15 November 2018
Can you make a visual representation of something you've never actually seen?
12 October 2018
I went to the NHS-R Community Conference in Birmingham on Tuesday. It was great. Here are three observations about it.
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?
29 November 2017
Analysts don't just need to master the stats; they also need to understand the processes they're describing,
22 November 2017
What are the practical actions that need to be taken to make the use of control charts the default choice for trusts to monitor their operational data as well as being used for improvement projects?
3 October 2017
If NHS data analysts were to draft an American Declaration of
Independence-style manifesto, then one of the truths we hold to be
self-evident would be that all information should be useful
. Our data
shouldn't just be something that managers and clinicians find
"interesting". And it certainly shouldn't just be something they ignore. No. Our data should be instrumental in helping to change things
that need to be changed.
But the process by which information gets turned into action (Before he
started using the hashtag #showflow, Chris Green used to add the hashtag
#dataintoaction to his tweets) is not at all straightforward. This engagement between data and decision-making is the Holy Grail for a lot of
us, and we spend a lot of time trial-and-error-ing different ways of
achieving this alchemy.
One theory I stumbled across recently is the Learning Healthcare System. This approach by Charles Friedman
makes the point that there are behavioural as well as technical
aspects to getting data into the bloodstream of the decision-making
21 August 2017
One of my more vivid Flowopoly memories is of a workshop on a wintry day at Wishaw General Hospital two-and-a-half years ago. Back in those days the method we used for choosing which bad day and which good day to replay wasn't as sophisticated as it is now (we used to just pick days when A&E four-hour compliance was either "bad" or "good"), and I remember that the thing that made the good day good wasn't the usual thing (that there were empty beds available downstream of A&E for the patients who needed to be admitted to flow into). Instead it was that none of the A&E attendances that day needed to be admitted until mid-afternoon, by which time the Assessment wards had had time to recover from their perilous 8:00am fullness situation and release beds thus enabling a relatively breach-free day. The hospital's unscheduled care system was effectively "rescued" by the late arrival times of its majors in A&E.
25 July 2017
When information analysts use data to describe healthcare they usually start with institutions or departments or staff disciplines and measure things within those categories. How many hospital admissions? How many GP consultations? How many A&E attendances? How many district nurse contacts? It’s as if we are looking at a map of healthcare where boxes represent buildings (e.g. hospitals or A&E departments) and circles represent staff disciplines (e.g. district nurses or GPs), and we then populate these shapes with aggregated numbers.
17 February 2017
Two months ago, the Health Foundation published a thought paper written by Martin Bardsley. The first
paragraph of the report sets out the problem it was addressing.
15 February 2017
Flow_ology is a method that gets data
about patient flow into the bloodstream of a general hospital. I teach this
method in a workshop that has three acts.
10 February 2017
Imagine you've been given the job of preparing a great big three-course
Sunday lunch for the full extended family. Three complicated dishes you've never cooked
before. But all you've got in front of you is an incomplete, fragmented
list of ingredients. No step-by-step instructions. And even the list of
ingredients has got vital things missing from it.
3 February 2017
There's a narrative arc to the Flow_ology course which mirrors the argument underpinning the theory I call "The Fullness Hypothesis".
24 January 2017
What's the use of data without pictures or conversations?
Anaximaps is a new one-day workshop that shows data analysts how to acquire, analyse and populate user-drawn maps of health and social care processes.
23 January 2017
This is a thought-provoking piece by William Davis, published in the Guardian
on 19 January 2017. Although its sweep is broad, much of what this article says is highly relevant to healthcare statistics. One of my favourite sentences from it is: "Official knowledge becomes ever more abstracted from lived experience, until that knowledge simply ceases to be relevant or credible."
20 January 2017
One of the questions that keeps me awake at night is this: "When you've got
a problem, and you don't know what the solution to that problem is—but you do
know that information will help in the understanding and
solving of the problem—how do you decide what information to look at?"
13 January 2017
We need to start looking at health and social care activity from an "individual patient journey"
perspective. This means being a lot less interested in knowing—separately and in
isolation—how many A&E attendances in total, how many district nurse
contacts in total, how many homecare visits in total, how many GP
consultations in total. And—instead—being a lot more
interested in how individual patients become an embodiment of collections of events.
10 January 2017
There are three open course dates in London coming up over the next two months. Demystifying Statistics
is a whistle-stop tour through the basic stats syllabus. Arguing with Numbers
shows how to describe numbers using the written word and the spoken word - ranging from the one-side-of-A4 summary to the longer, more complex, data-rich report. Flow_ology
teaches a 'method' for analysing and presenting unscheduled care data on patient flow within acute hospital organisations.
6 January 2017
I considered at great length the question of field. In classical
anthropology, there's a rigid distinction between "field" and "home".
Field's where you go to do your research, immersing yourself, sometimes at
great personal risk, in a maelstrom of raw, unsorted happening. Home's
where you go to sort and tame it: catalogue it, analyse it, transform it
into something meaningful.