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Making sense of the shadows: priorities for creating a learning healthcare system based on routinely collected data
  1. Sarah R Deeny,
  2. Adam Steventon
  1. Data Analytics, The Health Foundation, London, UK
  1. Correspondence to Adam Steventon, Data Analytics, The Health Foundation, 90 Long Acre, London WC2E 9RA, UK; adam.steventon{at}


Socrates described a group of people chained up inside a cave, who mistook shadows of objects on a wall for reality. This allegory comes to mind when considering ‘routinely collected data’—the massive data sets, generated as part of the routine operation of the modern healthcare service. There is keen interest in routine data and the seemingly comprehensive view of healthcare they offer, and we outline a number of examples in which they were used successfully, including the Birmingham OwnHealth study, in which routine data were used with matched control groups to assess the effect of telephone health coaching on hospital utilisation.

Routine data differ from data collected primarily for the purposes of research, and this means that analysts cannot assume that they provide the full or accurate clinical picture, let alone a full description of the health of the population. We show that major methodological challenges in using routine data arise from the difficulty of understanding the gap between patient and their ‘data shadow’. Strategies to overcome this challenge include more extensive data linkage, developing analytical methods and collecting more data on a routine basis, including from the patient while away from the clinic. In addition, creating a learning health system will require greater alignment between the analysis and the decisions that will be taken; between analysts and people interested in quality improvement; and between the analysis undertaken and public attitudes regarding appropriate use of data.

  • Quality improvement
  • Healthcare quality improvement
  • Statistical process control

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