Thinking out loud: the Kurtosis blog
9 March 2018
Information's direction of travel needs to change
2 March 2018
What should we do while we wait for a new and better media container for data messages?
21 August 2017
Sometimes we have to modify the fullness hypothesis
20 January 2017
Patient journeys consecutively and rapidly
27 October 2016
Describing pain with numbers
20 November 2015
Friction. There's a friction in Flowopoly. It's an unsettling tension between—on the one hand—all the "faff" associated with putting on a Flowopoly Event (the tables, the boards, the labels, the cards, the easels, the whiteboards, the magnets, the red hotels, pretty much everything about it, in fact) and—on the other hand—this irksome sense I have that actually Flowopoly isn't something "special" at all. It isn't an "event". It isn't even a "gimmick". No. Flowopoly is actually just another way of looking at data.
13 November 2015
We all know it's important to do this, to use evidence (in the form of data) to inform our understanding and actions, but we somehow just don't seem to be able to do it—not the demand and capacity aspects of patient flow, at any rate. We're seemingly OK about using data to describe the "here-and-now-ness" of emergency care; but we're not very good at describing (or understanding) the general "big picture-ness" of it. What I mean by that is that we're reasonably comfortable using data to describe—for example—how many patients are currently breaching the four-hour target, or how many medical patients are currently boarding in surgical beds, or how many delayed discharges there currently are. But we're less skilled at using data to understand why these things are happening in the first place—what are the system dynamics that are causing the dysfunction?
6 November 2015
Flowopoly tries to make visible three aspects of patient flow, which we tend to categorise as: 1. How many? 2. How long? 3. How full? Using more specific terminology, How many? means attendances (or admissions), How long? means length of stay, and How full? means Fullness, or occupancy.
2 November 2015
I've been preoccupied for some time with the idea that when it comes to getting data embedded into decision-making, it's not just a question of identifying the right data to look at, or even the right way to analyse that data; it's also about finding—or setting up—the right forums in which to discuss the data.
14 October 2015
I was at a Creative Edinburgh Talking Heads event last night...
4 October 2015
I stumbled across a brilliant article while I was looking for something else.
23 January 2015
Grids can be catwalks for data to parade upon
16 January 2015
Random factoid-type numbers that describe how bad things are
9 January 2015
We need to be fluent in two languages
2 January 2015
Can we turn "the science bit" into something more than just a "bit"?
19 June 2014
Snapshots, movement and blur
13 June 2014
How a data analyst started thinking about individual patients
19 March 2014
Yesterday we needed a typically bad day, not an unusually bad day
14 February 2014
Numbers with narratives; numbers without narratives
7 February 2014
An important lesson for data analysts from Andrew Stanton
28 January 2014
An email conversation between Neil Pettinger and Simon Dodds
24 January 2014
Five things we've learned from the first four months of Flow_ology
14 January 2014
We need to be both Type-A analysts and Type-B analysts
3 January 2014
Do we need a bit less here-and-now and a bit more there-and-then>?
31 December 2013
Do we have to choose between patient stories and data? Or can we have both?
16 July 2013
Is there a magic number when it comes to bed occupancy?
5 July 2013
What does the word "dashboard" tell us about the way we see data?
21 June 2013
Why we need to start looking at unscheduled care data in a new way
5 June 2013
Can happy families help us make sense of Emergency Department delays?
28 May 2013
The most widely used measure of hospital bed utilisation can be problematic
2 May 2013
...so that you can avoid using them and therefore preserve your impartiality
26 April 2013
...and there are signs that it's already causing problems
15 March 2013
Introducing a new analytical imagination activation tool
9 January 2013
Here's a different way of addressing last week's question of whether hospitals are more inclined to discharge patients when it's busy
3 January 2013
Do we discharge patients when they are ready to be discharged? Or do we—instead—discharge them when we need their beds?
30 November 2012
To mitigate or not to mitigate: that is the question.
280 November 2012
Do re-admission rates say more about patients than they do about hospitals?