Team Name
Benjamin & Joseph (Benjo)
Track
Health care & Life Sciences, IoT
Use Case
Developing a solution to automate and scale the line by line auditing of medical bills to ensure integrity and fairness for all healthcare payers
Technology
Python, SAS Visual Text Analytics, Intelligence Decisioning, Machine Learning, Analytics for IoT and Event Stream Processing
Region
APAC
Team lead
@KeithJong
Team members
@AngieLiu , @fina_whd @sounddust @keithpng
Social media handles
Benjo Team
Is your team interested in participating in an interview?
Y
Optional: Expand on your technology expertise
Keith has been a software engineer for the past 9 years, during which he has developed various innovative solutions. Angeline is a nurse by profession with over 20 years of experience. She has 8 years of professional medical bill auditing and is currently contributing to the development of the knowledge base for the solution.
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<BenJo_Pitch>
<BenJo_Jury>
Auditing is widely recognized as a labor-intensive and meticulous task, prone to human errors due to fatigue and skill limitations. However, the advent of machine learning and advanced natural language processing models has made the automation of audit processes increasingly viable.
Our objective is to concentrate on the production of an automated medical bill auditing solution, leveraging our team's expertise in this domain. Conducting a comprehensive audit of every item on medical bills for fraud and errors is currently impractical due to the vast volume of items. Manual audits, even of samples, would compromise both the time efficiency and accuracy of the process. By integrating SAS's machine learning capabilities with our experts' knowledge and proprietary business rules model, we can reduce human errors and enhance the scalability of medical bill auditing.
The risks associated with automation can be mitigated by involving human experts in the development and review stages, ensuring a robust and reliable automated auditing process.
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