Using A.I. to Unite Fraud and Payment Integrity

Using A.I. to Unite Fraud and Payment Integrity

Part 1 of our series on how the SIU can use artificial intelligence to overcome common challenges. Bringing together fraud and payment integrity efforts on a single A.I.-powered platform can accelerate health plan savings.

Data and work silos have become a top barrier to health plans’ ability to effectively combat fraud, waste and abuse. Imagine your SIU has been building a case against a suspect provider and requests 50 medical records. At the same time, the provider’s reputation as a bad biller has not gone unnoticed, and another department requests 150 medical records. And just like that, a strong fraud case virtually disappears over a simple communication error. Not to mention the associated provider abrasion. Today, artificial intelligence capabilities have progressed to close these gaps and support data sharing that improves outcomes across cost containment. How could your health plan benefit from using A.I. to align fraud and payment integrity efforts?

Why the SIU and Payment Integrity Should Align

The biggest challenges we hear from SIU leadership ultimately come back to poor communication with payment integrity. Not only the fallout from not knowing about active or previous audits of a suspect provider. But also the need for measuring the team’s efficiency beyond basic metrics of cases opened and dollars saved. These silos can even prevent both the SIU and payment integrity from knowing what information might be available.

You benefit from access to as much intelligence as possible. And this disconnect forces you to work harder instead of smarter. How do these silos emerge, and what could your FWA team do with better access to more timely data on providers?

4 consequences of FWA and payment integrity silos

Limited communication and information sharing between the SIU and overall payment integrity can arise due to several factors. Cultural, political and technology barriers can create the separation. Then, administrative processes emerge in their wake to deepen the divide. As a result of these silos, the entirety of cost containment suffers the consequences:

  1. Potential provider abrasion due to pursuing false positives or duplicated efforts.
  2. Lack of efficiency to get through all leads, cases and audits in an appropriate manner.
  3. Lack of effectiveness in thoroughly managing cases and audits which can lead to missed savings opportunities and undetected fraud, undetected payment policy gaps and even undetected inabilities to follow contract terms both on the provider and the payer side.
  4. Inability to report on SIU progress – and the ROI of the SIU – in a very timely and effective way. This gap also prevents health plans from meeting State and Federal regulatory reporting guidelines, especially for those plans that are Medicare Advantage or Medicaid HMOs.

FWA isn’t necessarily separate from overall payment integrity initiatives – particularly provider audit and data mining. The way each function pursues cost savings may have some similarities and some differences. But ultimately, they have the same goal: paying claims appropriately and efficiently. To eliminate the duplicated effort that can stymie this goal, start by considering what information can and should be shared.

What information should the SIU and payment integrity share?

A.I. – like the fraud and payment integrity efforts it supports – gets its power from varied information that provides additional context for data-driven decision making. If your health plan hasn’t historically shared information across cost containment functions, we recommend you come together on this issue by asking a few questions. Namely, what intelligence does payment integrity currently share with you, and what do you wish they would share? And conversely, what intelligence do you share with them, and what would be beneficial to share?

These questions may seem obvious at first. But particularly when thinking about what isn’t being shared, you may have to probe a little deeper. Consider the information that hasn’t been possible to share as well as anything useful that may have been overlooked in the past. In general, we find that the SIU and payment integrity benefit from shared visibility in five major areas:

  • Provider audits and cases: The SIU can see when an audit is open, and an auditor can be notified when you open a case against a provider.
  • Provider audit history: Utilize past PI audit history for FWA scoring and modeling of AI functions to increase detection capability and accuracy.
  • Medical records: A shared view of medical records cuts down on potential provider abrasion as well as duplicated effort.
  • Provider communications: A shared view of medical records requests and other correspondence and education with providers in question and other case stakeholders. Information may also include interviews (audio or documents) with providers, patients, office staff, etc. related to audits or cases.
  • New schemes or audit types: Two-way sharing of this information provides ideas for each function and allows the SIU to dig deeper into areas of high potential.

If your FWA function has already opened the lines of communication with payment integrity, you are ahead of the game. Even reactive, informal and/or manual processes where you share insights via email or regular meetings, for example, offer value. But to attain a true competitive advantage, you should seek out real-time information exchange that integrates seamlessly into current workflows.

Maximize SIU Effectiveness with A.I.

To enhance the value of the information you exchange with payment integrity, artificial intelligence has developed into a true solution. Highly practical applications of A.I. for health plans already include better detecting complex and previously unknown healthcare fraud schemes. Combining that with intelligence from payment integrity audits and integrating it into the workflow maximizes the effectiveness of both functions.

