Sep 9–12, 2019

Improving revenue cycle management with deep learning – a health care case study

Sanji Fernando (OptumLabs)
2:35pm3:15pm Thursday, September 12, 2019
Location: 231

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 health care. There haven’t been many examples of deep learning models deployed in health care, making this session distinctive in content.

Level

Intermediate

Description

Deep learning is attracting attention in health care in a variety of areas. A lot of work is underway 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 is 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 is 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, and/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 is 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 result is the model helps drive a more efficient and compliant reimbursement process by engaging physician advisors to review only the challenging cases where their expertise is most needed.

In this session, Sanji Fernando, Vice President and Head of the OptumLabs Center for Applied Data Science, will share 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 tradeoffs, model development and optimization, model selection, deployment, and model operations. Sanji will also share key learnings along the way on what worked well and opportunities for improvement in training future deep learning models in health care.

Prerequisite knowledge

Attendees should have a basic understanding of deep learning.

What you'll learn

The audience will gain an understanding of how the presenter approached training a deep learning model, including use case understanding, performance metric selection, data preparation, tool selection and tradeoffs, model development and optimization, model selection, deployment and model operations.
Photo of Sanji Fernando

Sanji Fernando

OptumLabs

SANJI FERNANDO is a Vice President at OptumLabs, where he leads The Center for Applied Data Science (CADS). CADS focuses on the application of new data science methods to solve complex health care challenges by applying breakthrough innovations in artificial intelligence and machine learning to create software product concepts.

Sanji joined OptumLabs in 2014 from Nokia, where he was the Head of Data Science for Nokia’s Cloud Computing Group and HERE, Nokia’s navigation services division. Sanji spent 9 years at Nokia in a variety of roles with Nokia’s Multimedia Division, Nokia Research and Nokia Ventures.

Prior to Nokia, Sanji was a co-founder and VP of Engineering of a venture-backed mobile software company, Vettro. Sanji began his career in management consulting.

Sanji is a graduate of Trinity College with a bachelor’s degree in computer science. He lives in the Boston area with his wife and three boys. In his free time, Sanji enjoys coaching his sons in basketball and baseball.

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