Background: Access to palliative care is a key quality metric which most healthcare organizations strive to improve.\nThe primary challenges to increasing palliative care access are a combination of physicians over-estimating patient\nprognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a\nmismatch between patient wishes, and their actual care towards the end of life.\nMethods: In this work, we address this problem, with Institutional Review Board approval, using machine learning\nand Electronic Health Record (EHR) data of patients. We train a Deep Neural Network model on the EHR data of\npatients from previous years, to predict mortality of patients within the next 3-12 month period. This prediction is\nused as a proxy decision for identifying patients who could benefit from palliative care.\nResults: The EHR data of all admitted patients are evaluated every night by this algorithm, and the palliative care team\nis automatically notified of the list of patients with a positive prediction. In addition, we present a novel technique\nfor decision interpretation, using which we provide explanations for the modelâ??s predictions.\nConclusion: The automatic screening and notification saves the palliative care team the burden of time consuming\nchart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients rather then\nrelying on referrals from the treating physicians.
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