Alison's thoughts on measuring for Access QI
Written by Alison Redpath
24 February 2020
Hi, I’m Alison. I’ve worked with data to support improvements in healthcare for over 10 years now, so you’d think a new project would be straight forward; it never is. Here are a few examples that have been taxing from a data perspective.
Access QI is looking at pathways, and data is usually stored in silos – GP database, hospital PAS, radiology, labs, etc. We can identify data by pathways IF there is an identifier somewhere along that pathway. Some of the pathways chosen for Access QI are fairly straight-forward. For example, looking at a skin cancer pathway the referrals come in marked “Suspicion of Cancer” to Dermatology, or looking at the Child and Adolescent Mental Health Services (CAMHS) all referrals are directly to the service. Others are not so straight forward, for example haemorrhoids, where referrals come under “General Surgery” and therefore it is not possible to pick out these referrals electronically.
Collecting extra data for a project is always controversial. There is a very real issue of putting too much burden of data collection on those front line staff who are pretty busy (understatement?!). However if we don’t have the right information for the project we are running, we can’t be sure about what has happened and whether our theories are sound; in other words, the project might tell us nothing. In an ideal world we’d have all the information we needed at the touch of a button, but almost always we need to go for a compromise. My approach is to collect the minimum necessary to be able to answer all the questions needed for the project, and to get this right it is vital to involve those who are going to be doing the changes and collecting the data before you set any collection templates up.
Demand and Capacity Balance
I have a lot of experience of using a Demand, Capacity, Activity and Queue (DCAQ) approach, mainly within mental health settings, yet (again) this project brings new challenges. The first measurement plan was developed with colleagues from IHI who have a lot of experience in this field. They introduce the concept of the demand and capacity measure. It’s not how I’ve worked before, and we’ve had a few challenges with defining the measure for the Scottish audience, but I am hopeful that when we start to get data we will gain an understanding of how practical the measure is.
We are very near the beginning of the project still, with the first data submission expected in the next week, yet we already need to think about the end of the project. How can we capture our learning? What is relevant? We hope to know the answers to these and more questions soon…