US 12,293,835 B1
Systems and methods for authorization automation using artificial intelligence
Ayush Mathur, Novi, MI (US); Brian Fornelli, Indianapolis, IN (US); Xiaoyu Sun, Indianapolis, IN (US); Xinkai Chen, Indianapolis, IN (US); James D. Martindale, Indianapolis, IN (US); Harsha Arcot, Indianapolis, IN (US); Madeline Glasheen, Indianapolis, IN (US); Summer Ashley, Indianapolis, IN (US); Stephanie Wilson-English, Indianapolis, IN (US); Anthony Nguyen, Indianapolis, IN (US); Vincent Pantone, Indianapolis, IN (US); Urmesh Shah, Indianapolis, IN (US); Chao Zhang, Indianapolis, IN (US); Pice Chen, Indianapolis, IN (US); and Adarsh Ramesh, Schaumburg, CT (US)
Assigned to Elevance Health, Inc., Indianapolis, IN (US)
Filed by Elevance Health, Inc., Indianapolis, IN (US)
Filed on Apr. 13, 2023, as Appl. No. 18/300,249.
Claims priority of provisional application 63/362,918, filed on Apr. 13, 2022.
Int. Cl. G16H 50/20 (2018.01)
CPC G16H 50/20 (2018.01) 9 Claims
OG exemplary drawing
 
1. A machine learning based method for authorizing the performance of a treatment, comprising the steps of:
receiving a treatment authorization request for a treatment, the treatment authorization request including a historical record of the person who will receive the treatment and treatment identifying information relating to the treatment;
creating an extracted text of the historical record using optical character recognition on the historical record;
determining whether to analyze authorization performance of the treatment using a machine learning authorization process, wherein the determination is based on treatment identifying information and whether treatment authorization guidelines exist for the treatment;
in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process:
identifying authorization criteria for the treatment based on the treatment authorization guidelines, wherein the authorization criteria includes records data conditional to authorization of performance of the treatment;
identifying a natural language record processing model corresponding to the treatment authorization guidelines;
performing natural language processing on the extracted text of the record in accordance with the identified natural language record processing model to identify relevant record data in the record;
determining whether the relevant record data meets the authorization criteria;
in response to a determination that the relevant record data meets the authorization criteria, authorizing the treatment; and
in response to a determination that the relevant record data does not meet the authorization criteria:
recognizing a specified keyword within the extracted text, using a named entity recognition model (NER), wherein the NER is trained using policy data relating to a policy having the policy type, and wherein the specified keyword is identified as relating to the policy type; and
displaying a visual representation of the clinical document on a display, wherein the visual representation comprises a visual indicator of the presence of the specified keyword on the page.