Reinventing Utilization Management to Bring Value to the Point of Care

Reinventing Utilization Management to Bring Value to the Point of Care

If you would like a version of this article suitable for printing, click this image to download the PDF

How an automated exception-based approach can make UM more efficient and effective

By Nilo Mehrabian

How can health systems deliver the right care, at the right cost, in the right setting, without overwhelming delivery and reimbursement systems with administrative burden?

The shift from volume to value-based care requires the deft combination of value-based delivery (enabled through actionable intelligence and new care delivery models) and value-based payment (enabled through select provider networks and new reimbursement models).

Providers and payers must operate across a transparent, administratively simple, shared ecosystem. This giant leap from today’s world in which healthcare stakeholders currently operate might appear impossible. However, as providers take on greater accountability for cost and share more risk with payers, there is a real urgency for change.

The good news: the technological capabilities needed to affect change are available today. What’s missing: an effective bridge between the current volume-based systems, in which communication between providers and payers happens after the care decision (with the limited exception of pre-authorization), and a value-based system, in which rich data and enhanced intelligence are automatically shared in real-time to inform decision making.

Such a bridge can be built by starting with the current, albeit flawed, pre-authorization model as a foundation, enhancing current core systems and investments rather than trying to completely rebuild healthcare. The result is a new form of utilization management (UM) that shifts the balance of interactions from post-care decision with claims to pre-care decisions.

This can be done through multiple layers of seamless automation that use existing medical information systems (i.e., EHRs, care management portals, etc.) to minimize or even eliminate routine administrative tasks, and empower providers and payers to focus manual medical necessity and authorization review efforts only on cases that require their clinical expertise.

This exception-based approach increases the value of review and authorization processes by adding evidence-based decision support to their roles. By driving communication around evidence-based practices and appropriate care at the point of decision, the industry starts to bring value-based care delivery and, ultimately, value-based payment together.

Solving this practical challenge will foster genuine collaboration between payers and providers based on a shared priority to ensure that quality care for value is delivered, while significantly reducing their administrative burdens.
This exception-based approach increases the value of review and authorization processes by adding evidence-based decision support to their roles. By driving communication around evidence-based practices and appropriate care at the point of decision, the industry starts to bring value-based care delivery and, ultimately, value-based payment together.

The Limits of Traditional Utilization Management

The shift to value-based care would seem so logical and promising that nearly all stakeholders would want to support it. The sobering reality is that current payer-provider relationships, their technological systems, and their organizational infrastructures are only just now becoming ready to accommodate the transition.

In the new world of value, payers and providers should be able to collaborate effortlessly at the point of care. A patient entered into the system by the provider should automatically trigger the relevant data, processes, and tools needed to deliver cost-effective, evidence-based quality care.

Providers and payers should know the patient’s relevant care history, which approaches and treatments are supported by the evidence, and whether those are included in the patient’s insurance benefits package and provider network. The payment of care should be administered under the appropriate reimbursement model, and providers should have shared access to the data and actionable intelligence needed to deliver the right care in the right setting.

All additional points along the continuum of care would also be appropriately informed and coordinated seamlessly. This is decidedly not the case in the traditional volume-based healthcare system. Pre-authorization and admission review are limited and often flawed examples of opportunities for payers and providers to interact and determine the clinical and financial impacts of care.

Under the current approach to UM, providers must seek approval from payers for care through a cumbersome, manual, and often retrospective process. It begins with the medical necessity review, an administrative task typically performed by highly skilled clinical staff. They manually extract clinical data from disparate sources and systems and enter it into their UM system to complete the medical review. The medical review then accompanies an authorization request, which is nearly always submitted to the payer through fax, phone, or email.

Once the payer receives the review, they usually conduct yet another review using the same clinical information that was attached to the authorization request, creating redundant, duplicative work. This manual process can take between two days and two weeks before the provider has approval to proceed.

In some cases, care already has been decided and delivered, before the authorization requests are submitted. This puts payers in the position of serving as guardians of cost, medical necessity, network utilization, and reimbursement rules. Not surprisingly, payers and providers view this relationship as adversarial and the traditional utilization management function as a burdensome but necessary evil, fraught with the potential for conflict, rather than an opportunity for collaboration. This approach to care oversight adds to the administrative and workflow burden incurred by all parties and impedes the timely delivery of appropriate care for the patient. According to a study from the American Medical Association¹:

    • About 64% of physicians reported difficulty determining which tests, procedures and drugs require authorizations.
    • About 63% of physicians reported waiting several days for authorization responses on tests and procedures, while 13% waited more than a week.
    • Nearly all of responding physicians reported that eliminating authorization hassles was “very important” (78%) or “important” (17%).

