Folder Evidence base

There is a large and increasing body of evidence and thought on the use of measurement in improvement and in the health sector more generally.  This collection contains just some papers that hopefully will get you thinking or lead you to more in depth reading.

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Documents

pdf Stages of Data Acceptance

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Stages of data acceptance.pdf

Stages of Data Acceptance

Adapted from the IHI, the 'stages of data acceptance' provides a view on how teams may (or may not!) engage with data and give some understanding of the stages people pass through.  

pdf Funnel plots for comparing institutional performance (Speigelhalter D, 2005)

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Speigelhalter D 2005 Funnel plots and insitutional performance.pdf

Funnel plots for comparing institutional performance (Speigelhalter D, 2005)

David Speigelhalter, Winton Professor of the Public Understanding of Risk in the Statistical Laboratory at the University of Cambridge writes on how funnel plots can be flexible, attractively simple, and avoid spurious ranking of institutions into ‘league tables’.  Contains technical approaches to dealing with overdispersion.

pdf Lessons from variation (Mohammed MA et al. 2001)

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Mohammed MA et al 2001 Lessons in variation.pdf

Lessons from variation (Mohammed MA et al. 2001)

A review of how understanding variation can help to identify and potentially prevent serious issues, using six case studies including the Shipman case and childrens cardiac surgery at Bristol.

pdf The meaning of variation to healthcare managers, researchers & patients (Neuhauser et al 2011)

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Neuhauser et al 2011 The meaning of variation to healthcare managers, researchers & patients.pdf

The meaning of variation to healthcare managers, researchers & patients (Neuhauser et al 2011)

Healthcare managers, clinical researchers and individual patients (and their physicians) manage variation differently to achieve different ends.  This paper outlines different approaches to understanding and using variation in different contexts and considers what a 'gold standard' approach might look like.

default Value of small sample sizes in rapid-cycle quality improvement projects (Etchells & Woodcock, 2017)

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A frequent problem in quality improvement is that people waste time collecting too much data.  In this paper, our goal is to offer some general guidelines to measuring fidelity of implementation on small samples in the face of constrained measurement resources.

pdf The use of control charts by laypeople and hospital decision-makers for guiding decision making (Schmidtke et al. 2016)

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Schmidtke et al 2016 Control Charts and Decision Making.pdf

The use of control charts by laypeople and hospital decision-makers for guiding decision making (Schmidtke et al. 2016)

The paper presents two experiments that compare lay people’s use of control and non-control charts to make decisions between hospitals (Funnel charts vs League tables) and to monitor changes across time (Run charts with control lines vs Run charts without control lines). As expected, participants more accurately identified the outlying data using a control chart than a non-control chart, but their ability to then apply that information to more complicated questions (e.g., where should I go for  treatment?, and should I investigate?) was limited. The discussion highlights some common concerns about using control charts in hospital settings.