Predicting all-cause risk of 30-day hospital readmission using artificial neural networks

TLDR

Avoidable hospital readmissions drive high costs and affect care quality, and although electronic health records enable proactive risk identification and many machine‑learning models have been tried, an accurate real‑time predictive model for hospital settings remains needed. Using over 300,000 California hospital stays from Sutter Health’s EHR, we built and tested a TensorFlow‑based artificial neural network model. The neural‑network model achieved a 20% higher precision (0.24 vs 0.20) than the LACE score, demonstrating superior predictive performance, and we also highlighted the added value of social‑determinants data and a cost analysis to guide post‑discharge interventions.

Abstract

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.