Sep 9–12, 2019

Improving revenue cycle management with deep learning: A healthcare case study

Sanji Fernando (Optum)
2:35pm3:15pm Thursday, September 12, 2019
Location: 230 A
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Professionals seeking to learn practical information from a real life example on developing and deploying a deep learning model in healthcare




Deep learning is attracting attention in healthcare in a variety of areas. A lot of work is underway in training deep learning models to support clinical applications, such as the prediction and diagnosis of disease. While promising in these areas, deep learning also has inherent risks in interpretability of results and quality of data that need to be addressed before it’s more widely used in the clinical environment. On the other hand, there is near-term opportunity to use deep learning to streamline operational processes without running the risk of applications in diagnostic and decision support tools.

OptumLabs recently trained and deployed a deep learning model that’s making an impact in prioritizing work and cases for manual review to achieve operational efficiencies. A significant number of these cases are challenging for provider organizations to evaluate against medical necessity criteria for reimbursement because they are not clear-cut. They often result in denials, rework, or inappropriate reimbursement of insurance claims. Often a physician advisor is engaged to do a second-level review when cases don’t meet initial medical necessity criteria or if the impact of regulatory or payer guidelines needs to be considered. There’s a lot of labeled data available on the inputs and outputs of decisions made in this process, creating fertile ground for a deep learning model to help drive a more efficient and compliant reimbursement process. With this deep learning model, all admissions can now be screened automatically to identify the ones that need a second-level review to support the medical necessity review process. The model helps drive a more efficient and compliant reimbursement process by engaging physician advisors to review only the challenging cases where expertise is most needed.

Sanji Fernando explores his experience from the end-to-end process of building, deploying, and operating a deep learning model that improves hospital revenue cycle management, including use case understanding, performance metric selection, data preparation, tool selection and trade-offs, model development and optimization, model selection, deployment, and model operations. Sanji also details key knowledge gained along the way on what worked well and opportunities for improvement in training future deep learning models in healthcare.

Prerequisite knowledge

  • A basic understanding of deep learning

What you'll learn

  • Gain an understanding of training a deep learning model
Photo of Sanji Fernando

Sanji Fernando


Sanji Fernando is a senior vice president at Optum. He leads the artificial intelligence (AI) and analytics platforms team for Optum Enterprise Analytics (OEA), supporting the design and development of leading-edge AI models and analytic tools for the enterprise. Previously, Sanji was a vice president at OptumLabs and led the OptumLabs Center for Applied Data Science (CADS), which applied breakthroughs in AI and machine learning to solve complex healthcare challenges for UnitedHealth Group (UHG) by developing and deploying software product concepts; was the head of data science for the Cloud Computing Group and HERE, the navigation services division, and worked in a variety of other roles with Nokia; was cofounder and vice president of engineering at Vettro, a venture-backed mobile software company; and was a consultant with Viant and Accenture. Sanji is a graduate of Trinity College with a bachelor’s degree in computer science.

  • Intel AI
  • O'Reilly
  • Amazon Web Services
  • IBM Watson
  • Dataiku
  • Dell Technologies
  • Intuit
  • Gamalon
  • Hewlett Packard Enterprise
  • MapR Technologies
  • Sisu Data
  • Intuit

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