The Impact of Quality Incentive Models in Medicaid Markets

  • November 30, 2017
  • 1:00 PM – 2:00 PM ET
  • Online
about

Thirty-one of our fifty states now have Medicaid managed care, and several markets are expected to implement managed care in the next few years. More than $160B in Medicaid spending occurs through the Managed Care Organizations. As more and more states seek to do more with less, increasing accountability for health quality outcomes is placed on health plans.

Most managed care states tie performance on key HEDIS measures to capitation incentives or withhold arrangements. Each state model is different in terms of the measures emphasized and the percent of capitation revenues at risk (ranging from 1-5%).

Join this webinar to learn the typical quality payment approaches states use, issues often faced by health plans under each model and what states are expected to do with payment models tied to quality performance in light of near term Medicaid reform efforts.

Attendees will learn:

  • Learn key insights about the different pay for quality models used in state Medicaid managed care programs
  • Understanding of how plans are looking to their revenue management organization to implement strategies and solutions
  • Identify emerging trends and upcoming changes to pay-for-quality models in major markets
  • Learn how to maximize the impact of a single in-home visit to address multiple care gaps, capture important risk adjusting diagnoses and enhance the individual care plan
  • Learn about proven solutions to close quality gaps for your enrollees, including case studies on Medicaid chronic care populations
  • Improve key measures related to pediatric health, including Well Child visits and lead screening
  • Improve traditionally challenging diabetic quality scores, through innovative in-home tests
  • Avoid high cost, acute admissions and re-admissions with better treatment and monitoring of multiple chronic conditions
  • Learn about best practices in Medicaid Long Term Services and Supports, including strategies for your dual eligible population
  • Learn about revenue management synergies in Medicare and Medicaid for dual plan members

SPEAKERS


Jenny Ritchie
Vice President, Sales
Matrix Medical Network

Ms. Ritchie has more than 20 years of health care industry experience bringing innovative business, operational and clinical solutions to health plans throughout the country.

She joined Matrix in early 2017 with a focus on helping health plans better manage risk within their Medicare Advantage, Managed Medicaid and Commercial member markets. Previously, she worked with national, regional, and Blues health plans on HEDIS and Stars measurement programs to drive overall quality improvement. Since joining Matrix, she has helped lead the company’s efforts in bringing quality performance programs to the forefront.

Prior to joining Matrix, Ms. Ritchie served as regional vice president of payer sales at Availity, a healthcare information technology company that helps improve the collaboration between health plans and providers.

Ms. Ritchie has a Bachelor of Science degree in Marketing from Clemson University.


Clay Farris
Director of Operations

Mostly Medicaid

Mr. Farris has advised CMS administrators, state Medicaid Directors and a wide range of other clients in the healthcare industry. His unique blend of management consulting, project management, policy making and analytics help deliver on time, actionable results for a wide array of business challenges.

His experience includes policy making at both the federal and state levels, management consulting for large organizational change initiatives, big data solution sales and implementation and cutting-edge analytics. He currently serves as the Director of Operations for Mostly Medicaid, where he leads key project components related to consultation design, Medicaid subject matter expertise and project management.

He has a Masters Degree in Health Policy from the Johns Hopkins Bloomberg School of Public Health and is also a Certified Internal Auditor.