  Background: Major postoperative complications are associated with increased
short and long-term mortality, increased healthcare cost, and adverse long-term
consequences. The large amount of data contained in the electronic health
record (EHR) creates barriers for physicians to recognize patients most at
risk. We hypothesize, if presented in an optimal format, information from
data-driven predictive risk algorithms for postoperative complications can
improve physician risk assessment. Methods: Prospective, non-randomized,
interventional pilot study of twenty perioperative physicians at a quarterly
academic medical center. Using 150 clinical cases we compared physicians' risk
assessment before and after interaction with MySurgeryRisk, a validated
machine-learning algorithm predicting preoperative risk for six major
postoperative complications using EHR data. Results: The area under the curve
(AUC) of MySurgeryRisk algorithm ranged between 0.73 and 0.85 and was
significantly higher than physicians' risk assessments (AUC between 0.47 and
0.69) for all postoperative complications except cardiovascular complications.
The AUC for repeated physician's risk assessment improved by 2% to 5% for all
complications with the exception of thirty-day mortality. Physicians' risk
assessment for acute kidney injury and intensive care unit admission longer
than 48 hours significantly improved after knowledge exchange, resulting in net
reclassification improvement of 12.4% and 16%, respectively. Conclusions: The
validated MySurgeryRisk algorithm predicted postoperative complications with
equal or higher accuracy than pilot cohort of physicians using available
clinical preoperative data. The interaction with algorithm significantly
improved physicians' risk assessment.
