Summary of Can Gpt Improve the State Of Prior Authorization Via Guideline Based Automated Question Answering?, by Shubham Vatsal et al.
Can GPT Improve the State of Prior Authorization via Guideline Based Automated Question Answering?
by Shubham Vatsal, Ayush Singh, Shabnam Tafreshi
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper explores whether a language model called GPT can assist health insurance companies in processing prior authorization requests more efficiently. The prior authorization process requires healthcare professionals to obtain approval from insurers before performing certain procedures on patients, which is time-consuming and challenging. The authors frame this task as a question-answering problem, prompting GPT to answer questions based on patient electronic health records. They experiment with different conventional and novel prompting techniques, achieving a mean weighted F1 score of 0.61, outperforming standard methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to help health insurance companies make faster decisions. When doctors want to do a certain procedure on a patient, they need to get permission from the insurance company first. This process can take a long time and be difficult. Researchers tried using a language model called GPT to see if it could help with this task. They asked GPT questions about patients based on their medical records and found that it was able to make good decisions most of the time. |
Keywords
* Artificial intelligence * F1 score * Gpt * Language model * Prompting * Question answering