Alerts and workflow automation

A.I. to unite fraud and payment integrity efforts can look like alerts that notify the SIU of an open audit on a provider, or vice versa. It could also prevent payment integrity from pulling claims for any type of audit on providers with active cases until a determination is reached. This workflow automation allows auditors and the SIU team to be more proactive with their work. It also eliminates duplication of effort, which wastes time and can make a health plan look disorganized.

Role- and user-based security

A.I. and workflow automation can also ensure users have access to information on a need-to-know basis. From limiting who has access to sensitive data like medical records to prioritizing who gets notified of new schemes or audit types. You want to be able to segment this access as much as possible.

Configurable dashboards

What an SIU manager needs to see at any given time will differ from analyst or investigator needs. What medical management or another area of payment integrity needs visibility on also will vary. Being able to define those elements – at the role and user level – and discretely share information allows each expert to have real-time insights pushed to them. No need for ad hoc exchange of already-outdated data. These dashboards also make it easier to track the SIU team’s ROI and efficiency by automating a very manual reporting process.

Pareo A.I. Unites Fraud and Payment Integrity

The value of using A.I. to integrate fraud and payment integrity efforts cannot be overstated. For health plans that use Pareo across all payment integrity functions, a provider’s actual audit history is incorporated into a distinct A.I. model. These results roll up into a provider’s Super ScoreTM so abusive billers don’t get overlooked and inaccurate billers don’t become false positives that waste investigative resources. This model can also provider a “trigger” for the SIU to begin monitoring a highly audited provider.

Moreover, the SIU has full visibility on providers surfaced by Pareo Fraud. You can see all open audits, audit types, amounts, full claim history and the current status – automatically. As an integrated part of the payment integrity technology platform Pareo, it’s a comprehensive, web-based solution for the detection, intake, management, dissemination of information, decisioning, tracking, and reporting on leads and cases that are the result of fraud, waste and abuse.

These benefits just aren’t available with the siloed, legacy FWA tools in use at most health plan organizations. Pareo Fraud can help you quickly convert detection results to leads or cases, communicate relevant information back-and-forth with the audit function, and refer honest billing mistakes for provider education. Advanced integrative technology that provides broader access to real-time data will allow your health plan to modernize the SIU and broader payment integrity efforts alike.


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

Keeping Up with 2021 Healthcare Payer Technology Trends

Keeping Up with 2021 Healthcare Payer Technology Trends

Reviewing the drivers, restraints, challenges and opportunities for healthcare payer technology in 2021.

Those organizations that kept pace with healthcare payer technology trends weathered the uncertainty of this year relatively unscathed. In fact, 2020 made the strongest case yet for health plans succeeding with technology. If your health plan felt less prepared, you may have increased the intensity of your strategic planning efforts to ensure your health plan is on the right track. To that end, let’s explore the industry drivers, restraints, challenges and opportunities impacting these strategies as we look to 2021.

Drivers: Top Motivators for Health Plans in 2021

We can’t reflect on the past year and look forward to 2021 without addressing the novel coronavirus pandemic. It stands to have an outsized impact on the industry for years to come. And even though it didn’t uncover any unknown issues, it accelerated the need for solutions practically overnight.

Because COVID-19 so efficiently highlighted known gaps in the healthcare system – including how far behind many stakeholders are digitally – it is the source of the primary drivers for healthcare payer technology. Health plans must take care of these in the coming year or risk falling even further behind.

Calls for increased transparency

In 2019, we predicted that "health organizations will need to make real upgrades in technology if they haven’t already, or face issues meeting government regulations.” And in 2020, two rules brought this prediction to the forefront.

First, the rules against information blocking were finalized. Though deadlines for compliance have been delayed, the need for healthcare data interoperability has never been greater. This initiative is poised to solve several of the issues worsened by the pandemic, and health plans will continue to push for increased data sharing. Improvements in care quality and decision-making and progress on value-based care programs should result.

The administration also finalized a price transparency rule. It calls for first posting online documents that include prices for healthcare services and medications. A “shoppable” experience for consumers will follow.

The ultimate goal of technology is to break down barriers and allow information to empower a better healthcare system. Health plans have realized advanced technology is only as good as the data that fuels it. With interoperability, data accessibility and transparency as a focus, health plans will naturally evolve to start questioning any process within their organization that inhibits information sharing.

“Health plans that are able to adapt to these changing trends are far better positioned for long-term success.” FierceHealthcare

Work-from-anywhere environment

Improving data accessibility extends to internal operations at payers as well. The modern work-from-anywhere environment has arrived. The technology that supports it must follow. Health plans have adapted to the “do more with less” credo that pervades most industries, but manual and labor-intensive processes only contribute to the administrative burden.

By adopting integrative technology platforms, health plans can eliminate data silos and improve collaboration and oversight. As payers start to experience the big picture benefits of advanced technology, health plans will be able to work towards becoming more proactive and less reactive.

