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Using decisioning to build the healthcare payment of the future

I saw this report from McKinsey - Overhauling the US health care payment system (subscription required). Their summary has three key points:

  • The hugely inefficient US health care payment system is ripe for transformation.
  • The inefficiency is concentrated in the $250 billion that consumers pay doctors and hospitals and the $1.3 trillion that insurers send to these providers. The heart of the problem is a mix of high transaction-processing costs and the lack of an efficient way to make consumer-to-provider payments.
  • Over the next five years, rapid innovation may lead to a restructuring of the value chain of health care payments and to a shift in the sector’s balance of power. Financial institutions have an opportunity to take on a more prominent role, while payers risk losing influence to new entrants. Providers stand to benefit as fewer dollars are wasted on transaction-processing inefficiencies.

What if the healthcare system operated with more business fundamentals in place where both consumers and sellers, in this case physicians or hospitals, had full knowledge about the cost?  Today, given the choice of a cosmetic surgeon or general practitioner stitching a gash on your child’s face in an emergency situation, who would you choose?  Given the assurance of same results, i.e., no scarring or increased infection rate, that decision might well be based on cost.  But providers don’t talk about cost because they’re not quite sure.  It’s all too complex, and depends on multiple factors, including the contracted insurance company and type of benefit plan.  So without this information, you make the best decision and hope for the best. Imagine running any other kind of business in a similar fashion.   As the paper points out there are a couple of key areas - claims and uncompensated costs - where a more efficient system is required.

  • In claims one seeks to pay only the claims that should be paid.  The scale of problem can be seen from an audit by the Office of Inspector General of the U.S. Department of Health and Human Services of Medicare. Of the $191.8 billion such claims paid in 2001, $12.1 billion – should not have been paid due (www.NHCAA.org). Even if this is correctly identified later, the payers are stuck in a "pay and chase" mode, trying to recover the money paid. What if you could accurately and automatically ensure payment of claims that ought to be paid, and able to detect problems stemming from fraud, abuse and errors – prior to payment?
  • Uncompensated healthcare costs in 2005 were $28.8 billion, or 1.5 percent of the 1.9 trillion spent on healthcare in the US . As the paper points out, much higher rates than in other businesses. What if you could derive a correct initial payment amount, based on likelihood to pay in response to a requested amount, estimate settlement amount and suggest payment plan during admission or registration?

Add to this the need for more information to support consumer-directed healthcare and ever increasing pressures on costs and you have a system in urgent need of solutions. Something can be done, however, by automating and improving operational decisions, the intelligent use of business rules and predictive analytics and a focus on customers.

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Enterprise Decision Management or EDM is perfect for addressing these issues. Remember, EDM uses business rules, predictive analytics and adaptive control to automate, improve and connect decisions:

  • Business Rules - healthcare is a heavily regulated system at both the State and Federal level. Not only are business rules an effective way to implement regulations, they allow for systems to be changed rapidly and cheaply every time a new regulation, or a court ruling, changes the rules.
  • Predictive Analytics - detecting fraud is really important in the healthcare space with claims fraud representing billions of dollars. Neural Nets, along with other predictive models, are effective at detecting known and emerging patterns of fraud and powerful in detecting problems prior to payment. Connected decisions, such as linking claims payment decisions clinical decisions, can provide greater visibility into the consumers’ overall healthcare landscape toward greater quality of care as well as prevention of unnecessary claims payment with those made at check-in or service delivery, for instance, are also very visible in healthcare and the ability to connect analytics across decisions is important. As others have said, predictive analytics are a must for payers.
  • Adaptive Control - nothing in the healthcare space stays still. For example, fraudsters are always adapting and trying new things, different collections and debt recovery strategies need to be tried and compared. Adaptive control is essential in building a robust payment infrastructure.

Enterprise decision management may not be able to fix the healthcare payment system on its own, but it sure provides the critical backbone you need. To illustrate, let's think about how the healthcare payment process might look at a very high level if everyone involved was using EDM. 

  • A patient checks in at a hospital for surgery. The hospital's check-in process identifies the expected services, verifies insurance coverage, confirms the agreed upon or list price for the service, and estimates what the patient will have to pay. Payment is collected from the patient
  • If the patient is uninsured the check-in process is similar to the "instant credit" process at a store. Small amounts of verifiable information are collected and used to identify patients who should be asked to pay up front, those who can be billed, those whose credit card is good for the money and so on. Those who cannot pay are immediately identified and charity/other applications started to cover them.
  • Differences between the expected services and actual services are billed after the fact in a similar way - tthe exact amount owed by the patient is billed to the patient, and the rest to the various payers involved.he exact amount that will actually be owed by the patient is billed to them, the rest to the various payers involved.
  • Payers decide which claims are fraudulent before paying them, using automated decisions, allowing them to pay within the legally allowed time limits while not risking "pay and chase" where claims are paid and then found to be fraudulent
  • Consumers are able to access these same decisions online to help them find the true cost, to them, of different options and providers enabling them to become empowered consumers.

Each of these decisions requires decision service to deliver the right decision across the various systems and channels.

Thanks to Teri Tassara for her insight on this. If you are interested in the topic, you might like a paper I wrote for Montgomery Research on the use of technology to eliminate healthcare fraud (Teri and I blogged on this topic also) and this post on using EDM to improve hosptial billing processes.

 

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