Improve Prospective Claims Analysis

January 29, 2013
2:00 - 3:30 p.m. EST
Online

Why 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 payment processes.

This 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.  

Webinar 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: 

Todd Perry

Todd Perry
Managing Director and Health Analytics Subject Matter Advisor, Accenture

Todd is global lead for Accenture Health Payer Analytics with 18 years of experience in healthcare, both in the payer and provider segments. 

 

Leana FowlerLeana Fowler
Managing Director and Quality Assurance Director, Accenture

Leana 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.