Method for Computer-Assisted Medical Billing Using Natural Language Processing Techniques

Case ID:
2021-028

­BACKGROUND

Medical billing is critical to the revenue cycle management of healthcare providers; accurate and efficient billing is crucial to reimbursing healthcare providers for their services. However, current systems depend on human coders parsing through clinical summaries and notes to list treatments for billing and insurance reimbursement. Unfortunately, this is a time-consuming process prone to errors, further complicating medical billing. Existing computer-assisted systems are simple broad human-computer interactions that do not address the root problem of the complex nature of healthcare communications. There is an essential need for an effective computer-assisted process by which human coders can be relieved of their burdens to ensure accurate and speedy bill processing for healthcare providers.

SUMMARY OF TECHNOLOGY

Investigators at OSU have developed a novel two-step method for extracting critical information for medical billing directly from clinical discharge and summary notes. The first step segments clinical notes into important sections, using predefined dictionaries and rules. The second step extracts the information from these segmented sections, using rule-based algorithms and deep learning models to then generate treatment codes that are fed into a billing engine. This novel system would take this time consuming and difficult process and make a system able to use direct clinical notes to reduce the workload on human coders, increasing billing accuracy and efficiency. 

POTENTIAL AREAS OF APPLICATION

  • Healthcare provider settings in which human coders document treatments for billing

MAIN ADVANTAGES

  • Reduces burden on human coders in medical settings
  • Needs only simple discharge summary notes to function

COMMERCIAL OPPORTUNITY

This market has virtually guaranteed sustained growth. The medical billing outsourcing market for 2021 was estimated to be $11.3 billion. However, it is projected to have a compound annual growth rate of 12.66% for the next seven years to $25.9 billion by 2028.

STAGE OF DEVELOPMENT

  • Prototype
Patent Information:
For Information, Contact:
Jai Hariprasad Rajendran
Commercialization Officer
Oklahoma State University
jair@okstate.edu
Inventors:
Zhuqi Miao
Suhao Chen
Andrew Gin
Thanh Thieu
William Paiva
Keywords:
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