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Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability

Meghan Brennan1, Sahil Puri2, Tezcan Ozrazgat-Baslanti3

  • 1Precision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Anesthesiology, University of Florida College of Medicine, Gainesville.

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A machine-learning algorithm, MySurgeryRisk, significantly improved physicians' preoperative risk assessment accuracy for major postoperative complications. This intelligent decision-support tool enhances physician decision-making and is feasible for clinical implementation.

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Area of Science:

  • Medical Informatics
  • Surgical Risk Assessment
  • Machine Learning in Healthcare

Background:

  • Major postoperative complications increase costs and mortality.
  • Electronic health record complexity hinders timely preoperative risk assessment.
  • Data-driven algorithms can augment physician decision-making.

Purpose of the Study:

  • To evaluate the usability and accuracy of the MySurgeryRisk machine-learning algorithm for preoperative risk assessment.
  • To compare physician risk assessment accuracy with and without the decision-support platform.
  • To assess the impact of an intelligent decision-support platform on identifying patients at high risk for postoperative complications.

Main Methods:

  • Prospective, nonrandomized pilot study involving 20 physicians.
  • Comparison of physician risk assessment accuracy against the MySurgeryRisk algorithm for 150 clinical cases.
  • Area under the receiver operating characteristic curve (AUROC) used to measure predictive accuracy for six postoperative complications.

Main Results:

  • MySurgeryRisk algorithm demonstrated superior accuracy (AUROC 0.73–0.85) compared to initial physician assessments (AUROC 0.47–0.69) for most complications.
  • Physician accuracy significantly improved for acute kidney injury (12% net improvement) and prolonged ICU stay (16% net improvement) after algorithm interaction.
  • Physicians found the algorithm easy to use and valuable for decision support.

Conclusions:

  • A validated machine-learning algorithm (MySurgeryRisk) for real-time predictive analytics is feasible and accepted by physicians.
  • The intelligent decision-support platform effectively augments physician preoperative risk assessment.
  • Physician involvement in design and implementation is crucial for successful technology adoption.