Measuring clinical performance using routinely collected clinical data

Following the well-publicized problems with paediatric cardiac surgery at the Bristol Royal Infirmary, there is wide public interest in measures of hospital performance. The Kennedy report on the BRI events suggested that such measures should be meaningful to the public, case-mix-adjusted, and based on data collected as part of routine clinical care. We have found that it is possible to predict in-hospital mortality (a measure readily understood by the public) using simple routine data—age, mode of admission, sex, and routine blood test results. The clinical data items can be obtained at a single venesection, are commonly collected in the routine care of patients, are already stored on hospital core IT systems, and so place no extra burden on the clinical staff providing care. Such risk models could provide a metric for use in evidence-based clinical performance management. National application is logistically feasible.

[1]  D. Prytherch,et al.  Risk‐adjusted prediction of operative mortality in oesophagogastric surgery with O‐POSSUM , 2004, The British journal of surgery.

[2]  P. C. Weaver,et al.  Comparison of P‐POSSUM risk‐adjusted mortality rates after surgery between patients in the USA and the UK , 2003, The British journal of surgery.

[3]  P. C. Weaver,et al.  Towards a national clinical minimum data set for general surgery , 2003, The British journal of surgery.

[4]  Rajneesh,et al.  POSSUM and P‐POSSUM for risk‐adjusted audit of patients undergoing emergency laparotomy , 2004, The British journal of surgery.

[5]  P. C. Weaver,et al.  The use of routine laboratory data to predict in-hospital death in medical admissions. , 2005, Resuscitation.

[6]  C. Lam,et al.  Validation of POSSUM scoring systems for audit of major hepatectomy , 2004, The British journal of surgery.

[7]  P. C. Weaver,et al.  POSSUM and Portsmouth POSSUM for predicting mortality , 1998 .

[8]  M. Yii,et al.  Risk‐adjusted surgical audit with the POSSUM scoring system in a developing country , 2002, The British journal of surgery.

[9]  S. Ashley,et al.  An evaluation of the POSSUM surgical scoring system , 1996, The British journal of surgery.

[10]  C. D. Collins,et al.  Caseload or workload? Scoring complexity of operative procedures as a means of analysing workload. , 1990, BMJ.

[11]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[12]  G. Teasdale.  Learning from Bristol: report of the public inquiry into children's heart surgery at Bristol Royal Infirmary 1984-1995 , 2002, British journal of neurosurgery.

[13]  M. Mckee,et al.  'Failure to rescue' as a measure of quality of hospital care: the limitations of secondary diagnosis coding in English hospital data. , 1999, Journal of public health medicine.

[14]  W. Henderson,et al.  The comparative assessment and improvement of quality of surgical care in the Department of Veterans Affairs. , 2002, Archives of surgery.

[15]  M. Mckee,et al.  Hospital mortality league tables , 2003, BMJ : British Medical Journal.

[16]  D. Prytherch,et al.  Portsmouth POSSUM models for abdominal aortic aneurysm surgery , 2001, The British journal of surgery.

[17]  P. C. Weaver,et al.  Comparison of P-POSSUM risk-adjusted mortality rates after surgery between patients in the United States of America and the United Kingdom , 2003 .

[18]  F. Grover,et al.  Risk adjustment of the postoperative mortality rate for the comparative assessment of the quality of surgical care: results of the National Veterans Affairs Surgical Risk Study. , 1997, Journal of the American College of Surgeons.

[19]  D. Prytherch,et al.  Risk‐adjusted predictive models of mortality after index arterial operations using a minimal data set , 2005, The British journal of surgery.

[20]  Richard Lilford,et al.  Use and misuse of process and outcome data in managing performance of acute medical care: avoiding institutional stigma , 2004, The Lancet.