Additional studies found each preauthorization costs payers and providers between $50 and $100, adding to the $74 billion annually²  in administrative costs to payers, and increasing the estimated $31 billion in annual administrative costs burdening providers (roughly $68,274 per physician)³.

Traditional utilization management also fails to deliver rich data on provider utilization patterns and network performance that can be shared between payer and provider. This hinders the development of improved policies, high performing networks, and effective, targeted provider interventions.

The approach to determining what needs to be authorized or what requires medical review is more art than science, and ends up creating more hassle and expense than value. Moreover, the coverage, network, and complex reimbursement rules that could factor into clinicians’ decisions are not incorporated into the process in which the care is being determined.

The shift from volume- to value-based care is forcing those dynamics to change. The emphasis on quality and cost-effectiveness is leading to new delivery and more sophisticated payment models that shift some degree of financial risk from payers to providers. Accordingly, accountability for the clinical and financial impact of care decisions is now assumed by both parties, and to an extent, the patient.

While the complexity and uncertainty created by this transition is significant, the opportunities created by reform are promising. It is estimated, for example, that enhanced collaboration can lead to a significant reduction in the $800 billion lost to administrative inefficiencies, provider inefficiencies and error, medically unnecessary and duplicative care, unwarranted use, and overutilization, fraud and abuse4.

Shifting to a Collaborative Exception-Based Model

Although traditional utilization management tends to be universally disliked, if transformed into a new collaborative model, it could serve as a bridge to the future.

Utilization management already drives the collection and aggregation of clinical and financial performance data before care has been delivered, although it is generally not available for analyses until long afterward when claims and care management data have been retrieved and reported. That said, it is precisely this aggregation of clinical data that provides an opening for collaboration and for enhancing decision support in advance of care.

While the administrative burden of conducting a manual review is high, it is possible to automate the authorization process by integrating it into both the payer’s and provider’s workflows. This would lead to a significant reduction of the manual work involved in following up on authorization requests, as well as helping to eliminate redundant medical reviews.

In this scenario, immediate approval could be generated based on medical review results coupled with the payer’s business rules. Automated decisions could also take into account data on provider utilization patterns and network performance.

This process could be further streamlined by programmatically extracting data directly from the EHR to automatically populate the medical review. Querying the clinical record directly in this way brings obvious advantages to the UM process, reducing the administrative work required for the medical review, and reducing human errors introduced when clinicians manually copy information between multiple systems.

Significantly, from the payer’s perspective, automating this process increases the trustworthiness of the review because the clinical data came directly from the EHR—the source of truth—without human intervention. And when the automated process transfers the clinical values into the medical review, that additional transparency further enhances trust.

This automatically generated review becomes the input to the automated authorization process described above, which increases efficiency, and speeds the process. This is not like the traditional gold-carding process, where providers avoid an authorization altogether. The medical costs and utilization patterns associated with such an approach to authorization are generally higher because they remove the ability to measure and monitor provider utilization. Gold carding just stops watching.

Over time we’ve seen requirements for authorization wax and wane, and we firmly believe the cycle of on-again, off-again utilization management must stop. Today, utilization management is coming back into vogue, with significant increases by payers in the care events for which they require authorization. The percentage of medical claims reporting prior authorization increased on average by 2.3% from 2011 to 2013, with some payers doubling and tripling the number of care events that require authorization5.

Instead, using shared, cloud-based technology, payers can see which provider is 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 which care events need an authorization and how much data is required. For those authorizations that are not immediately approved at the point of care via cloud technology, the payer can simply approve submissions that need further review immediately, without additional burden.

In the bridge to value-based decision support, an appropriate balance between reducing medical costs and managing administrative costs must be achieved to stop this cycle. Figure 1, below, illustrates the range of options available for choosing the right intervention.

At the extreme ends of the spectrum, a payer can choose to reject or approve all care, which is just not practical. Today, in the case of authorizations, the only option generally available to payers is manual review. That’s not good enough.

Using shared cloud-based technology and a new approach to utilization management, the options are smarter and less intrusive depending on the pattern of utilization data collected. For example, a manual review may be automated for approval when the payer allows providers to supply their medical review by interacting with an automated clinical algorithm rather than manual submission.

The speed of approval can be further increased and the administrative burden lessened if the payer instead chooses to require a notification for that provider, avoiding a lengthy medical review of an authorization, but using the very same process. In such a case, instead of detailed questions about the clinical scenario, approval could be simply granted based on information about diagnosis, type of care event, provider, and setting.

The ability to configure these options based on the utilization patterns of that provider, or by care event, is key to developing a collaborative win-win relationship between a payer and its providers. The more the provider practices in line with evidence and policy, the lower the administrative burden.

Both the payer and provider can see this practice data—the provider can demonstrate proficiency, and the payer can monitor and incentivize provider participation without the burden of a manual discussion. In the process, the provider will know automatically if care events are covered, what the appropriate medical and network polices are, and whether they require a deep manual review or simply a notification as they are making their decisions and before the care is delivered.

