Last year in England, around 11 per cent of patients failed to attend an outpatient appointment. This equates to 5 million appointments a year. Non-attendance at outpatient appointments - known as did not attends (DNA) - has a significant impact on the NHS in terms of cost and increased waiting times.
Clinically, it results in missed opportunities to commence or change treatment.
DNA incidence can be measured in two ways: from the Department of Health quarterly activity return or by using aggregated data from the secondary uses service. In our experience, trusts have tended to focus on the quarterly activity return for internal performance management. However, outpatient SUS data provides a richer picture. The first graph shows that for most trusts there is good agreement between DNA rates from SUS and quarterly returns. Where there is disagreement, it tends to be because trusts have sent too few DNA records to SUS compared with the quarterly returns.
A number of approaches to reduce outpatient non-attendance are available. For example, using text messaging to provide reminders is gaining credibility. Our experience is that significant upfront financial investment and time are required to maximise this technology. A better understanding of demographic profiles would allow a targeted and more cost effective approach. Trusts and primary care trusts can exploit information about demographic characteristics to target patients most likely to miss appointments.
Analysis of SUS data suggests patient specific factors that can indicate the likelihood of non-attendance. The second graph shows that the deprivation quintile seems to be linked to outpatient non-attendance. Patients from the least deprived areas are more likely to attend.
DNA rates by age
The final graph compares DNA rates by age group. From the age of 20, DNA rates tend to fall to a minimum in the 70-74 age groups. They then begin to rise slightly.
Taken together, these demographic factors can be used to generate a model to predict the risk of a patient not attending an outpatient appointment. Trusts can use this data to aim resources at groups most likely not to attend.
Analysing routinely collected datasets can offer significant insights. One size fits all approaches to improving NHS services are at best a waste of resources, as they tend to be ineffective. Using calculated risk of non-attendance to tailor approaches is a much more powerful approach.