Changes in membership mix

Health plans have started to experience shifts in their lines of business. This year has brought an influx of members into Medicare Advantage, Medicaid and ACA plans. While severe impacts to employer-sponsored plans have not yet materialized, how consumers think about healthcare coverage has changed for good.

Members are tasked with owning their own healthcare experience and expect the relationship with their health payer to be frictionless and intuitive. Not meeting consumer demand will open payers up to disruption. But if health plans make technology decisions with their eye firmly on the member, they will also find numerous opportunities to improve program integrity efforts.

Changing competitive landscape

Increasing consolidation among health systems and payers is also motivating health plans to innovate. They understand that relying on legacy technology and paper-intensive processes minimizes the ability to scale. Health plans are taking steps now to upgrade their position.

And not a moment too soon. The long-predicted disruption to healthcare has arrived as top retailers have made bigger inroads in the industry. In an increasingly consumer-driven environment, demonstrating value to members and employers is key. Payers with a tech-first mindset – and the ecosystem to match – will have the strategic advantage in these situations.

At the same time, health plans will see more technology vendors looking to leverage experience with other industries into similar successes in the healthcare sector. Technology can help rapidly improve ROI on the claims recovery process. But health plans will need to shrewdly evaluate these solutions to ensure a good fit.

Restraints: Navigating the Roadblocks Health Plans Face

Health plans have long known the advantages of advanced healthcare payer technology. But the usual suspects continue to block progress. Slim margins, data security concerns and shortages in skilled workers could prevent health plans from making headway on their goals this year.

Uncertain medical loss ratios

The uncertainty around how the ongoing pandemic will affect medical loss ratios has some health plans putting strategic technology investments on hold. Profits at most insurers have risen this year, but many industry leaders predict a forthcoming correction. Combined with the historic struggles to efficiently and effectively transition to digital processes, taking on new technology projects may feel too risky in the short-term.

Health plans can overcome this perceived risk by seeking out solutions that surface quick wins and set them up for long-term advantages. Look for speed to value. Enterprise healthcare payer technology that is easy to implement, builds empathy with stakeholders, improves efficiency and reduces team frustration will pay dividends.

Concerns about data security

Dealing with large amounts of patient data makes health plans a prime target for security breaches. And the entire industry trying to quickly integrate numerous data sources has the potential to create vulnerabilities in the system. But health plans moving too slowly with technology adoption can lead to irreparable harm as well.

Current manual approaches to PHI – locally stored data, paper faxes, etc. – are even more vulnerable than secure digital processes. Modern technology, on the other hand, can grant you more control. It allows you to be more granular with granting access to PHI, for one. It also creates a digital log of access. For even greater peace of mind, seek out HIPAA-compliant technology vendors that pursue HITRUST CSF and SOC 2 certifications.

Unexpected costs of outsourcing

The data sharing and transparency regulations and other technology initiatives have payers concerned about how they will pay for and staff these projects. Additionally, health plans will have to overcome learning curves, fear of change and other internal challenges as they select solutions and look for increased returns. The vast majority – 79% – will look to outside vendors to cover these gaps.

Predictable costs will make these burdens easier to bear. Off-the-shelf solutions that are easily configurable will prove more cost-effective than custom-built technology. And a strategic combination of insourcing and outsourcing activity based on health plan core competencies will also optimize spend. A partner and technology 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.

Challenges: Factors for 2021

Internal restraints aren’t the only barrier to success with healthcare payer technology. Let’s look at the broader industry factors that could challenge health plans in 2021.

Provider financial instability

So far, providers have borne the brunt of the pandemic financially, and their survival is at risk. Healthcare payer technology strategies will need to support this valuable group. Without a broad network of providers, health plans will find it difficult to advance on engaging members and lowering healthcare costs. Health plans that use technology to focus on this relationship can overcome this challenge. Consider solutions that ease providers’ claims payment administrative burden and support real-time communication.

Slow adoption of value-based care

Value-based care continues its slow adoption among providers. Those participating in alternative payment models performed better than their fee-for-service counterparts in 2020. They also are more likely to pursue population health improvements that stand to keep their patients healthier during the pandemic. But other providers may hesitate to take on more risk.

Health plans can support providers in this transition with healthcare payer technology that overcomes trust and abrasion issues. Increase data transparency so both sides of the relationship are working from the same playbook. Come to agreement on interpretations of value and quality. And measure everything: clinical quality, consumer experience, return on investment, and more. Then share those data insights and work together on continuous improvements and innovations.

Considerations for selection to participate in CMS’ largest bet on value-based care to date “will include an entity's risk-sharing experience, IT infrastructure, compliance and beneficiary engagement.” Healthcare Dive

Uncertain political landscape

A new administration will be in place this coming year, including new leaders at government healthcare agencies. Stabilizing the coronavirus response will likely be the focus of any short-term action, which most healthcare stakeholders should welcome. Whether or not additional burdensome regulations or market changes will be introduced is currently unknown. Agile, tech-forward health plans will be positioned to succeed no matter what happens on this front.

