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This paper frames policy retrieval in prior authorization (PA) as a sequential decision-making problem, modeled as an MDP where an agent iteratively selects policy chunks or stops to make a decision. They train policies using offline RL algorithms (CQL, IQL, DPO) on logged trajectories of baseline retrieval strategies using synthetic PA requests derived from CMS coverage data. Results show that DPO achieves 92% decision accuracy with 47% fewer retrieval steps compared to exhaustive retrieval with CQL, demonstrating a Pareto-optimal trade-off between accuracy and efficiency.
Forget fixed retrieval: Offline RL agents can learn to selectively retrieve policy information, slashing retrieval costs by nearly 50% while maintaining state-of-the-art decision accuracy in prior authorization.
Prior authorization (PA) requires interpretation of complex and fragmented coverage policies, yet existing retrieval-augmented systems rely on static top-$K$ strategies with fixed numbers of retrieved sections. Such fixed retrieval can be inefficient and gather irrelevant or insufficient information. We model policy retrieval for PA as a sequential decision-making problem, formulating adaptive retrieval as a Markov Decision Process (MDP). In our system, an agent iteratively selects policy chunks from a top-$K$ candidate set or chooses to stop and issue a decision. The reward balances decision correctness against retrieval cost, capturing the trade-off between accuracy and efficiency. We train policies using Conservative Q-Learning (CQL), Implicit Q-Learning (IQL), and Direct Preference Optimization (DPO) in an offline RL setting on logged trajectories generated from baseline retrieval strategies over synthetic PA requests derived from publicly available CMS coverage data. On a corpus of 186 policy chunks spanning 10 CMS procedures, CQL achieves 92% decision accuracy (+30 percentage points over the best fixed-$K$ baseline) via exhaustive retrieval, while IQL matches the best baseline accuracy using 44% fewer retrieval steps and achieves the only positive episodic return among all policies. Transition-level DPO matches CQL's 92% accuracy while using 47% fewer retrieval steps (10.6 vs. 20.0), occupying a"selective-accurate"region on the Pareto frontier that dominates both CQL and BC. A behavioral cloning baseline matches CQL, confirming that advantage-weighted or preference-based policy extraction is needed to learn selective retrieval. Lambda ablation over step costs $\lambda \in \{0.05, 0.1, 0.2\}$ reveals a clear accuracy-efficiency inflection: only at $\lambda = 0.2$ does CQL transition from exhaustive to selective retrieval.
