Objective:

To demonstrate how temporal misalignment in data preprocessing affects reinforcement learning (RL) recommendations in sepsis management, highlighting its significance for patient outcomes.

Key Findings:

• Temporal misalignment leads to inappropriate treatment recommendations in nearly half of patient states, potentially compromising patient safety.
• Over 80% of the literature on RL in healthcare is affected by this methodological flaw, indicating a widespread issue.
• Inflated performance metrics obscure the impact of temporal misalignment, misleading researchers and practitioners.

Interpretation:

The misalignment between state observations and action decisions undermines the causal structure necessary for effective RL applications in clinical settings, potentially leading to adverse patient outcomes.

Limitations:

• The proposed solution requires further validation in diverse clinical scenarios, such as different disease states and patient populations.
• The study primarily focuses on sepsis management, which may limit generalizability to other conditions, necessitating further research.

Conclusion:

Addressing temporal misalignment is crucial for improving RL-based decision-making tools in healthcare, ensuring they provide accurate and beneficial treatment recommendations that enhance patient care.