Opportunities: Chances to Excel with Healthcare Payer Technology

While challenges abound in an uncertain healthcare environment, so does opportunity. Changing member behavior and technology advancements may both offer health plans the chance to succeed with their digital transformation goals.

Members open to engagement

One unexpected benefit of this year is how it has opened up avenues for member engagement. People want to hear more from those responsible for their care. They have embraced home health. And they have welcomed technology into their healthcare unlike ever before.

Payers have caught on to the fact that providing improved member services is a differentiator in a consumer-driven market. By offering convenience and addressing social determinants of health, plans can offer broader benefits with perceived higher values while lowering costs. CMS has made it easier for health plans to offer supplemental benefits, another incentive for offering them. With health plans expanding coverage in this area, digital health adoption will continue to grow.

"Leveraging the power of your lifestyle and combining it with research and technology will enable people to take full control in their health journey. With the cost of healthcare rising, providing tools to prevent or reverse diseases that could be costly for patients and the system is a win-win.” Health plan Chief Innovation Officer

Advancements in technology

Technology advancements continue, as API standards are enacted and artificial intelligence capabilities improve. Plans can leverage the mountains of data they collect through improved data analytics technology, reducing time to reports and empowering real-time decision making. And these updates come just in time. Expansion of digital healthcare may prompt more incidents of improper payments and bad actors. But A.I. has also pushed forward opportunities to proactively combat fraud, waste and abuse. By going deeper and wider into the data to push likely leads to you, new schemes won’t pass you by. This improved technology can better integrate the SIU with overall payment integrity as well.

Through secure integrations, data sharing could be a hurdle that health plans finally surpass. Cognitive collaboration capabilities will emerge for health plans if they utilize the right technology solutions. Empowering users to break down barriers within their organization will drive efficiencies and improve the care continuum.

Partner to Make 2021 the Best Year Yet

Health plans can stay ahead of the curve by making strategic investments in change, particularly surrounding transparency. Integrative technology and shifts in program integrity approaches will allow payers to continue to gain ground and focus on proactive efforts, particularly when it comes to claims recovery and payment integrity.

Extending your competitive advantage transcends trends. Fortunately, a comprehensive technology platform like Pareo allows health plans to scale and improve processes, harness the power of A.I., increase medical savings, and accelerate ROI. Talk to ClarisHealth about how Pareo can keep you a step ahead of healthcare payer technology trends – no matter what the future brings.


See the ClarisHealth 360-degree solution for total payment integrity in action:

Will Artificial Intelligence Finally Make Good on its Promise to Healthcare?

Will Artificial Intelligence Finally Make Good on its Promise to Healthcare?

Artificial Intelligence is making the leap from much-hyped “trend” for healthcare technology to more widespread adoption. Here’s what A.I. is — and isn’t — and how health plans are proving its value.

Artificial Intelligence for healthcare has come a long way since 2017 when IBM Watson Health, the A.I. supercomputer fell short of its high expectations. A.I. is a powerful force in advanced healthcare technology and is poised to disrupt the industry. And, due to several converging factors in 2020, adoption accelerated. Let’s explore what A.I. is — and isn’t — and why it’s here to stay despite current limitations. But first, a few definitions.

Defining Artificial Intelligence

While varied stakeholders have found value in applications of artificial intelligence for healthcare, confusion remains about what it is and isn’t. When the industry uses buzzwords like “A.I.” and “machine-learning” to describe product functionality, know these words are easily misunderstood and thus, often used incorrectly. Let’s level-set with some working definitions of terms you might encounter when evaluating advanced healthcare technology.

Artificial Intelligence: Intelligence applied to a system with the goal of mirroring human logic and decision-making. A.I. is utilized for the purpose of successful knowledge acquisition and application, which it prioritizes over accuracy. A.I. simulates intelligence (the application of knowledge). It includes many subcategories and is often separated into three types: narrow, general and super.

Narrow Artificial Intelligence: Created specifically for a single task or to solve a single problem. Almost all applications of A.I. In use today are of this type.

General Artificial Intelligence: A type of broad and adaptable A.I. that can think and function just like humans. Not generally available today, though advancements in neural networks may offer a path to this reality.

Super Artificial Intelligence: A theoretical type of A.I. that is imagined to exceed human cognition significantly. It should emerge from the exponential growth of A.I. algorithms self-learning. Does not currently exist.

Machine Learning: An application of A.I. that allows a system to learn on its own. ML learns from data, and it aims to increase accuracy (success is a lesser concern). ML simulates knowledge. Source Includes both supervised and unsupervised methods.

