The PRONE score: an algorithm for predicting doctors' risks of formal patient complaints using routinely collected administrative data.
Spittal MJ, Bismark MM, Studdert DM. BMJ Qual Saf. 2015;24:360-368.
Past studies have found a correlation between patient complaints and patient safety problems. Researchers sought to identify physicians at highest risk for a second patient complaint using routinely collected administrative data. They developed a risk prediction model which predicted future complaints with reasonable accuracy. Factors such as procedural specialty, male gender, and time since prior complaint were associated with a subsequent patient complaint. Application of this model has the potential to allow real-time identification of physicians at risk for further patient complaints and possible litigation. Actions to reduce future litigation risk—such as directed education, referral to a regulatory agency, or notification of the risk of future complaints—could be appropriately targeted based on this prediction model. A related editorial urges prompt and rigorous investigation of patient complaints.
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Using a quantitative risk register to promote learning from a patient safety reporting system.
Mansfield JG, Caplan RA, Campos JS, Dreis DF, Furman C. Jt Comm J Qual Patient Saf. 2015;41:76-86.
Identifying risk factors for medical injury.
Guse CE, Yang H, Layde PM. Int J Qual Health Care. 2006;18:203-210.
Potassium and phosphorus repletion in hospitalized patients: implications for clinical practice and the potential use of healthcare information technology to improve prescribing and patient safety.
Hemstreet BA, Stolpman N, Badesch DB, May SK, McCollum M. Curr Med Res Opin. 2006;22:2449-2455.
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Pediatric antidepressant medication errors in a national error reporting database.
Rinke ML, Bundy DG, Shore AD, Colantuoni E, Morlock LL, Miller MR. J Dev Behav Pediatr. 2010;31:129-136.
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