“The Great Pause” Brings Unyielding Uncertainty to Medical Loss Ratios but Also Opportunity

“The Great Pause” Brings Unyielding Uncertainty to Medical Loss Ratios but Also Opportunity

Are better-than-expected medical loss ratios in 2020 setting up payers to take a severe hit on profit margins in 2021? Go on offense by making moves that feature these 4 risk-mitigating hallmarks.

Six months into the novel coronavirus pandemic in the U.S., the crisis has affected the healthcare industry unevenly. While providers have been hit hard financially, health plans have experienced record profits due to a significant drop in medical expenses. But while they come under scrutiny for the current medical loss ratios, health plans argue this is a situation that cannot last. They expect a pent-up demand for healthcare coming in 2021 that will more than make up for this year’s health plan profit margins.  

None of us can predict the future. But we can still find a path forward for payers. One that helps you successfully navigate the short-term uncertainty around healthcare claims costs and poises you for long-term success. The “great pause” – as the initial lockdown period of 2020 is sometimes referred to – has changed our economy and the way health plans think about business operations for the long term.  

Will COVID-19 Delay the Payer Financial Impact? 

The Affordable Care Act mandated health plans spend at a minimum 80-85% of premiums on healthcare or quality improvements. The result is a medical loss ratio (MLR) calculated as follows: 

MLR = Healthcare Claims + Quality Improvement Expenses / Premiums – Taxes, Licensing & Regulatory Fees  

With the onset of the COVID-19 pandemic, as consumers avoided or deferred care, the “healthcare claims” portion of that equation shifted dramatically. This downward trend was most significant in the second quarter. A recent study found that in April 2020, total hospital admissions declined 34%, including COVID admissions. At the same time, visits to ambulatory practices declined 60%, and emergency room visits dropped off 42%.  

This lower-than-expected use of healthcare services has reduced medical expenses billions of dollars across commercial health plans leading to, in some cases, doubling of profits. But, what does the future bring?  

The “bad” 

Especially at the onset of the pandemic, several experts predicted catastrophic healthcare costs would accompany COVID-19 and severely hit payers’ bottom lines. That worst-case scenario hasn’t materialized, though some worry that higher costs are on the horizon as a result of early care delays. 

 Of particular concern are decreased rates of screenings, vaccinations and other preventive care. At least 40% of individuals surveyed reported delaying care due to COVID-19 through the end of June. This article cites studies that note a 46% decline in cancer diagnoses and an 85% drop in common cancer screenings. CMS data revealed 22% fewer vaccinations administered, 44% fewer screenings for physical and cognitive development, and 69% fewer dental procedures among children between March and May of 2020.   

 A predicted future spike in elective procedures is also worrisome. By some estimates, they make up 37% of health plans’ hospital admissions spending. Some of those 40% mentioned above who delayed care undoubtedly fall into this category. As of July, though, procedures had rebounded to 16% below baseline levels. And a September survey found 60% of patients are open to rescheduling their elective procedures this year, a figure that jumps to 71% among those whose needs are more urgent. 

 The “good” 

When COVID-19 forced consumers and providers to re-think healthcare encounters, it yielded a few moves that may lower costs in the short-term and the long-term: 

 Embrace of telehealth and virtual care: The adoption of remote patient monitoring, virtual visits and more has jumped forward and may help better manage chronic conditions, including behavioral health. By improving access to care, it might also help redirect the non-emergent visits that have historically ended up in the emergency department. As a PwC report noted, “Even if telehealth increases utilization, many payers see the platform as an opportunity to get members the right care at the right time in the right place while also saving the member and the employer money.” 

Creative healthcare solutions: From at-home dialysis and other home health treatments to reassessing the usual cancer treatment protocols, the hyper-focus on effectiveness has started to chip away at the “more equals better” healthcare idea.  

Alternative payment models: Those providers participating in contracts that prioritize value fared better financially than their fee-for-service counterparts – and achieve improved outcomes for their patients. Combined with modern payer initiatives that focus on social determinants of health, coordinated care, population health and the transparent data sharing that supports these moves, the current environment might signal a sea change in this shift. 

The “wait and see” 

Because we are still very much in the middle of this healthcare crisis, these outcomes – the “bad” and the “good” – are far from a foregone conclusion. In fact, the latest estimates – from the Willis Towers Watson actuarial analysis of employer healthcare cost – project 2020 totals to come in 3.3 – 9.9% lower and 0.5 – 5% higher in 2021 for a combined cost reduction of 2.8 – 3.8% from non-pandemic levels. Even still, the analysts warn about volatility around these costs. 

With this uncertainty comes risk for health plans. Uncertainty around when and where elective procedures will resume, the total COVID-19 testing and treatment costs, and how ongoing unemployment will affect insurance coverage rates all make it difficult to act with conviction. In response to this lack of assuredness in the market, some health plans have gambled on suspending long-planned strategic projects.   

However, while the novel coronavirus is our collective reality for the time being, it will pass eventually. With that in mind, does it make sense to make relatively short-term moves that may prove costly in the long run? 

4 Hallmarks of Win-Win Strategic Moves for Payers to Implement Now 

We might feel the impact of COVID-19 on healthcare through 2022. And especially in the face of potential healthcare cost volatility in 2021, health plans have to extend their long-term visioning. What strategic moves both mitigate risk now and move the needle on your competitive advantage in the future?  

