Machine Learning for Healthcare Payer and Provider Orgs

This is a technology and application overview in depth on ML and AI applications in Healthcare Analytics . It is designed to help Payers and Providers understand the role and use cases of Machine Learning for Data Analytics and their applications to Healthcare. There is broad agreement in the industry and among research communities that AI will significantly alter and improve healthcare. However, there is a large gap between the generic optimism for revolutionary AI applications in the distant future such as cyborg physicians, fully automated clinics and care supported by robotics, and the current, near-term feasibility of ML and AI use cases from both business and technology points of view. In this talk, we will focus on this schism and first deconstruct it using a number of ML and AI use cases in the Healthcare industry from the point of view of 4 stake holders - Payer (or Insurance Plan), Provider (or Clinic/Hospital), Employer (or Stet, CMS) and Consumer (aka Member or patient). To this end, we will first provide an in depth introduction to Machine Learning, its essential methods and algorithms, and the tools used such as R, Spark, Storm and Hadoop, and the relationships among ML, AI, Statistics and the traditional Business Intelligence (BI). This will help one to understand the essential innovative value and novelty of ML and AI methods in the context of HIT. Using a few specific Payer and Provider Use Cases, we will discuss how ML and AI can be used to enrich the analytics practice for Healthcare. We will then conclude with a discussion on how to critically evaluate the ML and AI use cases, apps and start-ups for Healthcare, and identify the ones which could be profitably deployed in the near-term, intermediate term and long term. This Workshop is based on a condensed version of 2 courses taught by Prasad Saripalli at the University of Washington.

Prasad serve as a Distinguished Engineer at Capital One, a technology driven bank on the Fortune 100 list, redefining Fintech and Banking using data, technology, AI and ML in unprecedented ways. Most recently, he served as the Vice President of AIML and Distinguished Engineer at MindBody Inc - a portfolio company of Vista which manages the world's fourth-largest enterprise software company after Microsoft, Oracle, and SAP. Earlier, I served as VP Data Science at Edifecs, an industry premier healthcare information technology partnership platform and software provider, where we built Smart Decisions ML & AI Platform with Ml Apps Front. Earlier, he served as CTO and VP Engineering at Secrata.com, provider of Military grade Security and Privacy solutions developed and deployed over the past 15 years at Topia Technology for the Federal Government and the Enterprise, and as CTO & EVP at ClipCard, a SaaS based Hierarchical Analytics and Visualization platform.

Prasad has doctoral training in Engineering and Computer Science from the University of Florida and post-doctoral training from the University of Texas, and teached ML, Advanced ML, AI, NLP and Distributed Systems at Northeastern University.