[Paper Review] Challenges for Reinforcement Learning in Healthcare

Elsa Riachi, Muhammad Mamdani+2 more|arXiv (Cornell University)|Mar 9, 2021

Health Systems, Economic Evaluations, Quality of Life45 references4 citations

TL;DR

This paper identifies and analyzes key challenges in applying reinforcement learning (RL) to healthcare, focusing on reward function design, state and action representation, policy evaluation, and interpretability. It argues that observational data and clinical complexities limit RL's real-world deployment, advocating for improved validation standards and interpretability methods to ensure safe, reliable decision support in personalized medicine.

ABSTRACT

Many healthcare decisions involve navigating through a multitude of treatment options in a sequential and iterative manner to find an optimal treatment pathway with the goal of an optimal patient outcome. Such optimization problems may be amenable to reinforcement learning. A reinforcement learning agent could be trained to provide treatment recommendations for physicians, acting as a decision support tool. However, a number of difficulties arise when using RL beyond benchmark environments, such as specifying the reward function, choosing an appropriate state representation and evaluating the learned policy.

Motivation & Objective

• To identify and analyze core challenges in applying reinforcement learning (RL) to clinical decision-making in real-world healthcare settings.
• To examine how limitations in state and action space representation affect policy learning and evaluation in observational medical data.
• To highlight the risks of distributional shift and poor generalization when deploying RL policies trained on historical data.
• To address the critical need for interpretability and validation frameworks to ensure trust and safety in RL-driven clinical decision support.
• To propose future research directions focused on robust policy evaluation, state representation diagnostics, and standards for validating RL policies in healthcare.

Proposed method

• Uses a Markov Decision Process (MDP) framework to model sequential treatment decisions, with state space S, action space A, reward function r, discount factor γ, and transition probabilities P.
• Applies off-policy evaluation techniques to assess learned policies using observational data, acknowledging limitations due to distributional shift and confounding.
• Employs value-based RL algorithms such as Q-learning to approximate the optimal action-value function Q*(s,a) via Bellman equations.
• Proposes using local interpretability methods (e.g., LIME-like approximations) and feature importance metrics (e.g., mutual information) to explain policy decisions.
• Investigates the impact of state representation on policy performance, comparing continuous vs. discretized representations and their susceptibility to extrapolation errors.
• Recommends incorporating clinical and non-clinical factors (e.g., patient preferences, socioeconomic status) into state representations to reflect real-world decision influences.

Experimental results

Research questions

• RQ1 How do design choices in state and action representation affect the performance and reliability of RL policies in healthcare?
• RQ2 To what extent can off-policy evaluation accurately validate RL policies trained on observational EHR data?
• RQ3 How can interpretability methods be used to explain RL policy decisions in a clinically meaningful way?
• RQ4 What are the risks of distributional shift when deploying RL policies that deviate from the behavior policy in real-world clinical settings?
• RQ5 What standards or heuristics are needed to validate and diagnose RL policies in safety-critical healthcare applications?

Key findings

• Reward function specification is highly challenging in healthcare, as simplistic or misaligned rewards (e.g., minimizing mortality alone) may lead to suboptimal or counterintuitive policies.
• Discretizing continuous treatment actions (e.g., drug dosages) based on quantiles can lead to poor policy learning, as most data falls into the lowest bin (e.g., zero dose), making policies indistinguishable from no-action baselines.
• The choice of state representation significantly influences policy performance and interpretability, with continuous representations prone to extrapolation errors and discrete ones losing granularity.
• Off-policy evaluation may fail to detect flaws in learned policies if the state representation does not adequately capture relevant clinical dynamics or confounding factors.
• Interpretability tools such as feature importance and local surrogate models can help explain policy decisions but may not fully reveal how features are interpreted by the agent, especially when non-clinical factors influence decisions.
• There is a critical need for standardized validation frameworks to assess policy robustness, especially in rare or underexplored states, to ensure safety and reliability in clinical deployment.