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Enhancing surgical efficiency: predicting same-day cancellations in urologic procedures.

Pablo A Suarez1, Sudarshan Srirangapatanam2, Lynn Leng2

  • 1University of California, San Francisco, USA. pablo.suarez@ucsf.edu.

World Journal of Urology
|December 17, 2025
PubMed
Summary

This study developed a predictive model using electronic health records and social determinants of health to identify patients at high risk for day-of-surgery cancellations in urologic procedures. The model achieved 80% AUC, aiding in timely preoperative interventions.

Keywords:
Health inequitiesHealth services accessibilityNo-show patientsSocial determinants of healthUrologic surgical procedures

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Area of Science:

  • Health Informatics
  • Surgical Outcomes Research
  • Predictive Analytics in Healthcare

Background:

  • Day-of-surgery (DOS) cancellations disrupt healthcare efficiency and patient care.
  • Early identification of patients at risk for DOS cancellations is crucial for effective perioperative management.
  • Urologic surgeries are susceptible to DOS cancellations, impacting resource allocation and patient pathways.

Purpose of the Study:

  • To develop and validate a predictive model for DOS cancellations in urologic surgery.
  • To integrate electronic health record (EHR) data, including social determinants of health (SDOH), into a predictive model.
  • To identify high-risk patients for timely preoperative interventions and reduce cancellation rates.

Main Methods:

  • Retrospective cohort study of adult patients undergoing elective urologic procedures.
  • Utilized EHR data encompassing demographics, SDOH, clinical history, and healthcare utilization.
  • Employed lasso-optimized logistic regression for model development and internal validation via cross-validation.

Main Results:

  • The predictive model identified 13 key variables for DOS cancellation prediction.
  • The model demonstrated an Area Under the Curve (AUC) of 0.80 with 83% accuracy.
  • Achieved 81% sensitivity and 55% specificity at a 0.4 threshold, indicating strong predictive capability.

Conclusions:

  • This study presents the first risk score model integrating comprehensive SDOH for predicting DOS cancellations.
  • The developed model serves as a novel tool for identifying high-risk patients and guiding preoperative interventions.
  • External validation of the model at diverse clinical sites is recommended to assess generalizability.