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From stoplight reports to time series: equipping boards and leadership teams to drive better decisions
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  1. James Mountford1,2,
  2. Doug Wakefield3
  1. 1UCLPartners, London, UK
  2. 2Royal Free London NHS Foundation Trust, London, UK
  3. 3Center for Health Care Quality (CHCQ), Department of Health Management and Informatics, University of Missouri, Columbia, Missouri, USA
  1. Correspondence to Dr James Mountford, UCLPartners, 3rd Floor, 170 Tottenham Court Road, London W1T 7HA, UK; james.mountford{at}uclpartners.com

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One of us was shown a letter received by a hospital infection control leader from the CEO congratulating her on an excellent monthly performance—for the previous month MRSA infections had decreased from 4 to 2 cases. A couple of months later the same CEO sent a letter expressing serious concern, asking for an explanation of why the monthly MRSA cases had doubled from 2 to 4. Implicit in the CEO's letter is an all too common misunderstanding when using point-to-point data comparisons that every data point is a signal of meaningful change. Absent any information about or understanding of the nature and extent of the underlying variation of the process or event type being analysed, in point-to-point comparisons the only thing one can be sure of is that the second data point will likely be either higher or lower than the preceding data point.

Common to board members, corporate-suite executives, directors and managers is the need to rapidly interpret key data and to decide what if any actions are needed. Two papers in this edition highlight the critical need to ensure that such data presentations do not lead decision-makers astray. In the first paper by Schmidtke et al,1 analysing data presented to Boards of English NHS Trusts, control charts are offered as an effective and efficient tool to distinguish results due to chance variation from results due to significant changes. The Anhøj et al2 paper from Denmark critiques the use of the seemingly ever present ‘red, amber, green’ stoplight reports, and also endorses the need for longitudinal analyses to detect trends and meaningful data shifts rather than looking at individual data points in isolation. Together these two papers are useful contributions to a literature about what forms of data decision-making groups should see in order to focus …

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