Consider decisions that make sense, no matter what the future brings regarding medical loss ratios. Look for these four characteristics of moves that promise to propel your health plan past the risk-averse reactive mode that can result in inaction and unwanted setbacks. 

1. Easy to implement

When you think of plans that are easy to implement, policies and processes that are already in place may come to mind. For instance, when CMS provided payers flexibility on medical loss ratio reporting and rebates this year, health plans didn’t hesitate to exercise their options. Many had already taken it on themselves to provide direct support to consumers. This relief came in the form of waiving COVID-19 cost sharing, extending premium payment grace periods, and offering premium rebates and discounts.  

By the same turn, if your health plan is already in the process of pursuing an enterprise technology platform, consider how quickly you could realize savings from that decision. And how difficult it would be to get back on track if you abandoned that process. Emphasize your potential speed-to-value when you encounter these roadblocks, and it won’t steer you wrong.

2. Build empathy with stakeholders

As COVID-19 ravages your consumers, network providers and employer clients, any strategic move that shores up these relationships holds promise for long-term benefit. For example, extending telehealth benefits and continuing member engagement campaigns for consumer satisfaction. Evolving provider engagement programs to further ease their claims burden. And offering creative solutions to keep employer healthcare costs down. 

Some of these strategies build on the positive outcomes realized thus far into the pandemic, which also make them easy to implement. And they include policies that mitigate healthcare delays and may even lower healthcare costs, making them a good match for today’s challenges and tomorrow’s opportunities. 

3. Improve efficiency

No matter how healthcare costs impact medical loss ratios next year and the next, it pales in comparison to one perennial challenge. Administrative complexity makes up at least 10% of annual healthcare spending in the U.S. Reducing your administrative burden holds the greatest potential to increasing your profit margins.  

For that reason, prioritize decisions that will yield the greatest return on investment. Times like these increase the stakes greatly. And you simply can’t afford not to invest in solutions that could double or triple your recoveries while improving efficiency.

4. Reduce team frustrations

What issues did the pandemic bring to the forefront for your team’s day-to-day? For most of us, work flexibility and communication have become more important than ever. Work cultures that depend largely on face-to-face interactions have had to adapt quickly to maintain productivity. And your email inboxes may have taken a hit as their limitations as workflow tools have been tested. 

As a result, teams across all industries have quickly adopted virtual meeting solutions that may already have been in place. You may also consider secure, cloud-based collaborative technology platforms. Especially ones developed specifically for the healthcare industry can help you extend a work-from-anywhere posture and invite key external stakeholders into the workflow.  

Set Yourself Up for Success – No Matter What Medical Loss Ratios Bring 

While there continues to be talk about “getting back to normal,” we may have to recognize that the new normal equals uncertainty. We have to become agile enough to succeed under those circumstances. Take this opportunity to implement solutions that address inefficient processes. Innovate your way through broken relationships with stakeholders. A scalable technology platform like Pareo can help you transform your payment integrity function, which better positions you to successfully navigate unpredictable medical loss ratios and their impact on profit margins. 

 

NOW'S THE TIME FOR TOTAL PAYMENT INTEGRITY

Talk to ClarisHealth about how Pareo®, a total payment integrity platform, is driving innovation at health plans. 

The Strategy Nearly Every Health Plan Considers: Outsource vs. Insource

The Strategy Nearly Every Health Plan Considers: Outsource vs. Insource

Slim margins, fewer resources, and consolidation have made it harder for health plans to compete. Could balancing outsource vs insource efforts accelerate their goals? 

With shrinking margins, fewer and fewer internal experts, and increasing consolidation, health plans may find it harder than ever to compete.  These limitations give rise to unique challenges, however unique solutions have emerged to overcome even the most insurmountable hurdlesTo ensure your health plan thrives in the current healthcare climate, you may have considered the relative benefits of outsource vs. insource strategies in their potential to transform your payment integrity results.

Health Plans Face Challenges with Scaling 

We hear regularly how legacy technology, “less is more” mentalities and lack of resources are top of mind for many health plans. The enormity of these struggles seemed to accelerate alongside the influx of millions of newly insured Americans that accompanied the passing of the Affordable Care Act. In its wake, many health plans discovered their largely manual processes could not scale to meet the growing concern around improper payments and wasteful healthcare spending 

Time has shown that the pace of change hasn’t stopped since. Even before the COVID-19 crisis hit, a stream of regulations continued to overwhelm the limited resources at most health plans. Compliance with the information blocking rules, for instance, can distract from other high-value activities – no matter the potential long-term benefits of both initiativesIncreased consolidation makes it more difficult to compete, while heightening the necessity to do so. Perhaps now more than ever, lack of resources continues to be a major barrier for many health plans.  

Altogether, the writing is on the wall: the future of healthcare requires claims processing modernization, data aggregation, information security and the ability to lead the transition to alternative payment models and gains in population health improvements – all with great urgency, says Healthcare FinanceHealth plans understand these directives and acutely feel the need to catch up, but inadequate technology, staffing shortages, competing capital projects, and combinations of these and other factors hold them back from progressing at the desired rate. 

 

Weighing Outsource vs. Insource 

Whether it’s transitioning more efforts prepay, going beyond compliance to proactively address FWA, or streamlining workflows to reduce administrative complexity, scalable processes that hold the potential to transform results are accessible to all payers – not just heavily-resourced health plans. But what offers the best path to maximizing your health plan’s cost containment goals? Let’s explore outsourcing vs. insourcing your payment integrity efforts. 

