Learning alerting models for clinical care from EMR data and human knowledge

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Abstract Medical errors are more broadly defined as adverse clinical events that are preventable. Studies show that medical errors remain one of the key challenges of health care and recent literature ranks medical errors as one of the leading causes of death in the US. The urgency and the scope of the problem prompt the development of solutions aimed to aid clinicians in reducing such errors. Computer-based monitoring and alerting systems that rely on information in electronic medical records (EMRs) play a key role in this effort. In the previous funding cycles, our group has been developing an outlier-based model-driven alerting methodology with significant potential to reduce medical errors. The method uses retrospective data to build machine learning models that predict physician actions from a broad representation of patient states. An alert is raised if a management action (or its omission) for the current patient deviates significantly from predicted management actions for similar patients. As an example of an actual alert generated by the system, consider a patient who has recently undergone a liver transplant and receives tacrolimus as immunosuppressive agent. The patient suffers a complication and undergoes corrective surgery; however, inadvertently, tacrolimus is not reordered following the surgery. Since not receiving the expected medication represents a deviation from predicted management practice in similar patients, it is a clinical outlier. Raising an alert to reorder the medication is therefore appropriate. Our current alerting system is silently deployed on the production electronic medical record system at UPMC and supports alerting in real-time. The current proposal takes the research program in a bold new direction. Alerting models will be enhanced using a variety of tools, including automatic evaluation of performance and the inclusion of an adaptive ICU-specific knowledge-base in addition to multi-domain, multi- resolution features derived from the EMR. Human experts will play a major role in determining appropriateness and usefulness of alerts when generated in real-time, contribute to the dynamic growth of the knowledge base, and evaluate the quality of the explanations provided for the alerts. Finally, the alerting system will be deployed across 12 ICUs in a step-wedge clinical trial to determine whether EHR-based alerting, when revealed to clinicians, modifies the rate and timing of their actions. Secondary end-points will include alert performance metrics, process- related outcomes, and patient-centered outcomes.