Using the Electronic Health Record To Identify Children Likely To Suffer Last-Minute Surgery Cancellation (Ohio)

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Operative procedures frequently provide dramatic health benefits or important diagnostic information. While late cancellation of surgery is infrequent, nationally, the absolute number of cancelled cases is high, making this a leading source of perioperative wastage. These cancelled cases represent unutilized health care resources, valued as high as a dollar per second. In addition, the negative impact on patients and families of last-minute cancellation is substantial, leading to a poor patient and family experience, with many expressing disappointment, frustration, and even anger.

Machine learning can uncover patterns in historical data to identify predictors and capture relationships among many factors to allow assessment of risk associated with a particular set of conditions. This project will apply machine learning against a large data set to develop a model to both understand and predict surgical cancellations on individual pediatric patients at two pediatric surgical sites. The existing data set includes context-specific data from the facilities’ electronic health records and also incorporates data on weather and circulating pathogens. Following refinement of the model, it will be integrated into the clinical workflow to inform cancellation prevention and mitigation strategies.

The specific aims of this project are as follows:

  • Develop computerized models for predicting surgery cancellation 
  • Identify key predictors of last-minute cancellation of surgery from the EHR and online data resources 
  • Establish a scalable, last-minute surgery cancellation prediction system 

The project will have both broad translational importance in perioperative management and also elucidate the etiology of cancellation. By facilitating quality improvement projects and operating room management strategies to increase utilization of expensive perioperative resources, it is anticipated that a model that can accurately predict the probability of last-minute cancellation of surgical procedures is likely to provide new opportunities for reducing health care costs and improving efficiency. Successful completion of this project is expected to lead to more timely surgeries at lower cost for hundreds of thousands of patients.

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