Improving palliative care with deep learning

TLDR

Access to palliative care is a key quality metric, yet physician overestimation of prognosis and staff shortages create a mismatch between patient wishes and end‑of‑life care. The study aims to improve palliative care access by applying machine learning to EHR data to identify patients who may benefit from palliative services. A deep neural network was trained on historical EHR data to predict 3‑ to 12‑month mortality, and the nightly predictions automatically notify the palliative team, with an added interpretability method to explain predictions. The automated screening reduces chart‑review burden and enables the palliative team to proactively reach out to patients rather than waiting for physician referrals.

Abstract

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