Next-Generation Utilization Management: Managing Utilization and Cost Without Denial of Service​​​

​Utilization management (UM) has been transforming steadily for the past decade. There is a clear shift away from a purely cost-centric, denial of service-based process. A gap exists between provider dissatisfaction with the UM process and the payer’s need to have the UM process to continue controlling fraud, waste, and abuse (FWA). This requires a move toward more exception-based authorization handling using decision support technologies and web-based self-service capabilities to increase automation–all noted as rising trends in UM.

Enhanced forms of UM include more focus on consultative rather than denial-based approaches, a greater focus on quality-of-care improvement downstream from the prior authorization request, network optimization using quality data to direct members to preferred providers as part of a site of service add-on, and value-based arrangements with other providers, who in turn, can forgo the requirement for prior authorization altogether. Payers have begun offering value-based or case rate programs instead of a traditional fee-for-service reimbursement to help reduce the burden of prior authorization on providers. The breadth and depth of innovations in the UM industry warrants a review of the next generation of UM.

Next-generation UM retains traditional utilization controls, and introduces some new ones, while delivering high-value patient care through add-on components beyond prior authorization service. The tenets of this model include: 

• Prior authorization by exception 
• Clinical auto-decision support 
• Utilizing best practice clinical pathways 
• Auto-review to accelerate turn-around times 
• Specialty peer-to-peer review 
• Quality-centric program design 
• Retrospective review and payment integrity 
• Value-driven network and realignment of incentives 

Prior Authorization by Exception 

​Traditional UM processes depend on clinicians to manually review medical records against clinical guidelines, even for approval of the most frequently requested services and procedures. With the availability of intelligent algorithms, technologies, and analytics, it is very possible today to identify the small sub-sect of requests that truly require high-touch review, letting the rest approve more seamlessly and automatically.  

Exception-based review requires identification of opportunities, where intelligent insights can be used to approve requests rapidly while specifically focusing on a review of situations that indicate the potential for FWA. There are 4 key methods to achieve an optimal exception-based prior-authorization process: 

• ​​T​ransparency to guidelines with recommendations – ​providers are given transparent access to appropriate use guidelines, while a real-time decisioning engine provides clear guidance, including recommendation for alternative best procedure. Providers who select the recommended procedure can avoid clinical review altogether, while delivering high-quality, guideline-based care.  
• ​​U​tilization pattern – utilizing technology and business intelligence to detect patterns of use, such as high frequency testing on the same patient, use of the same codes and criteria combinations on multiple patients, adding supplementary codes or unbundling services covered in a bundled code. Only providers who fall into one of these patterns of practice are reviewed by exception. 
• ​​R​isk stratification and predictive analytics – embedded risk-detection algorithms in the guidelines can help approve high-risk patient requests quicker, while focusing more on lower-risk patients receiving frequent follow-up studies as an exception for clinical review. 
• ​​R​apid approvals of pre-qualified providers – providers who have opted away from fee-for-service into value-based or case rate contracts, may be preferentially excluded from prior-authorization. Likewise, using provider practice intelligence, providers who practice mostly compliant to guidelines with few exceptions may be given rapid approvals, significantly reducing their burden.

Adopting any of the above capabilities introduces an exception-based UM process, significantly lowering administrative burden and provider abrasion ​while bringing​ greater focus back to the small percentage of ​requests that​ ​drive the majority​ of inappropriate utilization. 

Clinical Auto-Decision Support 

Procedure-based prior authorizations can often miss the big picture of the care that the patient is receiving. ​​For example, a procedure with 5 appropriate criteria, of which at least 3 must be met to approve a procedure. Each time a procedure request is received, the provider needs only to select the same criteria for the approval, whether or not that procedure is appropriate in the overall care pathway of that patient. Another procedure may have been just as valid – or even better – but was never ‘evaluated’ since it was not requested. Many ​p​ayers who have automated intake of a ​​prior authorization request stop short and only allow online ​portals​ with ​a ​request submission and documentation attachment. Some go a step further, offering a decision based on specific procedure selected, according to select approval criteria. Very few make recommendations based on understanding the full care path a patient ​may be ​on. 

While clinical decision support (CDS) can be implemented and integrated into provider-side electronic medial record (EMR) systems to facilitate appropriate ordering, the same type of systems can be utilized for prior authorization approvals by payers and UM vendors. Clinical pathways drive CDS systems, and more sophisticated CDS systems automatically extract clinical criteria needed to facilitate a decision about the appropriate next steps in a patient’s care path. 

High-fidelity CDS capabilities for UM should include: 

• Automated and computerized intake of authorization request at the time of decision-making, potentially even integrated to a provider EMR/ordering system 
• Condition-specific clinical pathways that dictate the necessity and frequency of procedures based on patient’s predictive and quality-focused treatment plan
• Ability to make recommendations for the most appropriate procedure, even if multiple procedure types are feasible for the patient’s clinical situation​     ​ 
• Ability to extract patient information in advance from multiple data sources to facilitate clinical input and reduce overall time to a decision.

Utilizing Best Practice Clinical Pathways 

Most ​p​ayers believe that they use peer-reviewed, evidence-based studies to construct their UM policies and guidelines. An AHIP survey of commercial payers showed a high reliance on CMS guidelines, payer’s own internal data on the utilization of procedures and drugs, or the vendor’s proprietary guidelines. More than 80% of guidelines used are procedure-specific. 

