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Surgical audit: statistical lessons from Nightingale and Codman


  • D. J. Spiegelhalter


There is a long history of interest in examining and comparing surgical outcomes. The ‘epidemiological’ approach was initiated by Florence Nightingale in her suggestion for uniform surgical statistics, and she clearly predicted the problems that are associated with collecting, analysing and interpreting such data. Unfortunately those responsible for implementing and reporting her scheme appeared not to have shared her insight. The contrasting ‘clinical’ approach was championed by Ernest Codman in his search for full and honest appraisals of surgical errors. Once again, despite initial enthusiasm, others had great difficulty in following his example, although we discuss a recent instance of a reflective analysis of an individual surgeon's performance. We conclude by suggesting that a synthesis between these approaches is appropriate, but we follow others in warning of the inevitable extra‐statistical difficulties that will arise.

Suggested Citation

  • D. J. Spiegelhalter, 1999. "Surgical audit: statistical lessons from Nightingale and Codman," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 45-58.
  • Handle: RePEc:bla:jorssa:v:162:y:1999:i:1:p:45-58
    DOI: 10.1111/1467-985X.00120

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    Cited by:

    1. Consonni, Guido & Veronese, Piero & Gutiérrez-Peña, Eduardo, 2004. "Reference priors for exponential families with simple quadratic variance function," Journal of Multivariate Analysis, Elsevier, vol. 88(2), pages 335-364, February.
    2. Peter C. Smith & Andrew D. Street, 2013. "On The Uses Of Routine Patient‐Reported Health Outcome Data," Health Economics, John Wiley & Sons, Ltd., vol. 22(2), pages 119-131, February.
    3. Scott, Steven L., 2004. "A Bayesian paradigm for designing intrusion detection systems," Computational Statistics & Data Analysis, Elsevier, vol. 45(1), pages 69-83, February.
    4. Sebastiani, Paola & Ramoni, Marco, 2005. "Normative selection of Bayesian networks," Journal of Multivariate Analysis, Elsevier, vol. 93(2), pages 340-357, April.
    5. Roverato, Alberto & Paterlini, Sandra, 2004. "Technological modelling for graphical models: an approach based on genetic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 323-337, September.
    6. Geng, Zhi & He, Yang-Bo & Wang, Xue-Li & Zhao, Qiang, 2003. "Bayesian method for learning graphical models with incompletely categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 175-192, October.
    7. Jan Beyersmann & Christine Schrade, 2017. "Florence Nightingale, William Farr and competing risks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 285-293, January.
    8. Gwyn Bevan & Richard Hamblin, 2009. "Hitting and missing targets by ambulance services for emergency calls: effects of different systems of performance measurement within the UK," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 161-190, January.
    9. David I. Ohlssen & Linda D. Sharples & David J. Spiegelhalter, 2007. "A hierarchical modelling framework for identifying unusual performance in health care providers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 865-890, October.
    10. Nicholas Longford & D. B. Rubin, 2006. "Performance assessment and league tables. Comparing like with like," Economics Working Papers 994, Department of Economics and Business, Universitat Pompeu Fabra.

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