Can GPT Improve the State of Prior Authorization via Guideline Based
  Automated Question Answering?
Shubham Vatsal Ayush Singh Shabnam Tafreshi
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.