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Clinical question: Is there a standardized way to identify doctors at high risk of incurring repeated medicolegal events?
Background: Medicolegal agencies react to episodes of substandard care rather than intervening to prevent them due to lack of robust prediction tools at the individual practitioner level. Various studies have tried to predict complaints at the individual practitioner level accurately but had limited success.
Study design: Retrospective cohort study.
Setting: Commissions in all Australian states, except South Australia, with 70,200 practicing doctors.
Synopsis: Researchers used administrative data to analyze a national sample of 13,849 formal complaints, which were lodged by patients in Australia over a 12-year period against 8,424 doctors. Using multivariate logistic regression analysis, predictors for subsequent complaints within two years of an index complaint were estimated. These predictors were used in a simple predictive algorithm, the PRONE (Predicted Risk Of New Event), a score designed for application at the doctor level. PRONE is a 22-point scoring system that estimates a doctor’s future complaint risk based on specialty, sex, the number of previous complaints, and the time since the last complaint.
Because the scoring system has strong validity and reliability, regulators could harness such information to target quality improvement interventions and prevent substandard care and patient dissatisfaction.
Bottom line: The PRONE score appears to be a valid method for assessing individual doctors’ risks of attracting recurrent complaints.
Citation: Spittal MJ, Bismark MM, Studdert DM. The PRONE score: an algorithm for predicting doctors risks of formal patient complaints using routinely collected administrative data. BMJ Qual Saf. 2015;24(6):360-368.
Clinical question: Is there a standardized way to identify doctors at high risk of incurring repeated medicolegal events?
Background: Medicolegal agencies react to episodes of substandard care rather than intervening to prevent them due to lack of robust prediction tools at the individual practitioner level. Various studies have tried to predict complaints at the individual practitioner level accurately but had limited success.
Study design: Retrospective cohort study.
Setting: Commissions in all Australian states, except South Australia, with 70,200 practicing doctors.
Synopsis: Researchers used administrative data to analyze a national sample of 13,849 formal complaints, which were lodged by patients in Australia over a 12-year period against 8,424 doctors. Using multivariate logistic regression analysis, predictors for subsequent complaints within two years of an index complaint were estimated. These predictors were used in a simple predictive algorithm, the PRONE (Predicted Risk Of New Event), a score designed for application at the doctor level. PRONE is a 22-point scoring system that estimates a doctor’s future complaint risk based on specialty, sex, the number of previous complaints, and the time since the last complaint.
Because the scoring system has strong validity and reliability, regulators could harness such information to target quality improvement interventions and prevent substandard care and patient dissatisfaction.
Bottom line: The PRONE score appears to be a valid method for assessing individual doctors’ risks of attracting recurrent complaints.
Citation: Spittal MJ, Bismark MM, Studdert DM. The PRONE score: an algorithm for predicting doctors risks of formal patient complaints using routinely collected administrative data. BMJ Qual Saf. 2015;24(6):360-368.
Clinical question: Is there a standardized way to identify doctors at high risk of incurring repeated medicolegal events?
Background: Medicolegal agencies react to episodes of substandard care rather than intervening to prevent them due to lack of robust prediction tools at the individual practitioner level. Various studies have tried to predict complaints at the individual practitioner level accurately but had limited success.
Study design: Retrospective cohort study.
Setting: Commissions in all Australian states, except South Australia, with 70,200 practicing doctors.
Synopsis: Researchers used administrative data to analyze a national sample of 13,849 formal complaints, which were lodged by patients in Australia over a 12-year period against 8,424 doctors. Using multivariate logistic regression analysis, predictors for subsequent complaints within two years of an index complaint were estimated. These predictors were used in a simple predictive algorithm, the PRONE (Predicted Risk Of New Event), a score designed for application at the doctor level. PRONE is a 22-point scoring system that estimates a doctor’s future complaint risk based on specialty, sex, the number of previous complaints, and the time since the last complaint.
Because the scoring system has strong validity and reliability, regulators could harness such information to target quality improvement interventions and prevent substandard care and patient dissatisfaction.
Bottom line: The PRONE score appears to be a valid method for assessing individual doctors’ risks of attracting recurrent complaints.
Citation: Spittal MJ, Bismark MM, Studdert DM. The PRONE score: an algorithm for predicting doctors risks of formal patient complaints using routinely collected administrative data. BMJ Qual Saf. 2015;24(6):360-368.