An even more efficient system can be achieved if the payer’s system, using shared cloud-based technology, can interact directly with the provider’s electronic health records (EHRs) behind the scenes, observing clinical data and utilization patterns and only requiring further review when exceptions to evidence-based care approaches have been identified.

To drive optimal provider adoption, this must be done across care events—diagnostics, procedures, specialty drugs, DME, etc.—and across their various payers, bringing a familiar, common workflow to the user, which is much more attractive than having to go to multiple systems for multiple payers. This is possible today with a common, cloud-based platform that enables exception-based UM and provides decision support.

For providers, this exception-based approach is ideal. Not only is the administrative burden eliminated, but the moments of intervention are also minimized. Providers want decision support only when it is really needed and provides them value. If they were being interrupted at every stage of care delivery, they would quickly get annoyed and fatigued by being forced to focus more on the administrative process than the patient.

For payers who don’t follow this path or don’t manage utilization, the danger remains that inappropriate utilization rates will start to increase over time without oversight and intervention. In the case of value-based relationships such as shared savings, providers are asking their payers for help with this type of technology.

The ability, therefore, to collect and analyze outcome data in order to refine the utilization management process becomes critical to the success of the shift to value.

Optimizing Utilization Processes with Analytics

How does a payer continue to manage authorizations that are generally approved the majority of the time, without intervening in the care delivery process excessively? The data generated by automation must be gathered in a cloud-based shared ecosystem, measured and smartly managed by exception.

In this way, the payer intervenes only to the degree necessary. If the provider is delivering appropriate, evidence-based care, then the burden of scrutiny should be minimized and it should be easier for the provider to deliver and be reimbursed for care. That is the formula for a collaborative payer-provider relationship and one needed for value-based care.

To understand when interventions can be minimized, payers must measure and manage utilization patterns, while refining policies and processes.

  • Measure: Measuring utilization data requires examining it in aggregate as part of an overall trend, rather than in terms of individual authorizations. Doing so makes apparent which requests are being automatically approved and which are automatically reviewed or canceled, and how frequently such interventions happen. This can be done based on the plan, product, provider, patient, or care event.
  • Manage: Drilling down, payers can compare utilization patterns of different networks and providers, and observe variations in care events and procedures. Accordingly, the health plan can identify outliers where requests are higher volume compared to the peer-set norm, in or out of network, or not in line with evidence-based approaches. In addition, it can also see when requests are routine and do not warrant additional scrutiny that would waste administrative resources. Based on this data analysis, the health plan can build a nuanced, exception-based approach by refining and optimizing its rules of authorization to facilitate the approval of requests that are aligned with quality and cost objectives while triggering notification in the system to scrutinize requests that are outside of their set parameters.
  • Refine: Once a system is in place to automate routine requests and signal alerts about outliers, the health plan and the provider can work together to understand the root causes of the outliers and intervene as appropriate. Ultimately, this collaboration can result in improved performance for the system from both administrative and medical cost perspectives that benefit the provider and the payer alike. This can also serve as key performance data for value-based relationships between them.

As an example of measure, manage, and refine, imagine that a health plan measures data for utilization patterns related to imaging orders and sees clear discrepancies between two different networks in the same region. In one network, the utilization patterns are fairly consistent and the costs are within established norms, while in the other network, utilization is erratic and costly.

After comparing with similar member-base characteristics, the health plan decides to manage those trends by requiring manual reviews for authorizations in the higher-volume network, while only relying on notifications for authorization in the lower-volume network.

Drilling down, the health plan then looks more closely at the imaging claims made in the high-volume network and determines that they are predominantly for ultrasounds conducted in one particular health system. Accordingly, it moves to a notification approach for requests for imaging except for ultrasounds, and works more closely with the health system in question to determine why utilization rates for those ultrasounds are so high and costly.

This can be examined at the individual provider level to see if greater education or intervention is needed for that provider. Together, the health 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.

Another scenario: a patient confers with a specialist about the need to get a hip replacement. The specialist obtains authorization for that surgical procedure through an automated system. Since the specialist and the hospital where the surgery will be performed are in the same network, that authorization is also automatically processed by the hospital’s registration department.

This lets the hospital use the same authorization number on their claim for the surgery when submitting to the health plan for reimbursement. In this situation, the specialist and the hospital are effectively working in tandem to make the administrative process easier.

There is value in this collaboration for the payer as well. Since the specialist and the hospital are both using evidence-based procedures in line with the payer’s policy, they are approved without review. Moreover, it is easier for the payer to process the claim because it is able to automatically gather all of the information involved in the episode, from the visit with the specialist to the surgical procedure and beyond.

