Don't lose sight of what's important

This blog entry is short, by necessity of the fact that I'm typing (very slowly) with one hand, having broken my arm, as a result of a rather silly cycle manoeuvre. However spending time at the hospital neatly provided me with an observation for today.  Healthcare is widely seen as a key area of potential enhancement through the use of data and analytics; and many technology vendors like to showcase examples from healthcare; to demonstrate how data can enhance health predictions, use IoT monitors to detect early-stage issues, use detailed analytics to enhance operational efficiency and effective use of limited resource. There are many really good use cases for healthcare data. 

So I was intrigued to spot a 'dashboard' report in my hospital's A&E department [emergency room], though the contents proved to be somewhat disappointing. The report had three core elements:

• summary of responses to a questionnaire that asked "how likely would you be to recommend our department to friends & family"; with a bar graph to depict the monthly volume of responses, together with another bar chart and a pie chart to show the split of responses for the last month, and also a table of data. So three depictions of the same data, to highlight that 80% of people would be highly likely to recommend the department. Incidentally 'highly recommend' is the first option in the SMS questionnaire. I'm sure they test the questionnaire by reversing the sequence of responses to ensure there's no bias in how the question is asked.....
• a list of comments received with the questionnaire responses - there were a handful and included "." and "comment" - [yes seriously]
• a highlight that the department had received 5 letters praising the service; listing quotes from each letter
• a highlight that 6 complaints had been received; with a single word bullet for each

I'm not going to name the hospital, as this is just illustrative of some generic weaknesses in using data, and amongst all the discussion of big data, advanced analytics, machine learning there is often a fundamental failure to focus on core objectives when summarising and communicating data: 

What's the key objective?

Why are we producing the dashboard? Hospital A&E's get a lot of attention due primarily to the cost of the service, and the fact that by nature patients need urgent, timely attention. But equally there's been concern that the service is abused, by non-urgent cases; and this impacts attention of care for serious cases. Understanding the motives for any analytics or summary is key. Without a clear objective, there will be incorrect focus. For example this unit is now called the 'Emergency department'; the 'accident' element has been dropped; from this it seems clear the hospital is pursuing an approach of ensuring the resources are used for only urgent cases. This is backed up by some other clear graphics that depict what the department should be used for.

Who's the audience?

Understanding the objective leads on to understanding the audience. This was a public dashboard - I expect they have a more detailed internal version. but the public version should focus on the public objectives. Providing the public with a summary of feedback on how recommendable the service is seems questionable in purpose and smacks of self-congratulation without any clear objective. 

What do they need to know?

I can immediately identify some key things I'm interested in as a patient: what's the typical wait time (for the kind of issue I have)? how does wait time vary by day or week or time of day? (my broken arm needs attention, but I could come earlier or later in day if it speeds my progress). And how do these wait times compare to other hospitals. it'd be useful, and insightful. Furthermore highlighting the proportion (hopefully declining) of inappropriate cases - that would better be dealt with elsewhere, with examples and care paths for the most common of these.

What will they do with the insight?

Insight is only useful with action;  and since my injury isn't critical then I could be flexible with what time of day I attend; so knowing what the peaks and troughs are might help me think about future attendance. similarly being aware of alternate options for other non-critical cases would keep me out of A&E in future. If the aim is to reduce non-urgent cases, then more help is required to flag examples, and explain alternate routes for medical treatment.

As I mention I don't want to single out this hospital, as this is just one report - it may itself be an outlier from an excellent analytics team. Instead this highlights how any piece of output needs to consider the fundamentals; and if it doesn't address these then it shouldn't be produced. Use that scarce analytical resource on something that will make more difference to strategic objectives.

In the meantime I have a few weeks to increase my one-handed-typing speeed and accuracy.