Spotlight on quality
Last month the college’s Quality Improvement lead Amar Shah
brought you the first of a series of QI case studies, which we hope
will inspire ideas for quality improvement in your areas of
The first one focused on Durham and Darlington Child and
Adolescent Mental Health Services Senior Management Team, which is
part of Tees Esk and Wear Valleys NHS Trust.
This month, we’re looking at work done by the East London NHS
Case study: East London Memory
Memory Clinics have been set up around the UK in order to
increase levels of diagnosis of dementia.
A Prime Minister’s challenge was set that each CCG area in the
UK would achieve a diagnosis rate of 66% of the expected population
and the two main performance indicators are that
It is expected that this is achieved in 95% of all cases.
The three memory clinics in East London were achieving the Prime
Minister’s Challenge, but performance against the 2 KPIs was
How did QI help?
The three memory clinics met with the QI team and the Deputy
Director and through the use of QI tools including flow-charting,
nominal group technique and affinity diagrams, the services
identified a number of key themes:
A number of change ideas were implemented –
Weekly tracking of wait lists against the KPIs
2 weekly meeting of Memory Clinic leads
Admin leadership enhanced
Better recording of “Do Not Attends”
Text messaging reminder service
Revealing clear areas to
The initial focus of the group had been on the inappropriate
referrals cohort, but it quickly became clear that the key to
addressing the issue was tracking the patient pathway and ensuring
systems were robust.
The effect of the QI approach has meant that within 6 months,
waiting times for referral to 1st appointment had
dropped to below 6 weeks, and over 95% of patients are now
receiving their diagnosis within 18 weeks. This was achieved
despite a 30% increase in referrals during the same period.
In early discussions with the staff team, it became clear
that there was a mismatch between how they were doing and how they
believed they were doing.
The common belief was that they were working as hard and as fast
as possible – that patients were getting seen, and that if further
improvement was expected then extra staffing would be required.
However, frontline staff were unable to say how many patients
were waiting to be seen and how long they were waiting.
We resolved this by making performance visible to all, through a
weekly report that showed how many patients were waiting to be seen
for a first appointment and for diagnosis, and what was the longest
wait against these two measures.
It became clear that there was a lack of standardised
process and we were able to identify patients who had slipped
through the net. We resolved this by redeploying resource to
create an administrative lead to monitor the admin processes.
Because admin staff were being accommodating to patient
requests, there was often very long waits and multiple failed
Non-attendance was not getting recorded properly and
consequently we were losing control of when to discharge patients
who were disengaging from the process.
We implemented a robust non-attendance protocol that ensured
only one further appointment was offered after two non-attendances,
with discharge following three non-attendances.
In order to support service users and carers to be aware of
upcoming appointments, we also introduced a text messaging reminder
‘Model for Improvement’
The entire work used the Model for Improvement as the quality
improvement method, with a range of tools being used at different
stages. Flow-charting, nominal group technique and affinity
diagrams were utilised to understand the system, and involve the
whole multidisciplinary team in developing change ideas.
PDSA cycles were used to test and then implement new ideas ie
the text messaging reminder service was prototyped in Tower
Hamlets, embedded into daily practice, monitored for a month, shown
to have a substantial impact on “Did Not Attend” rates and then
scaled up and spread to the other two Memory Clinics across East
Data was shared through a dashboard of measures, including the
two key performance indicators. These were visualised as run
charts, and over the course of the project, it is possible to show
sustained improvement in waiting times.