How does payer outsourcing work? 

Payer outsourcing involves a health plan contracting with a third-party vendor for claims processing and other functions. In this model, an outside group of focused experts perform what otherwise would be an “overhead expense” in the form of technology investiture and staffing/resources. As the pressure to optimize has increased, payer outsourcing has expanded beyond business process outsourcing (BPO) to include innovative technology and real-time resources that don’t require an up-front capital investment.  

Health plans are increasingly outsourcing some or all parts of their payment integrity program, including FWA, coordination of benefits, itemized bill review and more – for prepay and post-pay. Just look at Oscar Health, a startup health insurance company that made news relying on an insourced/outsourced model (along with advanced technology) to provide a more efficient healthcare experience.  

“The end goal is to process claims efficiently so doctors spend less time hunting down payments for the care they already gave, and reduce errors so consumers never have to deal with denied claims or paying for services they never received,” writes Oscar Health. 

Impressively, if your health plan is significantly under-resourced, an outsource to insource model can help you achieve serious traction in revenue gains without hiring additional personnel, adding expertise or immediately acquiring technology on your ownIt’s a strategy that many innovative startups rely on, and one that also works well for health plans at all stages of maturity.  

How can I effectively insource? 

Insourcing in this case, simply put, involves conducting payment integrity processes with your health plan’s own resources. If your health plan has pursued an internalization strategy in the past without much success or with limited gains, you aren’t alone. Legacy business models that rely largely on manual processes hinder innovation. But acquiring an advanced technology platform designed specifically for health plans can help you scale your internal resources. 

Starting with foundational functionality that addresses your most pressing need first ensures ROI, smooths user adoption and makes the most of limited resources. Dramatically reducing manual processes and achieving mutual value with suppliers allow you to reduce your administrative overhead and tackle payment integrity in the most cost-effective manner. 

Especially if you can acquire technology that works out-of-the-box for your needs and accommodates configurability without development, you can accelerate your speed to value. No matter your motivations, the potential for doubling or tripling your recoveries creates a solid case for insourcing. 

“Under-resourced health plans face unique challenges but benefit from unique solutions that transcend the outsource vs. insource argument. “

The Smart Solution: Start Wherever You Are  

Now that we have taken some time to weigh the outsource vs. insource argument, you might start to realize that elements of both strategies hold potential for your health plan. And that’s not surprising. Even the big national plans only started formalizing centralized payment integrity functions 10 years ago, so there’s still time to catch up and multiple ways to get there. 

The vast majority of our clients find their path lies down a hybrid model of outsource-to-insource or pursuing both concurrently in an optimized combination of the two. For instance, you may find it easier to internalize data mining with the right enabling technology that helps you streamline workflows and realize efficiencies. On the other hand, your health plan may decide to always outsource complex medical records reviews because of the specialized resources it requires.  

Statistics back up our experience with this hybrid approach to payment integrity; claims management – including payer services and product development – holds the largest market share of healthcare BPO. Fortunately, no matter your choice, the first step is the same. “For the same reason you outsource claims processing, find a trusted partner with expertise and advanced technology to ease your payment integrity burden,” says Jason Medlin, vice president of strategy and marketing at ClarisHealth 

A partner and a platform that allows you the flexibility to decide – service by service – whether to outsource or insource based on your cost-benefit analysis will better poise your health plan to scale effectively. Contingency-based relationships, like we use at ClarisHealth, are helpful for under-resourced health plans because they don’t require a large capital investment, which makes payment integrity outsourcing far more turn-key and affordable than you may realize. It also is worth noting that many plans will find it beneficial to move quickly on technology implementation as a way to speed overall time to value. 

 

Get a No-Risk Proof of Concept for Pareo   

At ClarisHealth, we don’t believe the outcome of the outsource vs. insource debate is necessarily binary. Rather, health plans can strategically outsource and insource select payment integrity efforts based on current resources while making a plan to adjust that mix over time to attain crucial internalization goals. We offer flexible delivery models, including an outsource to insource path, to meet health plans where they are.  

Talk to ClarisHealth about Pareo®, our comprehensive payment integrity solution, which makes for a seamless transition to an optimized blend of outsourced and insourced payment integrity.  

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Talk to ClarisHealth about how Pareo® can transform your health plan’s payment integrity operations.

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You Don’t Need Rules to Mitigate Healthcare FWA

You Don’t Need Rules to Mitigate Healthcare FWA

Health plan SIUs find deep learning models for fraud detection provide advantages over rules-based tools.   The onset of the novel coronavirus pandemic highlighted a glaring weakness in rules-based FWA systems: the need to develop rules well ahead of their actual use...

You Don’t Need Rules to Mitigate Healthcare FWA

You Don’t Need Rules to Mitigate Healthcare FWA

Health plan SIUs find deep learning models for fraud detection provide advantages over rules-based tools.  

The onset of the novel coronavirus pandemic highlighted a glaring weakness in rules-based FWA systems: the need to develop rules well ahead of their actual use in order to attempt to catch coronavirus-related FWA in a timely manner. At best, these rules are educated guesses at schemes that health plans are only beginning to pursue. But, as we’ve mentioned before, a fraud detection solution powered by deep learning eliminates the need for specific rules to be created. Even during an unprecedented pandemic, an A.I.-based solution natively surfaces aberrances that indicate fraud, waste and abuse. While the benefits of that change are significant, it’s also a huge mindset shift. How does fraud detection without rules even work?   

