# Can GPT Improve the State of Prior Authorization via Guideline Based Automated Question Answering?

arXiv: https://arxiv.org/abs/2402.18419 · HTML: https://mlsanity.com/paper/2402.18419/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Built on by (most cited first, 7 of 7)

- [A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks](https://mlsanity.com/paper/2407.12994/) (2024)
- [HealthAdminBench: Evaluating Computer-Use Agents on Healthcare Administration Tasks](https://mlsanity.com/paper/2604.09937/) (2026)
- [Attention to Non-Adopters](https://mlsanity.com/paper/2510.15951/) (2025)
- [Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation](https://mlsanity.com/paper/2603.19264/) (2026)
- [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026)
- [An Overview and Discussion on Using Large Language Models for Implementation Generation of Solutions to Open-Ended Problems](https://mlsanity.com/paper/2501.00562/) (2024)
- [Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design](https://mlsanity.com/paper/2604.17988/) (2026)

## Abstract (arXiv)

Health insurance companies have a defined process called prior authorization (PA) which is a health plan cost-control process that requires doctors and other healthcare professionals to get clearance in advance from a health plan before performing a particular procedure on a patient in order to be eligible for payment coverage. For health insurance companies, approving PA requests for patients in the medical domain is a time-consuming and challenging task. One of those key challenges is validating if a request matches up to certain criteria such as age, gender, etc. In this work, we evaluate whether GPT can validate numerous key factors, in turn helping health plans reach a decision drastically faster. We frame it as a question answering task, prompting GPT to answer a question from patient electronic health record. We experiment with different conventional prompting techniques as well as introduce our own novel prompting technique. Moreover, we report qualitative assessment by humans on the natural language generation outputs from our approach. Results show that our method achieves superior performance with the mean weighted F1 score of 0.61 as compared to its standard counterparts.