Supervised Models: Algorithms designed to learn by example, based on labeled datasets that provide an answer key that the algorithm can use to evaluate its accuracy on training data. The term “supervised” learning originates from the idea that training this type of algorithm is like having a teacher supervise the whole process. When training a supervised learning algorithm, the training data will consist of inputs paired with the correct outputs.

Unsupervised Models: Machine learning technique that finds and analyzes hidden patterns in “raw” or unlabeled data. By ignoring labels altogether, a model using unsupervised learning can infer subtle, complex relationships between unsorted data that semi-supervised learning (where some data is labeled as a reference) would miss. And do so without the time and costs needed for supervised learning (where all data is labeled).

Data Analytics: Process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

Predictive Analytics: Historical data that has been collected is utilized to try and predict behavior/outcomes (often called Data Science). To analyze data, it is routed into a report, at which point humans or artificial intelligence apply multiple factors to make predictions about expected outcomes. Though based on decades-old technology, “predictive analytics” often implies that a machine has performed the analysis and offered a prediction (rather than a human).

Neural networks: Seeks to simulate human brain processing, which is facilitated by networks of neurons. At its simplest, a neural network processes information in three layers: 1. Input layer where data enters the system, 2. Hidden layer where data is processed, and 3. Output layer where the system decides what to do with the information.

Deep learning: Allows for increasing numbers of layers through which data passes, where each layer of nodes trains on a distinct set of features based on the previous layer’s output. The further you advance through the layers, the more complex the features your nodes can recognize, because they aggregate and recombine features from the previous layer.

Overcoming Concerns About Artificial Intelligence for Healthcare

Though experts have long predicted that artificial intelligence for healthcare would take hold, adoption has progressed slowly. And no wonder. The industry has been historically hesitant in its pursuit of advanced healthcare technology. And for clinical care applications, particularly, providers express discomfort with “black box” A.I.

Forbes points out, “As humans, we must be able to fully understand how decisions are being made so that we can trust the decisions of AI systems. The lack of explainability and trust hampers our ability to fully trust AI systems.”

In addition to explainability issues, lack of broad access to healthcare data stymies effectiveness of A.I. Data silos and patient data privacy concerns both limit access that could push the technology forward. But federated learning could help overcome this barrier. It’s a privacy-focused approach to machine learning that allows companies to collaboratively form more representative data sets without sharing raw data. Once-generic models get smarter over time through decentralized data and decentralized compute power.

This approach could also help overcome another perennial concern about A.I.: model bias. A.I. systems learn to make decisions based on training data, which can include biased human decisions and amplify historical or social inequities. It’s a complex problem with life-and-death consequences in using A.I. for healthcare. But leveraging larger, more varied data sets, increasing transparency of processes, improving awareness of potential bias in A.I. outputs, and continuing to augment machine decisions with human expertise will mitigate this issue.

2020 Makes the Case for A.I. in Healthcare

Early adopters in the healthcare payer sector understand the benefits and risks associated with A.I. all too well, and skepticism of vendor claims of A.I. is high (and rightly so). However, the value of artificial intelligence in healthcare increased significantly in 2020 due to two primary drivers.

First, A.I. compute has been doubling every three and a half months making applications faster and cheaper. In just a year and a half, large image classification systems are training much faster, from three hours down to 88 seconds. Progress on natural language processing (NLP) classification tasks is also “remarkably rapid.”

Second, the novel coronavirus pandemic accentuated the foundational weaknesses in the healthcare system. All at once, various stakeholders around the industry sought A.I. applications to address the resulting challenges. They also found value in collaborating to achieve the data access, sharing and quality needed to power these tools. Solutions for drug discovery and disease prediction emerged at a record pace. And the data and research breakthroughs promise to continue to push the industry forward.

Applying A.I. to Advanced Healthcare Technology

A.I. is expected to permeate every facet of the industry healthcare, with annual spending on artificial intelligence in healthcare estimated to reach be more than $34 billion in 2025, up from $2.1 billion in 2018. A recent survey of healthcare organizations found 98% have implemented an A.I. strategy or plan to develop one.

The current applications of AI in healthcare are narrow and highly functional. But as adoption accelerates, it generates momentum which perpetuates the benefits of A.I. In fact, 59% of healthcare leaders expect to achieve a full return on their investment within three years.

Some current applications of A.I. for health plans include:

  • Detecting and preventing fraud, waste and abuse
  • Value-based care initiatives
  • Claims management
  • Supporting coordination of benefits
  • Surfacing business intelligence insights
  • Increasing effectiveness of clinical audits
  • Member outreach and engagement
  • Automating administrative processes
  • Predicting healthcare needs

“With AI, the more quickly organizations in early or middle stages of AI deployment move forward, the sooner they will overcome uncertainty and unlock the rewards of this powerful business tool.”

