Offline RL for Adaptive Policy Retrieval 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 t...
| Authors | Ruslan Sharifullin, Maxim Gorshkov, Hannah Clay |
|---|---|
| Primary category | cs.IR |
| Published | 6 April 2026 |
| Last updated at source | 6 April 2026 |
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