Thi Montalvo on the challenges employers face in using their health benefits data
June 07, 2021
Suzanne Delbanco talks with Thi Montalvo, Health Analytics Practice Leader – Health and Benefits for Willis Towers Watson. In her role, Thi helps employers and other health care purchasers make data-driven decisions for their health benefits strategy. While she’s seen many successful use-cases over her nine years specializing in health analytics, she’s also seen firsthand the many problems that are hindering the acquisition and use of data for optimizing employer-sponsored health benefits. From onerous legal agreements limiting the use of the data, to incomplete data fields, to lack of visibility into necessary information like diagnosis codes or provider specialty information, employers and other health care purchasers are struggling to put their own benefits data to use, despite being the ones who are footing the bill.
Learn how the proliferation of condition-specific benefit programs (often referred to as “point solutions”) has changed the data landscape in the employer-sponsored health care market, and what hoops benefit consulting firms like Willis Towers Watson have to jump through to perform the analyses that their clients need. Thi Montalvo and Suzanne also discuss what’s necessary to improve data sharing moving forward, and how the secure and reliable flow of aggregated, de-identified health data is an important piece of the evolving journey to improve price and quality transparency in the commercial health care sector.
“Are there opportunities to steer to a quality provider or facility down the street? If we can’t do that with the data, then we’re not serving the interest of our employers and their employees and getting them the health care that they need.” – Thi Montavlo, Willis Towers Watson
This episode is the first of many in CPR’s Data Series within the Listening In (With Permission) podcast. Click here to learn more about what CPR is doing to catalyze data sharing improvements across the health care market.