Simplifying the Business of Healthcare

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Tracy Flynn
8 years ago
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1 Simplifying the Business of Healthcare

2 Big data revolution couldn t have come at a better time The big data revolution has invaded healthcare, and it couldn t come at a better time. Health plans are ramping up capacity to cover the millions of new lives that have been granted access to more affordable insurance under healthcare reform, while also fi nding ways to trim operational costs to stay compliant with the medical loss ratio (MLR) requirements. Big data-fueled analytics are positioned to make a key contribution in helping payers solve some of their greatest challenges, including fraud, waste and abuse (FWA), brought on by this rapidly changing and expanding landscape. However, managing the terabytes, petabytes, and exabytes of information associated with big data isn t easy since typical repositories have limited ability to capture, store and manage such large amounts of information. Additionally, these repositories often cannot effectively communicate with other data sources, preventing a payer from synthesizing claims, medical and other information in a meaningful way. Health plans that can effectively use merged and analyzed data sourced from multiple payers, however, will be positioned to succeed in an uncertain market. Effective and creative use of big data could reduce U.S. healthcare spending by $300 billion to $450 billion, or 12 percent to 17 percent of the $2.6 trillion in baseline healthcare costs. - McKinsey & Company analysis

operational costs to stay compliant with the medical loss ratio (MLR) requirements.

3 Using big data for relief from fraud, waste and abuse Payers have long been aware and exposed to fraud, waste and abuse. The National Health Care Anti-Fraud Association (NHCAA) estimates that between $68 and $226 billion is lost annually to FWA. Abuse alone accounts for as much as 10 percent of healthcare spending, according to the FBI s Financial Crimes unit. These losses drain health plan resources and create fi nancial repercussions that extend to employers and consumers. Using technology to combat and recover FWA is nothing new to the industry, but traditionally, with access to only their own claims data and limited analytics, many payers have achieved only moderate success. For example, many health plans will leverage technology to identify abnormalities or suspicious patterns in claims and then try to associate it with a provider who has a history of overbilling. From there, the plan would likely provide the information to a special investigations unit (SIU) that manually checks suspected FWA claims and escalate the case as necessary. To proactively change the game, health plans need to elevate big data strategies that allow data sets from multiple sources to be combined, and more sophisticated analytics to be applied. Imagine patient lab or radiology results, historical electronic health record (EHR) data or even a picture of a provider s billing patterns across multiple payers enriching and increasing the accuracy of analytics. Health plans will gain both a much better understanding of care delivery and payment behaviors as well as more actionable intelligence in the fi ght against FWA. Traditional, and not insignifi cant, operational and technical challenges could hamper widespread clinical and claims data integration efforts in the near term. However, the advent of new delivery and payment models that link reimbursement with medical outcomes are driving stronger bonds across payers and providers as they collaborate for success under new shared risk models. Big data will play a major role in these payer-provider relationships, becoming the foundation that allows these two diverse groups to respond with equal effectiveness to healthcare s new environment.

Abuse alone accounts for as much as 10 percent of healthcare spending, according to the FBI s Financial Crimes unit.

4 Big data and predictive analytics The combination of big data and sophisticated analytics, such as predictive modeling, offers health plans the greatest chance to effectively detect and prevent improper payments before they go out the door. Payers that try to recover money lost to improper claims lose signifi cant time and resources in the appeal and litigation process. An integrated Multi-payer database can help health plans identify suspect behavior that would simply not be seen in a payer-centric data-set. For example, an algorithm can scan claims from multiple payer sources and calculate how often a certain facility or delivery network bills a certain code. If there are signifi cant outliers, the health plan can then investigate the impact of that outlier on them, whether monetary or in terms of patient safety or quality, and take appropriate actions to limit the damage and prevent it in the future. It s important to note that insurers are fully aware that losses are not always the result of fraudulent, wasteful or abusive behavior. Payers may also fi nd they are overpaying providers because of a contractual error or simply reimbursing more than the appropriate rate for a procedure. Analyzing stores of big data, health plans have a valuable tool for identifying these aberrant payments, whatever the cause, which they can quickly rectify with the provider organization or address in a new contract. Case study For example, by utilizing its trove of big data, one regional health plan was able to identify that treatment costs were increasing by more than 20 percent annually while the volume of its surgical Diagnosis Related Group (DRG) claims remained fl at. With access to more detailed information about these procedures, the payer was able to gain insights into how rising healthcare costs were negatively impacting operations. Utilizing big data, the health plan began modeling expense-reducing scenarios, such as how to move the services to less expensive settings, which will signifi cantly lower care delivery costs without sacrifi cing quality. Adding value to the system After a thorough analysis of the healthcare industry, McKinsey & Company concluded that the effective and creative use of big data could reduce U.S. healthcare spending by $300 billion to $450 billion, or 12 percent to 17 percent of the $2.6 trillion in baseline healthcare costs. Leveraging big data, both in new combinations like clams, clinical and lab data, as well as multi payer data, to more effectively detect and prevent the billions of dollars of improper payments will position health plans to play a major role in unlocking this value.

An integrated Multi-payer database can help health plans identify suspect behavior that would simply not be seen in a payer-centric data-set.

5 About the Author Mr. Stuart has been our Executive Vice President Payer Services since August Mr. Stuart has served in the same position for Emdeon Business Services since March 2006 and previously served as Executive Vice President of Payer and Vendor Strategy for Emdeon Business Services since August Mr. Stuart also served as Senior Vice President of Sales in the Transaction Services Division of WebMD Envoy from July 2002 to February 2005 and in various other capacities with our former parent company since July Mr. Stuart received a Bachelor s degree in Business Administration from Texas State University. Gary Stuart Executive Vice President of Payer Services for Emdeon Simplifying the Business of Healthcare

Stuart has served in the same position for Emdeon Business Services since March 2006 and previously served as Executive Vice President of Payer and Vendor Strategy for Emdeon Business

6 About Emdeon Emdeon is a leading provider of revenue and payment cycle solutions that connect payers, providers and patients to improve healthcare business processes. To learn more about our company, our services and our commitment to improving healthcare, visit EMDEON.6 ( ) or visit Emdeon Business Services LLC. All rights reserved. EMDA rev Lebanon Pike, Suite 1000 Nashville, TN USA 877.EMDEON.6 ( )

To learn more about our company, our services and our commitment to improving healthcare, visit www.emdeon.com/edge. 877.

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