But first, let’s discuss why rules-based fraud detection is no longer the ideal tool for the job, especially in the current environment of stressed providers and increasingly complex novel fraud schemes.   

Why Not Rules?

If your health plan has been working to mitigate fraud, waste and abuse for any length of time, you likely have invested a great deal of resources in a rules-based system. But the novel coronavirus pandemic has exacerbated some concerns around relying solely on this traditional approach to fraud detection. 

Continued telehealth

From the beginning of the pandemic-created healthcare crisis, there have been concerns that the rapid adoption of telehealth would usher in a wave of fraud designed to exploit the relaxed rules that made its expansion possible. While that outcome remains to be seen, virtual care encounters certainly go against the conventions of in-person provider visits that fraud rules have been structured for. Legitimate providers conducting majority telehealth visits could easily trigger rules designed to catch outliers in place of service, impossible days and numbers of visits. 

increase medical savings clarishealth

False positives risk provider abrasion

How are you distinguishing legitimate providers whose billing patterns have changed for good reason over the last few months from a few fraudulent opportunists? With the fallout from COVID-19 battering providers, health plans have been understandably reluctant to overly burden their providers with aggressive pursuit of overpayments.  

Unfortunately, traditional rules-based systems – even those updated with coronavirus-specific queries – are prone to creating false positives that overwork investigators and risk provider abrasion. Providers have already raised the alarm that COVID-19 diagnosis codes don’t accurately reflect some of the more severe complications of the diagnosis, which could worsen the false positive rate of rules-based tools that can’t evaluate multiple approaches to the data simultaneously and fare poorly with unstructured clinical information. 

Novel fraud schemes

As we presented in our short on-demand webinar on the topic, a unique healthcare crisis like this one can absolutely give rise to bad actors gaming the system for personal gain. But traditional rules-based fraud detection tools are primarily designed to look for fraud schemes you already know about. As a result, the SIU could easily miss novel fraud schemes featuring more subtle aberrances that nonetheless add up over time. The success of these more sophisticated approaches depends on the assumption that health plans have limited their fraud strategy to the same tools in use for decades. 

Rules vs. Deep Learning Models

If rules-based tools are increasingly outmatched by the growing complexity of healthcare data and fraud schemes, what kind of solution features the flexibility and advanced technology to help health plans meet their FWA goals? In a previous article, we covered just how deep learning is uniquely suited to overcoming many of the common challenges faced by the SIU. But, how does fraud detection without rules operate? 

To help illustrate how fraud detection differs in a rules-based environment and with a deep learning model, let’s explore a typical COVID-19 claim you might encounter in the SIU. 

A 55-year old patient calls his Internal Medicine provider with complaints of chest congestion and a sore throat. The patient has underlying bronchial asthma and sees his Allergist for this condition once per quarter. The Internal Medicine provider schedules a telehealth visit and, based on his findings from the visit, prescribes a full lab workup for COVID-19. The payer is billed the following:  

  • Dx:  COVID-19 
  • Office visit 99214 
  • Lab codes 87635, 86328, 86769 

How does this new claim make it through the SIU to be evaluated for potential fraud? 

COVID-19 scenario: typical rules-based environment

If the SIU works primarily with a rules-based system, it’s possible the tool has been updated with a query for Level 4 office visits with a lab panel based on a diagnosis of COVID-19. Otherwise, the rules will need to be updated accordingly with the help of the IT or analytics team. Then, the SIU would query other Internal Medicine providers billing the same combination and manually compare rates and paid amounts looking for clusters of outlier claims. 

These findings may lead the SIU to additional queries to increase the likelihood of understanding if there is fraud or not: 

  • Query patient for other visits?  
  • Query Internal Medicine provider for past 6 months on COVID-19? 
  • Is this a trend for this provider or a one-off? 
  • Is the provider’s concentration of COVID-19 diagnoses higher because of his geography? 

COVID-19 scenario: deep learning model

On the other hand, if the SIU is working with a fraud detection solution powered by deep learning models, much of the work related to historical claims and peer-to-peer provider comparison information has already been accounted for in the system. In this scenario, the patient’s history of asthma, peer provider comparison based on block of dates, spikes vs. trends, geography, patient acuity and more are present in the models, which continuously update as new information comes in. 

From there, the deep learning models in the system push likely provider fraud leads to the SIU. With Pareo Fraud: Detection, the billing provider could be highlighted with a high Super ScoreTM based on high model scoring for Unusual Active Days, Ratio of Px/Codes and High Claim Volume. The investigator could then one-click drill down to the provider for more supporting detail on these scores and optionally conduct additional peer-to-peer comparison. 

This workflow supports the investigator’s expertise while greatly reducing the manual effort and eliminating the need to “pull” unprioritized query results from the system. 

