The Evidence Ladder for Reinforcement Learning in Healthcare: From Retrospective Policies to Trusted Interventions

TL;DR

This paper proposes an evidence ladder for reinforcement learning in healthcare, connecting clinical treatment, patient engagement, and health-system operations. It highlights the gap between a policy's performance in historical datasets and its potential to improve care. The authors emphasize evaluating healthcare RL as an intervention in a changing sociotechnical system.

Why It Matters

The proposed evidence ladder can help bridge the gap between reinforcement learning research and real-world healthcare applications. It provides a framework for evaluating the effectiveness of RL interventions in complex healthcare systems. This can lead to more reliable and trustworthy RL-based decision-making in healthcare.

Key Findings

• evidence ladder for healthcare RL
• gap between historical performance and real-world improvement
• importance of evaluating RL as an intervention in a sociotechnical system