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ARXIV:2604.05125 · HEALTHCARE AI · SUBMITTED 08 APR · 05:54 UTC · FRESHNESS UNKNOWN
ARXIV:2604.05125HEALTHCARE AISUBMITTED 08 APR · 05:54 UTCFRESHNESS UNKNOWNRuslan Sharifullin · Maxim Gorshkov · Hannah Clay · arXiv
An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency.
Opportunity summary
Pain An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency. Such fixed retrieval can be inefficient and gather irrelevant or insufficient information.
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…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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)…
Healthcare AI moved forward this cycle; last verified April 2026. Public score 4.0/10.
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An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency.
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10.48550/arXiv.2604.05125An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency.
Abstract
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 $λ\in \{0.05, 0.1, 0.2\}$ reveals a clear accuracy-efficiency inflection: only at $λ= 0.2$ does CQL transition from exhaustive to selective retrieval.
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Dimensions overall score 4.0
PROBLEM
An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency. Such fixed retrieval can be inefficient and gather irrelevant or insufficient information.
METHOD
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 ins...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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 bes...
WHY NOW
Healthcare AI moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed public claims while anchored extraction refreshes.
An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency. Such fixed retrieval can be inefficient and gather irrelevant or insufficient information.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. 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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Healthcare AI moved forward this cycle; last verified April 2026. Public score 4.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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An adaptive policy retrieval system for prior authorization using offline RL to balance decision correctness with retrieval efficiency.
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Healthcare AI
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4.0/10 public viability
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