Benefits of Deep Learning Models for Fraud Detection 

Deep learning models for fraud detection allow the SIU to go beyond the Boolean, if-then logic of rules-based tools. Freed from these restrictions, investigators are better able to discern the true potential fraud offenders and realize several differentiators that offer unique value: 

  • Eliminates need for multiple queries and manually layering those results 
  • Continuously learns and evolves so that  the more investigators work with it, the more precise it becomes 
  • Adds specificity to results to reduce false positives and bring focus to providers with high likelihood of fraudulent or abusive billing practices – including the amount of dollars at stake 
  • Digs deeper and wider into the data in multiple dimensions to find the subtle changes in provider behavior that may indicate FWA 
  • Extends capabilities of identifying relationships in the data to find hidden, previously unknown schemes 

Pareo Supports the SIU in Transitioning to Fraud Detection Powered by Deep Learning 

The benefits provided by deep learning models go a long way towards your compliance goals, including FWA prevention. But making a move like this, along with its associated mindset shift, can also feel like a gamble. The perceived risks associated with upgrading your FWA solution are largely centered around the discomfort of change for users and concerns about wasted resources. But conserving resources – time, effort and expertise – is exactly what a deep learning-powered fraud detection solution helps accomplish.   

And, for those SIU teams that have invested heavily in a rules-based tool and value its reliability and familiarity, that effort is not lost. As an integrative platform, Pareo allows you to easily connect this resource as another useful data input that Pareo Fraud: Detection can leverage to improve the precision of results more quickly. 

Finally, as the only solution for the SIU that seamlessly integrates overall payment integrity efforts, Pareo Fraud can help you quickly covert detection results to leads or cases, communicate relevant information back-and-forth with the audit function, and refer honest billing mistakes for provider education. This ability to maximize recoveries and avoidance at the most optimized cost end-to-end is unique to Pareo and ensures this solution proves valuable for all stakeholders – both internal resources and network providers. 

THE FUTURE OF FWA IS NOW

Learn more about the ClarisHealth 360-degree solution for total payment integrity and FWA, Pareo Fraud: Case Management and Detection.

ClarisHealth Launches A.I. Powered Healthcare Fraud Detection Solution for Health Plans

ClarisHealth Launches A.I. Powered Healthcare Fraud Detection Solution for Health Plans

NASHVILLE, TENN. (PRWEB) SEPTEMBER 09, 2020 – – ClarisHealth, the company transforming health plan operations with its comprehensive payment integrity technology platform Pareo®, launched today an artificial intelligence-based (A.I.) solution for healthcare fraud detection. Pareo Fraud Detection works seamlessly with Pareo Fraud Case Management for an integrated, 360-degree approach to mitigating fraud, waste and abuse (FWA).

According to Mark Isbitts, Vice President of Program Integrity for ClarisHealth, who led the project, Pareo Fraud Detection provides significant advantages over conventional tools that have been in use for decades. This traditional approach involves sifting through massive amounts of data and defining thousands of rules to detect fraud, which limits its scope of effectiveness.

“Currently, the SIU deals with overwhelming numbers of false positives and manual processes and still may miss some aspects of fraudulent activity and new emerging criminal schemes,” says Isbitts. “Pareo Fraud Detection is based on our proven payment integrity technology platform and features a transparent and efficient user interface that provides a view of why a provider is marked as fraud and enables investigators to visualize different patterns and scenarios in just a few clicks – without inputting multiple rules. ”

Pareo Fraud Detection models were developed in partnership with data science experts from the College of Business Administration at the University of Illinois-Chicago (UIC). Kyle Cheek, PhD., the Director of the Center for Applied Analytics and a clinical associate professor of Information and Decision Sciences at the university, has worked in various payer fraud organizations. He helped ensure the solution escaped a fundamental drawback of A.I.: lack of explainability.

“An A.I.-based technique is not usually transparent about how it generates its outputs and is often referred to as the ‘black box,’” says Cheek. “However, Pareo Fraud Detection harnesses the power of deep learning applications of A.I. informed by domain expertise so the SIU will get information on which providers to investigate first, why a provider is suspicious, and how valuable the investigation can be – in real-time, efficiently, and with limited manual efforts.”

The release of Pareo Fraud Detection continues the ClarisHealth strategic direction of expanding the native functionality of the Pareo platform to maximize health plan cost avoidance and recoveries at the most optimized cost. According to ClarisHealth CEO Jeff McNeese, this approach is termed Total Payment Integrity™ and it transforms engagement with internal and external stakeholders.

“Just like there had been little innovation in payment integrity before Pareo, the same has been true for FWA solutions,” says McNeese. “Being able to solve payment integrity and FWA challenges end-to-end – from prepay to post-pay – on a single platform fulfills the Total Payment Integrity promise of Pareo.”

About ClarisHealth

ClarisHealth is the answer to the health plan industry’s siloed solutions and traditional models for identification and overpayment recovery services. ClarisHealth provides health plans and payers with total visibility into payment integrity operations through its proprietary advanced cost containment technology platform Pareo®. Pareo enables health plans to maximize avoidance and recoveries at the most optimized cost for a 10x return on their software investment. For more information please visit  https://www.clarishealth.com.

View source version at: https://www.prweb.com/releases/clarishealth_launches_a_i_powered_healthcare_fraud_detection_solution_for_health_plans/prweb17373183.htm

Source: ClarisHealth

3 Ways MCOs Can Prevent Fraud, Waste and Abuse

3 Ways MCOs Can Prevent Fraud, Waste and Abuse

A robust fraud, waste and abuse program at MCOs and MAOs includes three key areas: Technology, Clinical Audit and Investigative capability.