So far, no single A.I. vendor has emerged as the leader in the industry. In the coming year, healthcare payers will continue to see technology disruptors enter the market. For many plans, a selection of effectively managed vendors will be the most effective strategy to drive ROI. Though health payers will have to be careful of hype, particularly from tech vendors of artificial intelligence solutions for healthcare who lack industry experience. What works for one sector — say, finance — does not easily translate into healthcare, which is often more complex, more heavily regulated, and more data sensitive.

How Your Health Plan Can Find Value in A.I.

You might think that being a fax/email/spreadsheet organization means your health plan is woefully out of date, but you might not be as behind as you fear. Even as the early adopter stage of A.I. comes to an end, it still confers a competitive advantage for those health plans that can scale its use. But now is the time to move. Envision the role A.I. will play in your organization in the years to come and develop a strategy roadmap to invest in and leverage A.I. assets.

It pays to keep in mind that, while A.I. can offer much needed technology advantages to health plans, it can’t solve all payment integrity challenges on its own. Ensure your health plan has the best chance to realize these benefits now and into the future. You can and should deeply question your technology vendor and demand specifics around their solution’s A.I. capabilities.

Health payers will need to see all the moving parts of their tech ecosystem, including real-time metrics on advanced healthcare technology performance, in order to prove its value even if A.I. capabilities are touted. Increasing visibility across disparate departments, deriving insights from siloed data, realizing cost savings, and augmenting human expertise and productivity are exactly the type of improvements that show A.I. at its best.

Pareo – Powered by A.I.

At this stage, A.I.- powered solutions may be more commonplace than you realize, but the true value offered by artificial intelligence for healthcare varies from vendors to vendor. The most powerful way to harness A.I. capabilities is when they are applied as part of a broader solution, an advantage provided by an integrative platform like Pareo®.

Pareo® offers multiple applications for of A.I. as part of a broader “one-source” system insight platform for health plans and payers. Deep learning powers the fraud, waste and abuse detection and prevention solution that integrates seamlessly with overall payment integrity. With its multi-dimensional view of data, it offers distinct advantages over rules-based tools.

Pareo also leverages artificial intelligence to increase auditor efficiency and effectiveness with clinical audits. It integrates OCR technology to make unstructured data searchable, filterable and sortable. Then, NLP and machine learning applications of A.I. help auditors prioritize cases for review and automatically tag relevant documentation. The solution also generates confidence scores for denials so cost containment leaders can better trust results.

A.I. is a crucial technology for your health plans to adopt or expand upon throughout your organization. Set yourself up for success and start using artificial intelligence now to address the challenges in healthcare brought on by increasingly complex data.



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

5 Steps to Reducing Fraud, Waste and Abuse

5 Steps to Reducing Fraud, Waste and Abuse

Use these tech-enabled tips to comprehensively reduce FWA.

States require MCOs to take proactive measures that reduce not just fraud, but also waste and abuse. But how can you ensure your efforts not only meet compliance requirements but also help secure a competitive advantage? Through the lens of a total payment integrity solution like Pareo, successful comprehensive FWA management is achievable. Let’s dive in a little deeper and look at the most innovative steps that payers and health plans can take to reduce fraud, waste and abuse and maximize plan savings in 2020 and beyond.

1. Analyze your post-adjudicated and post-pay claims data.

Because Medicaid MCOs are administered at the state level, federally governed program integrity tactics and guidelines remain a challenge, says this 2019 report to Congress. But health plans and payers seeking to establish payment integrity in their program can look to this clue, provided by the referenced report, where the Commission recommends payers implement technology and processes appropriate to assessing their payment integrity performance. Specifically, “data systems capable of storing and analyzing patterns of claims data but also personnel with statistical, medical, and investigative expertise.”

This indicates that the Federal government will become increasingly interested in the analytics of claims data, including how financial responsibility was determined (post-adjudicated) and paid for (termed post-pay). Therefore, health plans, providers and MCOs must affix data-driven insights to the success of their FWA programs. Pareo makes available post-adjudicated and post-pay claims data for analysis and reporting efforts, and you can automate the mandated reporting of the effectiveness of your anti-fraud measures specifically.

2. Intelligently flag potential fraud, waste and abuse claims.

Does your FWA solution have the ability to flag potentially problematic claims in real-time? Does it autonomously flag potential fraud, waste and abuse claims at all, which prevents them from escalating into million-dollar mistakes? Pareo offers real-time flagging to assist health plans and payers in identifying and deterring claims that signal waste as well as multi-tiered provider scoring that indicates potentially fraudulent or abusive billing patterns.

