No one likes being told “no,” particularly patients and providers requesting preapproval for a test, a surgery or an extended stay at skilled nursing facility. Yet, utilization management is critical to reducing healthcare costs, as well as ensuring that patients are protected from unnecessary tests or procedures that could potentially do more harm than good.
In many ways, utilization management is like having and enforcing a speed limit. If there were no consequences—no radar gun, no ticket, no fine—people would be less likely to adhere to the posted rate of speed, potentially putting themselves and other drivers at risk.
Utilization reviews and the entire preapproval process set speed limits for providers and facilities; ensuring that patients have the right care, at the right time, in the right place, based upon whether or not the request meets nationally recognized clinical guidelines.
The challenge is, for some plans, preapprovals are highly manual, which adds expense, slows response, and increases the margin of error—with little visibility into provider behavior. By utilizing data analytics, automation and digital technologies, plans can reduce costs and improve quality in a number of ways:
Automate and Streamline Utilization Management Workflow
If the utilization review process is primarily human-driven, plans can utilize natural language processing (NLP), machine learning and a variety of digital tools to streamline and automate the process. For example, instead of human review, plans can feed preapproval data into a rules engine that includes coverage determination policies, claims logic, approved clinical standards and misuse triggers to automatically vet these submissions. These engines can also assess providers from a behavioral analytics perspective to more quickly spot anomalies, like claims that are particularly high for a procedure or type of physician or specialist.
Additionally, NLP technology can create significant efficiencies by processing unstructured, multipage authorization requests for service and extracting the data fields required for clinical review—time-consuming work typically performed manually by intake teams.
If there’s an anomaly or questionable request, that request is sent to a human for review and provider follow up.
Understand Provider Patterns to Drive Behavioral Change
Because physicians are generally the ones ordering the procedures, drugs, tests and elective hospitalizations, part of preapproval process involves a peer-to-peer review between plan and prescriber when a request is denied. In some cases, the request may be valid, but, the physician didn’t provide all of the clinical information required to determine an affirmative response. In other cases, that physician might require more education on what the clinical criteria, or accepted procedures, might be.
For example, if a physician requests an MRI of a patient’s back, the plan need to know why—is it to justify surgery, order a new intervention or simply find out more about that patient’s pain? If it’s a surgical request, many plans need evidence that the physician has tried more conservative options, like physical therapy and medications, and failed, before exploring a surgical option.
By using the collective provider data, plans can quickly identify which physicians require extra coaching, as well as those physicians who have a pattern of prescribing treatments that fall outside standard medical practices, and take remedial action.
Automate Acceptance of Specific Provider Requests
While data can be used to remedy non-compliant provider behavior, it can also be used to reward those physicians and facilities with a solid track record of doing things right.
Some health plans use their data to identify which physicians and facilities consistently follow preapproval protocol and rarely have denials, and give them ‘gold card’ status, which exempts them from the standard preauthorization process. Requests submitted by gold carded providers are automatically approved, saving a significant amount of time and resources.
This carrot approach may also incent other providers into compliance, as well.
Finally, plans can use their collected data to analyze their own preapproval patterns.
For example, there may be specific tests, procedures or medications that are approved 99.99 percent of the time, like CT scans, or hearing aids for Medicare Advantage patients. Instead of spending time and resources going through the prior authorization process, plans can eliminate the preapproval requirement on these requests, if they fall within a certain dollar threshold.
As a result, plans reduce costs, providers save time, and patients get the procedure or equipment they need more quickly.
It all starts with using the data you have to understand provider behavior, preauthorization patterns and applying digital technologies to create an environment of continual optimization.
To learn more about how EXL can help your organization effectively use and analyze the data you have, and automate workflows to reduce costs and improve outcomes, contact us.
Dr. Victor Collymore,
Chief Medical Officer