As your health plan grows its fraud, waste and abuse initiatives, there are three areas a strong prevention plan should address. Rather than relying on piecemeal components to combat Medicare and Medicaid fraud, as many health plans do, a comprehensive approach is best practice.

To truly be successful with a fraud, waste and abuse program, you must have three key pieces of the puzzle in place: Technology, Clinical Audits and Investigative capabilities. With all three of these tactics in place, health plans are more likely to increase their recoveries as a percentage of total claim spend. Pareo®, a total payment integrity solution, supports each of these fraud, waste and abuse capabilities.

Risks of Overlooking Common Types of Fraud, Waste and Abuse

Fraud, waste and abuse are three classifications of improper payments, which is a payment made or received in error in a government healthcare assistance program (like Medicare and Medicaid). Improper payments are more broadly combated by a comprehensive payment integrity and FWA program, as simply addressing fraud, waste or abuse alone will still miss some areas of fraudulent or wasteful activity.

According to this article, fraud, waste and abuse investigators typically focus on “two general areas: corruption and asset misappropriation.” They do this by analyzing the large amounts of big data generated by healthcare transactions. But are MCOs and MAOs doing enough in this area?

A recent report found that Medicaid insurer efforts to root out fraud, waste and abuse were disappointing. Major oversights found in their report include:

  • Failure to report offending providers to the state (allowing them to defraud other Medicaid insurers)
  • Failure to recover millions of dollars in overpayments, which could lead to increases in Medicaid rates that are based on fraudulent numbers 

Meridith Seife, a co-author of the report, said, “We are concerned anytime we see evidence that managed-care organizations are not [finding fraud and abuse and sharing it with states] in a rigorous way.” 

And a 2020 GAO watchdog report found that at least 63% of MAOs encounter data is missing national provider identifiers, data essential to tracking provider ordering, prescribing and billing habits. As the report states, “Both CMS and OIG rely on NPIs for ordering providers to conduct oversight and pursue fraud investigations.” 

As stewards of taxpayer dollars, MCOs and MAOs have a duty to thoroughly combat fraud, waste and abuse. To more effectively manage FWA in your organization, you need to be sure your solution has the following three key components:  

1. Technology 

According to CMS, Medicare improper payment rates have been steadily decreasing while Medicaid’s have been rising. The robust fraud initiatives in the Medicare program have been given the credit for the progress there, and their strategy was recently updated with the aim of further improving the effectiveness through 5 pillars:

  1. Stopping bad actors
  2. Preventing fraud
  3. Mitigating emerging programmatic risks 
  4. Reducing provider burden
  5. Leveraging new technology 

Steps to ensure proper payments have been taken in the Medicaid program as well. In 2018, under the third pillar of its reform initiative – integrity and accountability – CMS announced audits of state programs to review Medicaid enrollee eligibility as well as if programs are correctly reporting medical loss ratios. It also promised to leverage increased data sharing and analytics and step up provider education of proper billing practices. In 2019, it released a new rule intended to prevent known fraudulent providers from billing government insurer programs. 

The continued focus of CMS on eliminating Medicaid and Medicare fraud, waste and abuse means that health plans need to properly utilize technology to gain transparency into their process. Health plans and managed care organizations have to connect large volumes of data in order to comply with fraud, waste and abuse regulations. Advanced technolog– especially a solution that leverages applications of A.I. like deep learning – can integrate and process large amounts of data and to identify anomalies and patterns more effectively than people can do alone.

Technology-enabled fraud, waste and abuse solutions can quickly turn things around for MCOs – especially if they are plugged into a larger, more integrative payment integrity system. Documentation, risk identification, referrals, audit preparation and reporting are all capabilities that a robust fraud, waste and abuse technology solution can provide to MCOs and health plans.

 2. Clinical Audits 

The second element a preventive fraud, waste and abuse program needs to have is the ability to perform clinical audits. This gives MCOs and other payers and health plans the ability to review claims that have been flagged as potential fraud, waste or abuse cases. Clinical audits determine if diagnoses, prescriptions, encounters, procedures, and more are worthy of further investigation or not. An internal audit program is beneficial to a health plan, as it can often be used to prevent improper payments from occurring in the first place. 

While retrospective provider audits can unnecessarily stress the payer-provider relationship, that doesn’t have to be the case. By connecting real-time data between providers and payers, streamlining the medical records request process, seamlessly coordinating with vendors to prevent overlap, and transitioning more efforts prepay – all supported by Pareo – you can work to mitigate provider abrasion while satisfying compliance requirements.

Notably, the ability to analyze claims data and use predictive modeling allows a health payer, plan or MCO to effectively safeguard against fraud, waste and abuse. Tech-enabled clinical audits, like those performed within Pareo by a services vendor, can either complement or supplement the internal efforts of a health payer as well as those performed by Recovery Audit Contractors (RAC) for outlier billers. However, RAC audits have reduced significantly in recent years as payers move away from fee-for-service arrangements.

 3. Investigative Capability 

Increasingly, health plans, payers and MCOs should arm their FWA programs with a strong investigative arm in order to protect against non-compliance. If fraud is suspected, investigative capabilities allow direct reporting of fraud schemes to the appropriate authorities, providing evidence that supports (and protects) health payers and taxpayer funds. Should a claim be taken to court, both the evidence and a documented FWA process within a health insurance organization prove invaluable.