Preventive measures allow a plan to take immediate proactive steps to reduce fraud, waste and abuse, which are a large portion of the improper payment rates reported by CMS (last year averaging about 10%). A 2019 study published in JAMA found that approximately 25% of U.S. healthcare spending is waste. Of that, conservatively, clinical waste totaled 27% and fraud and abuse 7.6%. Total payment integrity solutions like Pareo provide a powerful platform for health plans looking to prevent and recoup these costs.

3. Automate auditing workflow.

There are several regulatory steps for claims auditing procedures, many of which can be automated with an advanced technology platform. Among the most useful provided by total payment integrity solution Pareo include:

  • Initialization of medical records requests which triggers a nurse audit review
  • Medical records details included for nurse audit review process
  • Aggregation of insurance claims data
  • Overpayment tracking
  • Overlap control – both preventing suppliers from working claims outside of their assignments and excluding active fraud cases from audits

These streamlined workflows increase auditor productivity 3x and contribute to 5% lower administrative costs for Pareo clients. Reducing the administrative burden for a health plan or payer allows for staff to focus on other higher-value tasks associated with cost containment goals. Also, a health plan can avoid the negative consequences seen by older payment integrity solutions that introduce unnecessary friction into the payer-provider relationship.

4. Integrate program integrity efforts end-to-end.

As the industry moves away from “pay and chase” activities into more proactive measures, program integrity processes that emphasize a 360-degree approach to cost containment and reducing fraud, waste and abuse should be the goal for health plans. This comprehensive model should integrate prepay to post-pay, internal to external, audits to provider, recoveries to posting, and payment integrity to the SIU for an end-to-end solution.

Technology platforms like Pareo offer intuitive processes and integrations for all stakeholders within the common framework:

  • Configure timelines appropriate to prospective or retrospective provider audits and easily apply successful post-pay concepts to the prepay process.
  • Increase transparency in the payer-vendor relationship to improve the audit assignment, concepts approval and invoicing process while maximizing effectiveness of internal and external resources.
  • Automate the refund letter request process, including supporting clinical information; streamline the provider communication and education feedback channel; and close the loop on recoveries and posting to reduce provider abrasion.
  • Share valuable provider and claims auditing information between payment integrity and the SIU.

Optimizing your avoidance and recovery efforts is just one of many ways to reduce fraud, waste and abuse.

5. Optimize with predictive analytics and A.I. capabilities.

“It is important to note that while all payments made as a result of fraud are considered ‘improper payments,’ not all improper payments constitute fraud,” writes CMS in an annual report for Congress dated from 2015. Distinguishing between the two is essential to reducing provider abrasion and false positives that can overwhelm the SIU, and integrating multiple relevant data sources can help with this distinction.

In their most recent report, dated November of 2019, CMS announced an initiative to keep unscrupulous providers out of federal insurance programs (known as Program Integrity Enhancements to the Provider Enrollment Process). Combined with the collaborative Healthcare Fraud Prevention Partnership already in place, it’s clear that information sharing designed to create an environment unfriendly to fraud schemes and support predictive analytics is the goal.

Health plans looking to minimize improper payments due to fraud, waste, and abuse should also take advantage of the power of predictive analytics. Pareo is an accessible platform that offers health plans and payers predictive analytics and applications of A.I. like deep learning capabilities designed to prevent and reduce FWA. In fact, many of the outstanding qualities of Pareo are in line with the proactive measures CMS is taking to prevent improper payments.

Pareo Meets You Where You Are So You Can Quickly Reduce FWA

If you think a comprehensive payment integrity and FWA technology platform that leverages the power of A.I. is only available to health plans heavily resourced with time, money and personnel, think again. Pareo offers unparalleled configurability and a unique outsource-to-insource model that allows you to take advantage of proprietary concepts and tech-enabled services to maximize your internal resources over time.

ClarisHealth designed Pareo as a total payment integrity platform unlike any other available on the market. By leveraging innovative technologies with a singular, user-friendly interface, our clients have seen dramatic improvements in their ability to reduce fraud, waste and abuse



See the ClarisHealth 360-degree solution for total payment integrity in action:

What’s your best path to adopting advanced FWA technology?

What’s your best path to adopting advanced FWA technology?

Take this interactive self-assessment to evaluate your current SIU successes and determine your health plan’s need for advanced technology to level up your fraud, waste and abuse mitigation efforts. 

More advanced technology solutions for healthcare fraud detection, analysis and management are now readily available on the market. But, if you’re used to traditional rules-based tools siloed from other cost containment functions, the mindset shift required to think a little differently about how best to augment the SIU’s expertise can be daunting. How can your health plan properly prepare to take advantage of an advanced FWA technology solution? Take the interactive self-assessment to find out. 

