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Mortality alerts, actions taken and declining mortality: true effect or regression to the mean?
  1. Perla J Marang-van de Mheen1,
  2. Gary A Abel2,
  3. Kaveh G Shojania3
  1. 1 Department of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Centre, Leiden, Netherlands
  2. 2 University of Exeter Medical School, Exeter, UK
  3. 3 Department of Medicine, Sunnybrook Health Sciences Centre and the University of Toronto, Toronto, Canada
  1. Correspondence to Dr Perla J Marang-van de Mheen, Department of Biomedical Data Sciences, Medical Decision Making, Leiden University Medical Centre, Leiden, RC 2300, The Netherlands; p.j.marang{at}

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Alerts have become a routine part of our daily lives—from the apps on our phones to an increasing number of ‘wearables’ (eg, fitness trackers) and household devices. Within healthcare, frontline clinicians have become all too familiar with a barrage of alerts and alarms from electronic medical records and medical devices.

Somewhat less familiar to most clinicians, however, are the alerts received by institutions from regulators and other regional or national bodies monitoring healthcare performance. After the Bristol inquiry in 2001 in the UK,1 research showed that given the available data Bristol could have been detected as an outlier and that it was not simply a matter of the low volume of cases.2 3 Had the cumulative excess mortality been monitored using these routinely collected data, then an alarm could have given for Bristol after the publication of the 1991 Cardiac Surgical Register and could have saved children’s lives.4 Similar assertions have been made about detecting problems at Mid Staffordshire National Health Service Foundation Trust—that excessively high hospital standardised mortality ratios (SMRs) pre-dated the eventual recognition of exceptionally substandard care subsequently confirmed by other means.5 6

Following the Bristol inquiry, the UK implemented a national mortality surveillance system. This system alerts hospital trusts when they have higher than expected in-hospital mortality for at least one of 122 diagnosis/procedure groups, using cumulative sum (CUSUM) charts. In a CUSUM chart, the difference between the actual and expected outcome is plotted cumulatively so that a series of acceptable outcomes makes the chart vary randomly around the average or baseline, but a series of poor outcomes will make the chart moving away from the average (usually upwards). CUSUM charts were recently shown to be particularly useful, in comparison with other types of control charts, for faster detection of increases in adverse …

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