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.

医疗保险公司使用先批准（PA）等过程进行费用控制，而本研究评估了GPT在验证关键因素方面的有效性，以帮助健康计划更快地做出决策，并发现我们的方法在自然语言生成方面表现出优越的性能。

通过基于准则的自动问答，GPT能改进事前授权状态吗？