Prospective Evaluation of a 90-day Mortality Prediction Model: From Silent Pilots to Real Time Deployment in the EHR
Rossi, L. A.; Roberts, L. M.; Zachariah, F. J.
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Prognostication in oncology is increasingly difficult due to the rapid evolution of therapies with significant improvement of survival. Accurate prognostication is essential to provide optimal, value-driven end of life care for cancer patients, and can promote goals of care (GOC) conversations with the potential to minimize chemotherapy or ICU utilization in the last weeks of life, and possibly increase hospice admission and length of stay.1 There are several recent publications on the application of machine learning for prognostication.2,3 We developed a 90-day mortality prediction model trained with data in the Electronic Health Records (EHR). After a non-interventional pilot stage, we deployed the model in February 2021 in the real-time Electronic Health Record Epic infrastructure of our cancer center. Here we present the model and evaluate its overall performance for the first 7.5 months since the go-live and outline our evaluation process for the next stages.
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