In contrast, condition-specific clinical guidelines are written as pathways of care for specific diagnoses, such as breast cancer, and they include. At each step, they include a multitude of appropriate procedures that can be performed and knowledge of downstream recommendations given the patient’s diagnosis and initial clinical staging. Peer-to-peer specialist reviewers readily use condition-specific clinical guidelines during UM to determine the best pathway of care for a patient and recommend that pathway using a consultative approach. Instead of denial of service, based on a patient-centric discussion on the overall pathway of care, the ordering provider may self-withdraw a procedure that is not necessary or change the procedure to a better alternative after consultation with a specialist.  

While a procedure-specific guideline may approve a procedure, as long as the procedure criteria are met, each time the procedure is requested, the condition-specific pathway may evaluate the need for a procedure relative to other procedures at each step of a clinical pathway. An example may be where a breast biopsy may be approved each time it is requested based on meeting the appropriate use criteria. Still, a condition pathway may evaluate the need for a breast biopsy against the value of a lumpectomy procedure and recommend the right procedure as best practice. This may avoid unnecessary biopsies, followed by a lumpectomy procedure at a later date anyway.

Auto-Review to Accelerate Turnaround Times 

​​UM ​has historically depended on manual intervention as a means to achieve savings. Only when a case fails the initial guidelines during the case intake that it requires clinical review. Once a case enters clinical review, the requesting provider may abandon the request, be unable to provide the documentation in time, or be unavailable for the peer-review call to discuss the request, resulting in case closure. This is also believed to be an “impact” by the ​p​ayers, resulting in savings and denied cases. However,​ type of impact came about due to burdensome ​processes that​​ may have even delayed needed patient care. 

While a CDS or a rudimentary rules adjudication engine that processes a set of fixed appropriate use guidelines to give a Boolean (yes/no) decision is the “first pass” on an authorization request, an auto-review engine, in contrast, automates the review of even the exceptions themselves. The exceptions are cases that have already failed the initial pass from the CDS or decision engine presented during the intake of a case request. The auto-review technology identifies missing clinical criteria, extracts what is needed to approve a case from the documentation provided, or, if the information is not available, queues the case for rapid review for “lack of information.” Auto-review may also have rules to approve a case based on a provider’s practice pattern or network participation, the patient’s stage of condition or risk, and/or whether a denial will likely be overturned later when additional information is presented.  

The other side of this is when payers and UM vendors develop too many exception rules, which are not transparent and result in loss of automation. The right ​system incorporates​ transparency and a well-balanced set of “clinical exceptions” that can be easily validated with peer-reviewed evidence-based studies, resulting in a highly streamlined and automated review platform.

Specialty Peer-to-Peer Review 

The typical UM process does not use specialty-specific physician reviewers, also known as peer review. UM processes based solely on determining medical necessity typically have a very low denial rate.   

Specialty-specific peer review enhances the quality of review and the overall care provided to the patient since it acts as a second opinion service by a specialist in that field. Convincing the provider to do the right course of testing by changing their procedure request if needed or allowing the provider to self-withdraw a procedure that is not needed, are all options at this stage, as opposed to denial of service.  ​ 

Quality-Centric Program Design 

Bringing a quality-of-care focus into the UM program by shifting focus from managing single procedures to a holistic care management design can greatly impact utilization reduction while improving patient outcomes.   

A quality-centric program design has the added benefit of being included in the 85% ​medical loss ratio (​MLR​)​ rather than the 15% administrative component due ​to the program's​ focus on improving quality of care, transparency, health outcomes, and patient safety. Some methods to develop a quality-centric program include but are not limited to using denial of service only when a procedure request may cause demonstrable patient harm, using decision support and specialty peer review to deliver the right guidance and appropriate procedure selection, developing quality metrics that demonstrate downstream improvement in health outcome measures and monitoring them as part of program performance KPIs, and using both quality and cost factors (value) when directing patients to specific providers within the network.  

Retrospective Review and Payment Integrity 

There is limited time to review an authorization request as physicians are awaiting a decision on their request. Aside from guideline compliance and potential clinical or peer review, there is no additional time to do any other checks. Furthermore, if a claim has yet to occur, it is unclear at the time of an authorization request how a claim will be submitted for a pre-authorized procedure. This is where retrospective payment reviews and integrity checks can further catch patterns of abuse and waste related to utilization management. The appropriate code submitted for the procedure authorized ​is a small​ aspect of this review. Additional review may include checks for adherence to the authorized code, use ​of the correct​ number of units for the primary treatment code, appropriate documentation, and the use of ancillary codes with primary treatment codes.  

Value-Driven Network and Realignment of Incentives 

The health care payment model is fee-for-service, which incentivizes doing more services for payments. Doing less or being watchful for incidental findings translates to lesser revenue for the provider practices and health systems. UM processes provide much-needed data, relevant to determine whether there is an opportunity to provide value-based care and reward providers and practices that are based on established guidelines.  

Data provides the needed insights to evaluate which network providers should be in-network and referred ​to ​more patients. Suppose a specialist provider is practicing according to guidelines, compared to another specialist with a high pattern of overuse of services that do not yield any further improvement in outcomes. In that case, the provider offers value-based care and complies with the guidelines. This insight can help payers optimize their networks and facilitate better overall care for their members.  

By leveraging companies that specialize in a holistic 360-degree UM model, payers and providers stand to benefit from the reduced burden of prior authorization and spend optimization. In contrast, patients stand to gain from timely and high-quality care. 

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