to use empty appointments to carry out other vital clinical duties.
So, it’s essential to validate the data. For instance, data at an NHS Trust we recently worked with showed 56 per cent utilisation of geriatric medicine clinics. This set alarm bells ringing. But we found that many of the clinics had slots at 10-minute intervals from 9am to 5pm to allow maximum flexibility for walk-in patients. They expected to only use a few of these appointments each day, giving time for the clinical team to do their other duties. Excluding these slots from the data revealed a more accurate picture of clinic utilisation at 87 per cent. This shows how utilisation that hasn’t been validated with clinical and operational teams can be misleading.
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To overcome the problem of misleading data, we first cleanse it. This finds and removes errors from the raw data using a set of rules, revealing accurate utilisation. For example, systems can record clinics as being 24 hours long, skewing results. Our rules scan the data for any such error. They cover all date ranges, missing data, materiality of data, matching clinic codes across capacity and activity data, clinic codes appearing in multiple specialties, and run times for clinics.
With clean data, we then generate dashboards to highlight potential data quality issues. These might include:
You can then filter all this by specialty and sub-specialty to identify the source of error.
Make it visual – by visualising data, it’s easy for everyone to see opportunities. It’s also more likely to be used than rows of Excel data.
Provide metric drilldowns – users should be able to drill down into clinic, clinician and date levels. For example, it’s important to see nurse- and consultant-led clinics. Nurse-led tend to have lower utilisation figures as their capacity is flexible so they can do other clinical activities. Consultant-led clinics are a priority given the cost of their time.
Make the opportunity realistic - you need to be able to add at least one full outpatient appointment to a single clinic for it to be an opportunity. This principle ensures we don’t overstate the opportunity by adding together lots of little opportunities that when brought together look like a large opportunity.
Rely on the activity data - where possible, we use clinic activity data as we believe this is more accurate.
By creating good data on clinic utilisation and sharing it effectively, you can improve productivity. This will increase RTT performance, efficiency and patient experience.