I was rounding a bend running at a charity race when I felt a pop in my calf. Instead of crossing the finish line, I ended up in an orthopedist’s office. The doctor ordered an MRI, which didn’t detect anything worrisome, but several weeks later, with my leg healed and me back to running, I got a bill for the MRI. The damage: $2,500. My insurance company had decided that an MRI was overzealous, given my symptoms. This is the dark side of utilization management (UM), where patients are left footing the bill.
Of course, there are many dark sides to UM in its present form. Providers resent needing to seek approval from payers for decisions they have already made, and payers don’t relish their role as guardians of cost and medical necessity. It’s adversarial and exceedingly expensive, generating billions in administrative costs.
But some form of UM is necessary to keep the nation’s medical bill from spiraling out of control, not to mention for avoiding unnecessary and unproven tests and procedures. It’s just designed all wrong, especially as we enter the new value-based world, where providers and payers need to communicate, be transparent and make shared decisions.
What I propose is a UM bridge from the traditional fee-for-service model, in which communication happens after care decisions have been made, to a value-based system, in which communication happens at the point of decision making. This bridge can occur through sophisticated cloud-based healthcare technology.
We can use the existing, albeit flawed, pre-authorization model as the framework on which to build this bridge. The model is vastly different from the UM we all know and find troublesome, yet it pragmatically uses the industry’s established UM process as a “jumping off” point. It’s there, providing at least a basic form of communication between payers and providers, so let’s use it, but improve it
Here’s how it works in a nutshell (see my white paper, Mastering Change: Succeeding in Healthcare’s New World Order: A Bridge to Value-Based Decision Support, for a more in-depth discussion). I envision a collaborative, exception-based approach that shifts interactions and decisions from post-care to point-of-care.
The model drives communication via a shared healthcare cloud around evidence-based practices and appropriate care at the point of decision. A patient is entered into the system by the provider, automatically triggering the relevant information needed to deliver cost effective, evidence-based care. Providers have access to data about the patient’s insurance benefits, provider network, and evidence based algorithms used by payers.
Rather than a painstaking manual process of authorization, the system generates questions for the clinician or digitally queries the clinical record. Providers receive clinical guidance based on widely accepted medical evidence as well as other relevant coverage information. Some events will garner immediate approval, others may go through further automated review and the remaining minority would be manually reviewed. This approval is based on a provider’s practice patterns and the patient’s health plan coverage
In this model, payers for care can see which providers are submitting authorizations and getting approvals for which care events, when and how often. Based on that data, a payer can dial up or dial down the care events that need an authorization transparently with the provider.
As data on provider practices and health systems accumulates, the benefit goes both ways as some providers may need only a notification, avoiding a lengthy medical review of an authorization. The more the provider practices in line with evidence and policy, the fewer authorizations needed, the lower the administrative burden and the more immediate the approval.
The provider will know if care events are covered, what the appropriate medical and network policies are, and whether they require a deeper review or simply a notification as they are making their decisions and before the care is delivered.
For this to work optimally, it should occur across care events, across payers, with one common workflow, rather than using multiple payer systems. The cloud-based platforms available today can enable this type of model. Payers will need to measure utilization data and manage utilization patterns in order to build this nuanced exception-based approach. The data will facilitate the approval of requests that are aligned with quality and cost objectives, and identify outliers, that can then work with the plan to help improve decision making.
For instance, if a plan sees discrepancies in imaging orders between two different networks in the same region, it can then drill down, looking at the claims of the higher volume network to determine why utilization rates are so high. Together the plan and the provider can develop a shared understanding of the causes of those exceptions and the best approaches for improving performance in line with evidence for those procedures. This can power new shared savings and value based reimbursement models based on quality and outcomes as well.
In this model, payers and providers can collaboratively measure, manage, and refine high-quality care delivery while reducing administrative costs—and do so with a common shared vision that balances all stakeholders’ concerns.
In the instance of my leg injury, the physician would have gotten an alert that the MRI would not receive an immediate approval, would be sent for further review and how much it would cost. That might have prompted the doctor to consider another imaging technique or perhaps take more of a wait and see approach, saving me $2,500 and a lot of lost time making phone calls to my doctor and health plan.
The opportunity to bridge fee-for-service to a value-based utilization management system is here. Let’s consider it. I believe those who do will want to take the next step and put it into place, because the financial and care benefits become obvious.
Matthew Zubiller is vice president of Strategy and Business Development for McKesson