Finally, consider a payer measuring the number of inpatient admissions for a particular provider. With data obtained directly from the EHR system, the payer looks at how frequently the provider refers patients to admission compared to his or her peers and whether chart reviews can be reduced.

Drilling down on the number of admissions that result in readmissions, the payer explores the possibility of enabling automated reviews versus manual reviews and whether continued stays should be automated as well for providers that demonstrate a high quality of care.

This level of analysis not only reveals new ways to improve performance and quality, but also reduces the need for case managers to manually investigate patterns and propose solutions. The payer can also explore the performance of the health system in question and assess outcomes for combined services to develop an even more effective approach to quality improvement.

These practical examples illustrate how technology to automate decision support offered by the payer for financial issues becomes more relevant to the provider while also supporting better clinical decisions. In addition, alignment between the payer and the provider is being effectively reinforced.

The more providers can align their care practices with the benefits and policies of the payer, the more providers will avoid the administrative burden of utilization management and be better able to demonstrate their value to a payer’s narrow network.

Performance-Driven Collaboration

In a value-based system, we are striving toward collaboration. How can exception-based utilization management be implemented, and what are the benefits of this model for both parties?

In a traditional utilization management system, compliance rather than collaboration is the rule. The retrospective nature of claims data means it might take months to access data around procedures that have already occurred and additional months to analyze such data. Typically, the analysis is used more often as the basis for investigation into possible waste and abuse rather than as a collaborative opportunity to seek performance improvement in utilization rates based on evidence.

In an exception-based utilization management decision support system, payers and providers communicate about care delivery in near real-time and learn to develop a more nuanced understanding of utilization patterns and variations in care. After identifying any outlier trends, the payer and provider can determine the root causes of such discrepancies.

Such analysis produces actionable intelligence that puts providers and payers on the same page, with a shared set of rules accessible at the point of care, as decisions are being made. Over time, payer rules and actionable content can be infused into the many points of decision that are being made by the provider. This will help determine the appropriateness of medical care while also reducing administrative burdens. Most significantly, it creates a traversable pathway to a value-based care system.

This approach engages providers and payers with a common language. They are using a shared technology to measure, manage and refine quality care delivery in line with coverage policy. It reduces barriers internally, and between payers and providers, so that the various functions can communicate across traditional silos. Eventually, this also opens the door to integrate shared rules into the provider’s workflow.

This is an essential bridge from volume to value. When utilization patterns and the benefits of improved performance are shared openly, the provider can organize its care delivery to drive value, and the health plan can incent or support such efforts by paying for value. Over time, payers will direct more care to the best performing providers, as defined by their ability to meet quality and cost goals in accordance with evidence. The best providers will work to increasingly align their practices to meet the payer’s definition of value.

Making the Vision Real

A transformative system is well within reach. It starts with the technology tools and platforms being developed today, and the collaborative ecosystem forming among the network of payers, providers, and vendors across the healthcare space.

The launch of InterQual Connect™ in May 2016 was a major step in the path to exception-based UM, combining InterQual® Criteria and authorization connectivity in a SaaS (Software as a Service) solution that’s integrated into payer and provider workflows.

By fully automating the authorization process, redundancy—where both payers and providers perform the same medical reviews—can be eliminated, and authorizations can be provided without needing to manually handle the request. This helps reduce administrative costs, speed authorizations, and help ensure appropriate care.

But what about the medical review? To fully realize exception-based UM, both the medical review and the authorization process must be automated and exception-based. Until recently, this wasn’t possible due to technological limitations. But today more than 75% of hospitals use an EHR6, and much of the data required to conduct a medical review is housed there.

InterQual AutoReview™, planned for an initial release in 2017, capitalizes on this by automating the InterQual medical review with data extracted from the provider’s EHR. A SaaS solution, it’s integrated into leading EHR systems and will transmit directly to the provider’s UM application.

From there, the authorization request along with the medical review can be transmitted to a payer’s care management system with InterQual Connect, where it can be automatically authorized using the combination of business and clinical rules.

Exception-based UM is now a reality, and the elimination of the adversarial relationship between payers and providers heralds better days ahead for payers, providers, and patients.

NOTE: If you would like a version of this article suitable for printing, download the PDF now.

¹    American Medical Association. AMA Survey of Physicians on Preauthorization Requirements (May 2010)

²    McKinsey & Company, Preauthorization sizing, Change Healthcare report (2008)

³    Health Affairs, What Does it Cost Physician Practices to Interact with Health Plans? (July/August 2009)\

4    Thomson Reuters report, http://www.reuters.com/article/usa-healthcare-waste-idUSN2516799520091026 (October 2009)

5    Medical Economics, Curing the prior authorization headache (October 2013)

6    American Hospital Association, Study: 75% of hospitals have at least a basic EHR (November 2015)