How AI/ML is Driving Transparency in Health Care Quality Improvement Programs

  • December 07, 2020
  • 11:00 AM – 12:00 PM ET
  • Online

Recent CMS and NCQA regulations are paving the way to a member-centric health care ecosystem. However, these regulations have turned the tables on health insurance providers, pushing them to reorganize their operational focus areas – with a greater emphasis on member experience and interoperability. The regulations have brought in multiple policy changes that impact health insurance provider operations including changing quality measures, new timelines, interoperability, digitized quality, network fulfilment and MLR compliance, among others. Health insurance providers now need to modernize their Quality Improvement initiatives to include prospective and super-prospective approaches.

Health insurance provider and health care provider e2e workflows need to be enriched with information and supplemented with strong analytic capabilities that enable them to respond quickly and spontaneously. This goes a long way in building delightful member experience at every step of the way.

Also, health insurance providers need to implement approaches that warrant transparency across processes while delivering on quality management objectives. By making proactive technology investments and driving processes in a planned fashion, they can effectively identify and bridge transparency gaps across the care delivery and quality measurement value chain.

This webinar will discuss how health insurance providers can utilize data science and analytics to drive transparency across the care continuum. We will talk about current transparency needs and prevailing gaps across the care delivery and quality measurement value chain – data, analytics, workflows, and engagement. We will also address the role of data science and analytics in building transparency for quality programs such as HEDIS and CMS STAR ratings. Subsequently, the webinar will establish through live examples and case snippets, how to bridge transparency across the value chain while demonstrating disruptive technology adoptions to orchestrate an outcome and experience driven quality improvement approach.

By attending this webinar, participants will be able to:

  • Identify factors driving the need for transparency with respect to quality operations and traits of successful health insurance providers
  • Understand the role of data science and analytics in structuring health plan operations, with the aim of driving transparency across the quality enterprise – e.g., chart retrieval, Star rating improvement, provider network management, etc.
  • Derive actionable steps to leverage AI/ML and NLP to embed transparency across data, analytics, workflow, and engagement, to streamline quality management initiatives


Jeffrey Springer
SVP Products & Solutions

Jeff has more than 20 years of leadership experience in healthcare, having worked with leading healthcare technology vendors. He leads product management, business analysis and product strategy at CitiusTech and is a pioneer in the payer technology space, having founded the first payer-provider contract management company in the US. He went on to lead product management and strategy at Siemens and MEDecision.

Jeff holds a Mechanical Engineering Degree and an MBA from University of Pennsylvania – The Wharton School, where he graduated as a Palmer Scholar.

Shitang Patel
AVP Health Plans

Shitang Patel is a Senior Healthcare Consultant at CitiusTech, with 15 years of experience spanning healthcare management and health policymaking settings, with a focus on payer strategy, business operations and supporting technologies. Prior to joining CitiusTech, Shitang worked for PwC Health Industries Advisory serving payer and provider clients, and also worked in the areas of health services research and policymaking in Washington D.C.

Shitang holds a Master’s degree in Health Services Administration from the University of Michigan.

Suman Giri, PhD
Director, Data Science

Dr. Suman Giri has more than 8 years of experience in Data Science and Engineering, and is passionate about impact driven problem-solving at scale through intelligent use of data and design. He is an experienced technology leader (AI/ML, RPA) in the integrated payer-provider (IDFS) market with a focus on integration of analytics into workflow and setting up high performing data science functions.

Previously, he was the Chief Data Scientist and Head of AI at EEme LLC, a Pittsburgh based startup (acquired by Tendril) and worked in various leadership and management positions at Aetna and Highmark (BCBS).

Suman holds a doctorate in Advanced Infrastructure Systems from Carnegie Mellon University and a BS in Mathematics and Physics from Oberlin College.