How insurance companies are beating your diagnostic lab out of revenue — and what you can do about it.

$299.00

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How Insurance Companies Are Beating Your Diagnostic Lab Out of Revenue — and What You Can Do about It!


Presenter: Sean McSweeney
  CEO, Apache Health
Duration: 60 minutes
Price: $299.00

Here’s How Your Lab Can Fight Back against Claim Denials, Claim Underpayments, Slow Payments, and Other Actions Payers Commonly Take to Minimize Payments to Labs!

If you own or manage a diagnostic lab, you already know it’s NOT a level playing field when it comes to getting paid by insurance companies.

Insurance companies routinely take advantage of labs. They underpay you, deny claims inappropriately, and request additional documentation in order to slow down payment.

Worse still, denied claims can be very difficult to challenge because insurance companies increasingly are using
highly-sophisticated data analytics, and even artificial intelligence, to
fight lab payment claims.

Some insurers are even pooling resources in order to leverage big data and artificial intelligence to avoid paying labs.

But You Can Fight Back and Win!

In order to increase the odds that your claims get paid on time, and in full, your lab needs to
use the same sophisticated data-driven weapons insurance companies are using against you!

Join us on March 6th, and Sean McSweeney, CEO of Apache Health, will explain how your lab can use data analytics to fight back against claim denials, claim underpayments, and slow claim payments to actually increase your lab revenue.

Here’s just a sample of what you’ll learn at this Webinar:
  • How can labs use the same analytics tools against the payers to reduce claim denials
  • How the right data tools can generate significant additional revenue for diagnostic labs – and help you get paid more quickly
  • How virtually any diagnostic lab, large or small, can quickly and easily benefit from the rapidly-growing technologies of machine learning and artificial intelligence in RCM
  • How it’s possible for labs to pool resources in the same way insurance companies do to further level the playing field

When you leave this webinar you will be able to:
  • Ask the right questions to identify where claim money is being lost in your diagnostic lab
  • Begin using data analytics to quantify performance and fight back against the insurance companies
  • Identify specific opportunities for capturing more revenue from insurers

Make no mistake: diagnostic labs across America are losing tens – even HUNDREDS — of thousands of dollars EVERY MONTH to claim denials, claim underpayments, and common insurance company practices that delay payments.

This webinar shows what you can do to fight back and recapture the money that you are owed.

Your Presenter:

Sean McSweeney, CEO, Apache Health

Sean McSweeney is Founder and President of Apache Health. Sean brings over two decades of industry experience in healthcare general management, marketing, sales, finance, and information systems. Prior to Apache Health, Sean was the President of Cobalt Health, a leading national laboratory billing company with over 125 employees. Sean led Cobalt to being the largest toxicology and molecular diagnostics laboratory billing company in the country. Prior to Cobalt, Sean was a Director at 4medica, a laboratory software firm in Los Angeles developing clinical information systems, especially online ordering and resulting for outreach laboratories. He developed corporate and product strategy, led marketing and communications, managed product development and was responsible for business development. Prior to 4medica, Sean managed the CT business for Toshiba America Medical Systems, leading it from $100 million to over $250 million per year in revenues. Before Toshiba, Sean held various sales roles for GE Medical Systems, where he received awards for regional and national sales performance. Sean holds an MBA with honors from the Columbia Graduate School of Business, as well as a bachelor’s degree with honors in electrical engineering from Dartmouth College.

 

 


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