What is decisioning?
Decisioning is becoming a major buzzword in the prior authorization process. Decisioning automates prior authorization by making determinations using machine learning and artificial intelligence combined with widely accepted evidence-based clinical criteria.
The real question is can the concept live up to the hype? More than 80% of the time, prior authorizations are eventually approved. Getting to the inevitable “yes” faster is where decisioning comes in. Recent advancements in decisioning technology have accelerated auto-determination, so 80 to 90% of prior authorization requests can now be instantly approved.
Member and provider satisfaction are both poised to increase when most authorization requests are auto-determined. There are also administrative savings as a result of the following:
- Less missing information outreach
- Lower clinical review volume
- Faster, more consistent nurse reviews (when a manual review is necessary)
Now that we have established what decisioning means, let’s go through five common misconceptions about it.
Myth #1: Decisions have to be based on certain third-party policies to use decisioning technology.
Of course, third-party policies can be automated. However, when a plan has invested substantial resources into creating a custom policy, that policy should not have to be discarded just because of technology limitations.
Decisioning technology is now available that can be configured to any policy.
Myth #2: There is no way to know why a prior authorization request is denied.
The lack of transparency in the prior authorization process is frustrating for providers and patients. This void is often referred to as a “black box.” That’s the 1.0 version of prior authorization technology. Providers and patients shouldn’t have to wait until an authorization request reaches a registered nurse to realize a simple piece of information is missing that could have already resulted in an auto-determined request and faster patient care.
Transparent real-time documentation detection and user nudging proactively prevent missing information.
Myth #3: Nurses must start from scratch to determine the response to a prior authorization request.
No one ever says, “I wish I could do more menial work.” It’s in everyone’s best interest to have nurses working at the top of their licenses. Rules powered by patient clinical and admin data (things like claims look back, ML note extraction/interpretation) remove the need for nurses to start from scratch with each prior authorization request.
Pre-processed authorization cases accelerate nurse reviews.
Myth #4: Prior authorization relies on easily gamified clinical assessment questions (CAQs).
Press “1” for this option. Press “2” for another. No one wants to wade through monotonous questions to get what they need. When prior authorization relies on clinical assessment questions, it can cause provider abrasion. But clinical assessment questions are also notoriously easy to gamify, eroding any benefit that the prior authorization process is designed to provide.
Decisioning rules based on patient clinical and admin data create a new standard of quality care to show that prior authorization doesn’t need to rely on clinical assessment questions.
Myth #5: Prior authorization decisioning technology can not catch gamification.
The legacy prior authorization model wasn’t built for robust analytics capabilities. As discussed in Myth #4, questions are susceptible to gaming and inaccurate information input. Routine audits and rigorous analytics enable decisioning transparency and rule performance enhancement.
Gamification is difficult with rigorous quality analysis that enables decisioning transparency and enhancement.
How does decisioning work?
Decisioning almost seems too good to be true without understanding how it works.
Completeness Scan
Completeness scan checks for prior authorization requirements and missing info. The user receives nudges in real-time to include appropriate documentation, which reduces the back-and-forth.
Lack of guideline transparency can frustrate providers and soon will be contrary to regulatory requirements. The completeness scan automates guideline selection and provides guideline transparency.
Automated Clinical Review
The automated clinical review (where appropriate) guides indication-level clinical review. This process drives auto-determination and provides analytics on the process and decision quality, substantially reducing the need for manual review and speeding up the time to care
Pended Review Pre-Processing
Despite the power of automated decisioning to reduce the manual review burden, a fraction of requests must still pend for UM nurse review. For pended requests, pended review pre-processing automatically prepares documentation for clinical review, saving clinical review nurses significant time and effort. It ensures authorization requests are clean and complete when they arrive at the desks of nurses and physicians for manual review. The innovative use of machine learning adds unique value by drawing the reviewer’s attention to the most relevant information in the documentation.
Discover how to employ technology to accelerate prior authorization decisions by 70%.
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About the Author: Cohere Health
