title: The PRONE score: an algorithm for predicting doctors' risks of formal patient complaints using routinely collected administrative data.
abstract: BACKGROUND
  Medicolegal agencies-such as malpractice insurers, medical boards and complaints bodies-are mostly passive regulators; they react to episodes of substandard care, rather than intervening to prevent them. At least part of the explanation for this reactive role lies in the widely recognised difficulty of making robust predictions about medicolegal risk at the individual clinician level. We aimed to develop a simple, reliable scoring system for predicting Australian doctors' risks of becoming the subject of repeated patient complaints.
  
  
  METHODS
  Using routinely collected administrative data, we constructed a national sample of 13,849 formal complaints against 8424 doctors. The complaints were lodged by patients with state health service commissions in Australia over a 12-year period. We used multivariate logistic regression analysis to identify predictors of subsequent complaints, defined as another complaint occurring within 2 years of an index complaint. Model estimates were then used to derive a simple predictive algorithm, designed for application at the doctor level.
  
  
  RESULTS
  The PRONE (Predicted Risk Of New Event) score is a 22-point scoring system that indicates a doctor's future complaint risk based on four variables: a doctor's specialty and sex, the number of previous complaints and the time since the last complaint. The PRONE score performed well in predicting subsequent complaints, exhibiting strong validity and reliability and reasonable goodness of fit (c-statistic=0.70).
  
  
  CONCLUSIONS
  The PRONE score appears to be a valid method for assessing individual doctors' risks of attracting recurrent complaints. Regulators could harness such information to target quality improvement interventions, and prevent substandard care and patient dissatisfaction. The approach we describe should be replicable in other agencies that handle large numbers of patient complaints or malpractice claims.
authors: Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia.
authors: Matthew J
authors: MJ
authors: Spittal
authors: Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia.
authors: Marie M
authors: MM
authors: Bismark
authors: Center for Health Policy/PCOR, Stanford University Medical School, Stanford, California, USA Stanford Law School, Stanford, California, USA.
authors: David M
authors: DM
authors: Studdert
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001315:Australia; D003625:Data Collection; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D019300:Medical Errors / Q000517:prevention & control*; D008875:Middle Aged; D015999:Multivariate Analysis; D061214:Patient Safety; D017060:Patient Satisfaction / Q000706:statistics & numerical data; D010817:Physician-Patient Relations; D010818:Practice Patterns, Physicians' / Q000592:standards* / Q000639:trends; D011237:Predictive Value of Tests; D011785:Quality Assurance, Health Care; D012372:ROC Curve; D012308:Risk Management
keywords: Governance; Healthcare quality improvement; Quality improvement; Risk management