I have previously highlighted the indicator burden on NHS trusts and also explained why I believe that refocussing on meaningful indicators has to be the way forward. However, we have to avoid a further proliferation of indicators, but rather shift away from operational indicators towards those that are outcome based and more representative of clinical practice.
I spend a lot of time in meetings with NHS leaders and staff and it is clear that data analytics has an increasingly important role to play in quality improvement. Dashboards are not dead, they are just diversifying, but if we are going to fully realise the benefits we have to ensure that our data analysts are embedded at the frontline. Being close to the frontline helps them better understand pathways, the way clinicians interpret data and how analytical outputs can be applied in practice. It is easy to forget that a data point is actually a person.
One of the ways we have been putting this into practice is through our work with NHS Improvement and the Getting It Right First Time (GIRFT) programme. GIRFT aims to reduce variation in the way services are delivered by sharing best practice. So, the starting point is to establish which NHS Trusts are achieving better outcomes which in turn requires an analysis of meaningful indicators. Since each GIRFT speciality is unique, this analysis has to be tailored. For example, our work on the data packs for breast surgery is very different from our work with geriatric medicine workstream.
In each case, we help build a picture by looking at system indicators. The Oldham Partnership has reflected this approach by creating “neighbourhoods” that are meaningful to local people through the thriving communities initiative.
For GIRFT, this is where clinician input is invaluable because we need to have an understanding of the service. This often begins by asking questions about clinical practice we are trying to find the answers to. The indicators need to be representative of the service and pathway they are measuring. Without appropriate indicators, we can’t have actionable insight.
In my next post I will outline the work we have been doing with the geriatric medicine workstream in more detail. https://www.linkedin.com/pulse/why-data-analysts-need-spend-more-time-frontline-jason-harries/