Self-Assessment: Evaluate Your Readiness for Advanced FWA Technology 

The first step when considering upgrading enterprise technology solutions – for any area of your health plan – is thoughtfully considering your current state. While there are no wrong answers, this self-assessment will help you level set on some key areas of evaluation and decision making to better prepare you for successfully adopting a modern FWA solution.

Your Results: How Did You Score? 

Now that this assessment has provided a framework to evaluate your current efforts to mitigate FWA, your scored answers can help you determine a suggested path to successfully adopting advanced technology for fraud detection, analysis and management. 

5-13 points: Time to access outside expertise 

At this level, your SIU may not have access to sufficient personnel and data science resources to make significant gains in your FWA mitigation efforts. The time spent running down false positives and manually compiling reports to satisfy regulatory compliance requirements only exacerbates the issue by further constraining over-taxed clinical and investigative experts. If you’re like many teams being asked to do more with less, how can you get ahead? Next steps: 

  1. At this level – especially if you struggle to maximize your value from current tools – the very idea of adopting advanced technology can seem an insurmountable obstacle. You’re not alone in this feeling, and it’s possible to catch up.
  2. An achievable goal is working on tackling overpayments in whatever form they come – namely, wasteful billing practices. Working with the audit function at your health plan and shoring up payer-provider relationships are excellent foundational best practices that can make your FWA function more effective. 
  3. While advanced technology, clinical audits and investigative capabilities are the three pillars of preventing and mitigating FWA, it’s okay if your SIU isn’t well-resourced in all of those areas. Outside expertise can help you shore up any gaps until you are ready to internalize additional functions. 

14-17 points: Integrating FWA and Payment Integrity yields dividends 

You’ve been able to make a great deal of progress on your own up to this point, and that is to be commended. But your current siloed tools that keep your SIU team disconnected from other areas of cost containment may be holding you back from real gains. Next steps: 

  1. Are your current tools for fraud detection, analysis and management as advanced as you think? Most are based on decades-old technology. These three tell-tale signs can help you investigate the differences. 
  2. Your FWA efforts may be stymied by a lack of key information if you’re not well aligned as an integrated, total payment integrity function. Watch this 10-minute presentation any time for a look at the consequences of these data silos and the five benefits of eliminating them. 
  3. Start thinking about how advanced technology like deep learning models can help you overcome perennial challenges like false positives and manual processes. This on-demand webinar is a great educational resource. 

18-23 points: Ready to take your FWA efforts to the next level 

Relying solely on the rules-based tool your SIU has worked with for years is no longer the sophisticated strategy your innovative organization has historically found success with. And advanced data science teams that are still relying on predictive analytics understand that applications of artificial intelligence have progressed to an ideal and achievable solution for mitigating healthcare fraud and abuse. Next steps:

  1. When you go looking for an advanced technology solution for FWA, it pays to understand that all A.I. isn’t created equal. Here are three questions to ask to determine if the solution you’re investigating is the real deal. 
  2. Connecting disparate data sources – geographic, weather, financial, audit data and more – that provide context to aberrances enables your SIU to work smarter. Here's how exactly deep learning models help address the complexity of healthcare data that foils simpler tools – while overcoming the interpretability hurdles presented by A.I. results. 
  3. While you don’t have to give up your reliable rules-based tool to take on an integrated fraud detection platform like Pareo, which is powered by A.I. based on deep learning models, ferreting out potential fraud and abuse without rules is a mindset change. This article helps explain the difference in the approaches, including how, by working with a modern FWA solution, your internal experts continue to provide a key competitive advantage for your health plan.

Bottom Line: Setting Expectations is the Key to Success 

No matter how sophisticated your current FWA operations are, you can successfully adopt advanced technology that helps you maximize avoidance and recoveries at the most optimized cost. Along with the stage-specific recommendations we offer above, here are three additional pieces of advice: 

  1. Start by setting realistic goals based on what is it that you want to accomplish. How do you want to use this technology in order to address the core challenges you're facing? 
  2. Determine if your staff is organized appropriately. If your goal is to increase your case level or increase your savings, does that mean that you're going to need additional staff? How would you organize (e.g., by line of business or product line)? 
  3. Prepare your SIU for the change. How will advanced technology affect your workloads in your day-to-day operations, and how do you make the most of your role? 

 Do you need help preparing for and navigating this change? Don’t hesitate to turn to us for fear that it’s too early to engage a partner in this processYour SIU can benefit greatly from advanced FWA technology that features true A.I. powered by deep learning models and seamlessly connects fraud detection, analysis and management. Those benefits are compounded greatly when they’re natively integrated on a platform like Pareo that also engages with overall payment integrity activity. This integration offers unparalleled visibility into the progress your team is making toward their goals and helps prove their sizable impact on cost containment at the health plan as a whole.



See the ClarisHealth 360-degree solution for total payment integrity in action:

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. 

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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. 


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