The amount of big data collected by healthcare organizations presents incredible opportunities to those invested in fraud, waste and abuse prevention. Governmental agencies are now using big data to investigate and prosecute FWA offenders. Mike Cohen, an operations officer with the OIG’s Office of Investigations, explains that “data…creates a pyramid effect, and we can go to the top of that pyramid.” And as fraud schemes grow increasingly sophisticated, the evidence data must evolve along with it. 

Pareo® supports investigative activities across a healthcare organization’s data ecosystem, and ClarisHealth staffs a team of expert-level investigators, a benefit which also supports cyber threat intelligence efforts. As with clinical auditing, the investigative component of a fraud, waste and abuse program can be  partially or completely outsourced to ClarisHealth.

Request a Fraud, Waste and Abuse Presentation 

Does your organization have what it takes to effectively prevent Medicaid and Medicare fraud, waste and abuse? Find out by requesting an FWA presentation from ClarisHealth, where a member of our team will discuss your specific needs.

Learn more about the ClarisHealth 360-degree solution for payment integrity and FWA, Pareo Fraud: Case Management and Detection powered by A.I. here.

NOW'S THE TIME FOR TOTAL PAYMENT INTEGRITY

Talk to ClarisHealth about how Pareo®, a total payment integrity platform, is driving innovation at health plans. 

Deep Learning and You: The Answer to the Complexities of Healthcare Fraud Detection

Deep Learning and You: The Answer to the Complexities of Healthcare Fraud Detection

When your health plan looks for an A.I.-powered solution for the SIU, here are 4 reasons deep learning should top your list. 

If your health plan is looking to improve your fraud detection capabilities – by reducing false positives, finding more missed opportunities or reducing manual processes – you’re probably considering adopting more advanced technology. When exploring your options, no doubt you’ve found mentions of “artificial intelligence” in this area intriguing. We’ve written before about how you can determine if these claims are valid or not, and while we have provided some insights on what different applications of A.I. may offer to healthcare, we want to get more specific about the ideal match for fraud detection: deep learning. 

So, what is deep learning, and why is it particularly suited to the complexities inherent in predicting instances of potential fraud?  

The Confusion Around A.I.

With the significant challenges facing the SIU, it makes sense that progressive health plan fraud organizations would be interested in emerging technologies that harness the power of A.I. But the exaggeration of the technology in pop culture leads to many misperceptions of what it is and how it is used. We tend to think of A.I. as a sort of a ubiquitous solution to a wide variety of problems without a lot of thought to how it works.  

Instead, our focus should pivot to the underlying methods that drive A.I. This wide variety of methods handles decision-making problems – primarily classification and prediction. And no application of A.I. is better at handling the complex situations presented by healthcare fraud than deep learning. 

What is Deep Learning?

Deep learning is a subset of A.I.-based technology methods. It relies on advanced technologies like machine learning, neural networks and supervised/unsupervised methods to process structured and unstructured data in a way that mimics human intelligence. Deep learning methods are used for detecting financial fraud, predicting healthcare costs and outcomes, and for smart home devices translating the signal that is your voice into recognizable inputs.  

We are not yet at the point where we can ask a voice assistant, “Is this claim fraud?” But the methods that allow the device to recognize the questions we ask are some of the very same methods that we can apply to fraud, waste and abuse detection in the highly complex healthcare data space. Though, at this point, it still requires a fair amount of subject matter expertise to accomplish that effectively (more on that below). 

Deep learning for healthcare fraud detection: peer-to-peer provider comparisons

Fraud detection models utilize raw claims, audit data, and demographics of providers and patients. The models use many derived features that involve peer-to-peer comparison of providers: 

  • Overutilization or misuse of services 
  • Provider share of volume and claim dollars in their peer neighborhood 
  • Provider share of patient costs 
  • Discrepancies between the diagnosis and the treatment 
  • Patient and provider geography 
  • Recency of billing to the payer 

The Complexity of Healthcare Fraud Detection Meets Its Match 

As we think about the FWA detection analytics problem – and the benefits that this broad class of deep learning methods and technology bring to that problem – it may be helpful to contrast healthcare FWA against other industries that have had success with the application of deep learning analytics to the detection problem. As you might recall from our previous article on the topic, the comparison that most often comes to mind is credit card fraud, despite a huge difference between the two: complexity.  

Challenge: Very little structured, historical claims data labeled “fraud” 

Even if financial institutions didn’t detect credit card fraud at the moment of the transaction, the fraudulent activity would still be found upon consumer evaluation of the monthly statement. That confirmation data, along with the exponentially larger numbers of credit card transactions vs. healthcare claims, provides financial institutions with reams of structured, labeled historical data to train fraud models. 

On the other hand, even upon close inspection of EOBs provided to consumers, these documents lack the specificity and understandability to root out healthcare fraud at this level. This difference in the historical data leads to the fundamental problem with advanced analytics, which is the problem of making correct classifications. How accurately can you identify fraud as a fraud, and a non-fraud as a non-fraud? 

Solution: Deep learning can handle vast amounts of complex, unlabeled, unstructured data 

While some information useful for fraud detection comes in the form of relatively structured claims, most data must be mined from free text clinical information. It’s incredibly inefficient for investigators to read through pages of information and reference several applications while looking for the needle in the haystack that is potential fraud. One of the best use cases for deep learning is taking unstructured clinical documents (notes, transcripts) and converting it to structured concepts with actionable information.  

