Improving Palliative Care with Deep Learning

TL;DR

This paper presents a deep learning approach using electronic health records to proactively identify hospitalized patients who could benefit from palliative care, aiming to improve end-of-life care quality.

Contribution

It introduces a novel deep neural network model for predicting mortality risk to facilitate timely palliative care interventions, with an interpretability technique for explanations.

Findings

The model predicts 3-12 month mortality with high accuracy.

Proactive identification improves palliative care outreach.

Interpretability method enhances trust in model predictions.

Abstract

Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life. We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.