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The study that could help my work would be the NLI (natural language inference). The NLI is a tool to highlight medical codes that weren't previously identified during encounters. It allows the coder to validate whether the medical code discovered by NLI is to be coded or deleted for CMS. Some of the codes that are found on the medical record are either current conditions, history codes, or part of the family history. The NLI can make it easier for the coder to find hidden codes, but it can be time-consuming to have to manually delete the codes, update the pages, and add the provider information.

The variable that would be tested: The company wanted to study if less than 55% of the encounters that used the NLI tool were able to find current chronic conditions to validate. They randomly selected 350 encounters to study. They determined that 200 of the encounters that were studied had chronic conditions. The Ho: p < 0.55 Ha: p>0.55.

Test statistics (z): 0.81(rounded)

According to my data: I reject the null hypothesis because the z score 0.81 is greater than the Ho: 0.55.

Renjit, S., & Idicula, S. (2021). Natural language inference for Malayalam language using language agnostic sentence representation.PeerJ (San Francisco, CA),7, e508-e508. https://doi.org/10.7717/peerj-cs.508Links to an external site.

Chamberlain University (2024). Math 225 Statistical Reasoning for the Health Sciences: Week 7 lesson. Downers Grove, IL: Online Publication