Moreover, the unsupervised deep learning acts in a way human expertise cannot. We know there’s probably fraud in the data, yet we’re not sure what it might look like when we find it. Which leaves us in a position of being unable to say, “I’ll know it when I see it.” But rather, “I’ll say and do it after I know that I’ve seen it.” Making these connections is what deep learning excels at, and it performs even better at this task with massive amounts of relevant data – beyond what limited historical claims information can provide. 

Challenge: False positives are expensive to handle

Even with enviable amounts of structured, labeled data, credit card fraud detection still averages a 90% false positive rate that no health plan could reasonably accommodate. But, with financial institutions’ largely hands-off approach to resolution, they are easily able to keep their costs low.  

SIU teams are far too under-resourced to sift through that many red herrings in search of legitimate fraud. Plus, in the effort to prove or disprove a fraud lead, the investigator likely will need to request additional information from the provider at some point – and risk unwanted abrasion in the process. Health plans understandably want to maintain these valuable relationships, and the vast majority of providers submit legitimate claims with the best of intentions, so false positives are a business risk for healthcare more so than other industries. 

Solution: Deep learning processes data in multiple layers and dimensions to fine tune results 

To offset the risk of false positives, healthcare fraud detection solutions must go beyond the “outlier analytics” version of deep learning that financial institutions can leverage. It’s a tradeoff between sensitivity (catching more potential fraud) and specificity (catching only very specific fraud). 

We don’t have a way to directly measure the accuracy of the analytics that we apply in unsupervised learning methods, so we have to use some indirect measures to make sure that the algorithms that we apply are accurate enough to not exacerbate the cost considerations of false positives. Deep learning is able to transform the space of the underlying data and go beyond provider anomalies through multiple layers of processing. As a result, you can better balance between false positives and false negatives. 

Challenge: Impractical to develop rules for all possible scenarios 

If your SIU has been working to mitigate fraud for any length of time, you likely have a trusty rules-based system at your disposal. These systems usually come pre-populated with a number of fraud-focused rules or concepts that are run against incoming batches of claims. You may also have a data science organization – or access to resources shared with IT or other areas of payment integrity – that work to augment this native functionality with your own proprietary rules. 

This competency is foundational technology essential to any healthcare fraud organization. But the process has its limitations – especially if your data science resources are limited. Theoretically, we can build as many rules as there are examples of fraud, waste, and abuse – but at a certain point, that becomes impractical. Additionally, FWA schemes tend to stay a step ahead of the rules we build, despite our best efforts. The unfortunate result of an analytics-only system is one that can only surface known schemes.  

Solution: Deep learning evolves to “get smarter” over time without explicit programming 

To the extent that we can build a base of rules is always useful, but advanced analytics based on applications of A.I. are what modern fraud data science organizations need to accelerate their capabilities. Fraud detection methods based on deep learning can, for instance, scrutinize links between multiple providers to see if an unethical or illegal activity associated with one provider may also be practiced by another. These advanced analytics can also explore demographic, geographic and other data about members and providers to double check or justify claims that may raise a red flag – all without being explicitly programmed to do so.  

Deep learning fraud detection solutions are also not limited to specific points in time. Historical information augments and informs the new data arriving and updating daily to get smarter over time. As a result, you do not need to update models every time new information comes in to identify short-term spikes and longer, more subtle trends in the data. This is where advanced technology excels for the SIU.  

Challenge: Healthcare fraud too complex to eliminate the human element 

Like all areas of health plan cost containment, fraud organizations are being charged with doing more with less. But with the high stakes of a fraud investigation and the complexity of fraud data, decisions often come down to value judgments. How can you make the most of your expert resources so they know which leads to focus on and why?  

Solution: Deep learning can leverage domain expertise 

Deep learning won’t replace your fraud team. Rather, it informs them by reducing the need for manual, repetitive work. It gives your team the ability to prioritize leads, focus on strategic analysis and improve accuracy. Moreover, the domain expertise that investigators and fraud organizations have spent years developing is an excellent input to more finely calibrate the results of algorithms based on deep learning methods. When investigators work alongside a deep learning system, their reviewing and labeling results improves and optimizes the analytics.  

Deep Learning is Key to Pareo Fraud: Detection 

For all of these reasons, deep learning is the core application of A.I. selected by ClarisHealth to power our Pareo Fraud: Detection solution. It’s able to dig deeper and wider into your data to find those very subtle changes and nuances in provider and member behavior that may indicate fraud, waste or abuse that even expert investigators may not be able to find on their own. Our 4-tier scoring methodology – configurable by health plan priority – allows the models to self-learn and evolve so that the false positive rate reduces with each round of provider scoring and data ingestion. 

Pareo Fraud: Detection is also able to transcend the explainability issue that creates confusion in many A.I.-based systems. It was designed to address this “black box” gap through a specialized Super Score output – a separate deep learning method – that provides insights into results to engender trust by users. And because it’s built onto the Pareo platform, it creates an end-to-end payment integrity solution that fully eliminates gaps in a health plan’s cost containment continuum, from prepay to post-pay. 

NOW'S THE TIME FOR TOTAL PAYMENT INTEGRITY

Talk to ClarisHealth about how Pareo®, a total payment integrity platform, is driving innovation at health plans.