Commercial quality “awards” are not a strong indicator of quality surgical care

Abstract Background This study aimed to determine whether publicized hospital rankings can be used to predict surgical outcomes. Methods Patients undergoing one of nine surgical procedures were identified, using the Healthcare Cost and Utilization Project State Inpatient Database for Florida and New York 2011–2013 and merged with hospital data from the American Hospital Association Annual Survey. Nine quality designations were analyzed as possible predictors of inpatient mortality and postoperative complications, using logistic regression, decision trees, and support vector machines. Results We identified 229,657 patients within 177 hospitals. Decision trees were the highest performing machine learning algorithm for predicting inpatient mortality and postoperative complications (accuracy 0.83, P Conclusion Hospital quality rankings are not a reliable indicator of quality for all surgical procedures. Hospital and provider quality must be evaluated with an emphasis on creating consistent, reliable, and accurate measures of quality that translate to improved patient outcomes.