Improve Prospective Claims Analysis
January 29, 2013
2:00 - 3:30 p.m. EST
pay-and-chase when you can make the right decision the first time? Incorrectly paid claims require fixing,
resulting in rework, waste and friction with members and providers. Claims errors, waste and fraudulent incidents
are most often examined retrospectively to determine what happened, what was
paid incorrectly, and what types of complaints are being received. A better – and market leading – solution is
to look at transactions before the claim is paid to predict and improve claim
webinar discussion will explore sophisticated capabilities and models to
predict and solve for claims payment issues.
Machine Learning algorithms, like Support Vector Machine (SVM), evaluate
thousands of permutations to predict issues that lead to over and under
payments that others miss.
attendees will learn about the opportunities to apply predictive modeling to
prospectively improve claims payment processes before any errors arise, thereby
creating tremendous value for both health payers and providers.
List of Speakers:
Director and Health Analytics Subject Matter Advisor, Accenture
is global lead for Accenture Health Payer Analytics with 18 years of experience
in healthcare, both in the payer and provider segments.
Director and Quality Assurance Director, Accenture
leads Accenture Health Payment Integrity Business Services Practice and has
worked across payers to maximize medical and administrative cost savings
through Payment Integrity.
The content presented in this webinar is solely attributable to the speaker and does not represent an endorsement by America's Health Insurance Plans (AHIP) of the accuracy of the information presented in the audio conference or any opinion expressed by the speaker.