How to use data to scare people …

… and how to use data for analytics.

Crisis hit GP surgeries forced to turn away millions of patients

That was the headline on the Royal College of General Practitioners (“RCGP” – UK family physicians) website today. The catastrophic tone was elaborated in The (London) Times: Millions shut out of doctors’ surgeries (paywall).
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The GPs’ alarm was based on data from the GP Patient Survey which is a survey conducted on behalf or the National Health Service (“NHS”) by pollsters Ipsos MORI. The study is conducted by way of a survey questionnaire sent out to selected NHS patients. You can find the survey form here. Ipsos MORI’s careful analysis is here.

Participants were asked to recall their experience of making an appointment last time they wanted to. From this, the GPs have extracted the material for their blog’s lead paragraph.

GP surgeries are so overstretched due to the lack of investment in general practice that in 2015 on more than 51.3m occasions patients in England will be unable to get an appointment to see a GP or nurse when they contact their local practice, according to new research.

Now, this is not analysis. For the avoidance of doubt, the Ipsos MORI report cited above does not suffer from such tendentious framing. The RCGP blog features the following tropes of Langian statistical method.

  • Using emotive language such as “crisis”, “forced” and “turn away”.
  • Stating the cause of the avowed problem, “lack of investment”, without presenting any supporting argument.
  • Quoting an absolute number of affected patients rather than a percentage which would properly capture individual risk.
  • Casually extrapolating to a future round number, over 50 million.
  • Seeking to bolster their position by citing “new research”.
  • Failing to recognise the inevitable biases that beset human descriptions of past events.

Humans are notoriously susceptible to bias in how they recall and report past events. Psychologist Daniel Kahneman has spent a lifetime mapping out the various cognitive biases that afflict our thinking. The Ipsos MORI survey appears to me rigorously designed but no degree of rigour can eliminate the frailties of human memory, especially about an uneventful visit to the GP. An individual is much more likely to recall a frustrating attempt to make an appointment than a straightforward encounter.

Sometimes, such survey data will be the best we can do and will be the least bad guide to action though in itself flawed. As Charles Babbage observed:

Errors using inadequate data are much less than those using no data at all.

Yet the GPs’ use of this external survey data to support their funding campaign looks particularly out of place in this situation. This is a case where there is a better source of evidence. The point is that the problem under investigation lies entirely within the GPs’ own domain. The GPs themselves are in a vastly superior position to collect data on frustrated appointments, within their own practices. Data can be generated at the moment an appointment is sought. Memory biases and patient non-responses can be eliminated. The reasons for any diary difficulties can be recorded as they are encountered. And investigated before the trail has gone cold. Data can be explored within the practice, improvements proposed, gains measured, solutions shared on social media. The RCGP could play the leadership role of aggregating the data and fostering sharing of ideas.

It is only with local data generation that the capability of an appointments system can be assessed. Constraints can be identified, managed and stabilised. It is only when the system is shown to be incapable that a case can be made for investment. And the local data collected is exactly the data needed to make that case. Not only does such data provide a compelling visual narrative of the appointment system’s inability to heal itself but, when supported by rigorous analysis, it liquidates the level of investment and creates its own business case. Rigorous criticism of data inhibits groundless extrapolation. At the very least, local data would have provided some borrowing strength to validate the patient survey.

Looking to external data to support a case when there is better data to be had internally, both to improve now what is in place and to support the business case for new investment, is neither pretty nor effective. And it is not analysis.

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