CLOUD COMPUTING FORUM: A HIMSS EVENT

HIMSS18 Annual Conference
Wynn Las Vegas
Mar. 5, 2018

Zahoor Elahi

Senior Vice President of Strategic Product Management
Optum

Zahoor Elahi serves as senior vice president of Strategic Product Management at Optum, developing the company’s transformative, end-to-end health care IT and business process solutions. In his role, Zahoor leads thought leadership, ideation, and product development using informed market- and client-centric strategic information and technology roadmaps. He is also responsible for ensuring the full suite of Optum products and solutions are optimized to maximize client value in the changing healthcare ecosystem.

Zahoor has more than 25 years of leadership experience in management, product development and business growth. Prior to joining Optum, he worked at Fidelity Information Services, where he grew two business units by developing and implementing payment solutions and information technology for providers, payers, third-party administrators and banks. Additionally, he has acquired, developed and subsequently sold several successful businesses.

March 5, 2018
4:10pm - 4:35pm
Mouton 2

Personalized medicine and machine learning are two of the hottest topics in healthcare, and no event at HIMSS would be complete without offering a perspective on their potential to transform healthcare. In this session, attendees will learn how the cloud offers a great opportunity to maximize their combined potential to drive value by improving outcomes.

Zeeshan Syed, founder of Health at Scale and director and clinical associate professor at Stanford Medicine, will discuss the use of the public cloud's development capabilities for iterative design of powerful machine learning systems and the execution of massive-scale experiments for machine-based learning.

As a case study, Zahoor Elahi, senior vice president of Optum, will discuss how UnitedHealthcare leveraged Health at Scale's machine-learning cloud platform to coordinate care for 40 million members and proactively refer patients to individually-optimal providers within complex care networks.

Key takeaways:

  • As more clinical and claims data becomes available, cloud-based machine learning allows for continuous refinement of predictive algorithms.
  • Cloud deployment allows modular machine learning algorithms to be updated and improved via input from a multi-disciplinary team of stakeholders.
  • Cloud computing democratizes machine learning and makes it accessible to different stakeholders in the health care system.
  • Running parallel experiments at scale allows machine learning to iteratively get smarter about predicting optimal providers for specific patients.

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