A Data First Approach to Fraud, Waste and AbuseUse Machine Learning to UncoverMore Meaningful Data

Michael EzzoDirector of Software Engineering NCI, Inc.

www.nciinc.com · 11730 Plaza America Drive · Reston, VA 20190 · contactnci@nciinc.com

The promise of machine learning to unlock smarter data analytics and surface more predictive trends rested on being able to tap into a massive amount of data to crunch and analyze. This chicken-egg situation is beginning to work itself out. In the healthcare industry, data is pushing into new voluminous heights and sophisticated machine learning capabilities are being employed to marry structured and unstructured data to improve clinical, administrative and financial operations.

One area in particular where machine learning is already paying dividends is in combating fraud, waste and abuse. This is especially true at federal healthcare agencies—where some of the largest payers in the industry operate under Congressional mandates. Being able to detect, prevent, deter and recoup fraudulent or erroneous payments efficiently and effectively is big business. Machine learning’s capability of becoming smarter with more data lends itself well to keeping up with the new fraud schemes that are constantly emerging. And at the same time, enables parties on both sides—provider and payer—to start identifying and making process improvements automatically.

Here are five process areas where machine learning can marry structured and unstructured data to unlock more meaningful insights, predictive analytics and comprehensive trends analysis in the fight against fraud, waste and abuse.

I. Medical Record ReviewAccording to a 2016 report by the Office of the National Coordinator for Health Information Technology, more than 90 percent of providers are using a certified electronic health record (EHR). Yet not all of these providers are able to exchange information and data seamlessly. There are, for example, over 1,000 EHR providers (ranked annually by Black Book Rankings), which makes interoperability a widespread challenge. With varying systems, charts, notes and images, etc., making sense of all this data to find the best outcome, especially at the point of care. It is often a laborious and tedious endeavor.

An ever-increasing amount of patient data is being collected, but healthcare organizations are challenged to realize its full potential. This is because 75 to 80 percent of data in EHRs is unstructured and can’t be stored in a conventional database and analyzed. The information from these records is a gold-mine of valuable information. What is required are new tools using machine learning and advanced analytics technologies that can extract key information from unstructured data so it can be loaded into database tables and mined for insights.

Machine learning can help sift through all of the structured and unstructured data that lives in the multitude of lab work, nurses and physician notes, discharge summaries and so forth. Automating this process—and continually learning from the data—can help make care interventions more applicable and perhaps even more predictable.

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II. Program IntegrityAlternative payment models focused on value over volume are creating new and additional reporting structures, and turning claims processing on its head. This is an area ripe for change, as machine learning software can introduce new efficiencies in claims processing by making the most out of unstructured data from doctor’s notes, for example. Trends and anomalies can more easily be uncovered and with greater frequency—ultimately enhancing the provider’s ability to choose the right care or preventative care for the patient. This new, enriched data helps insurers rationalize payments across various service levels and in support of cost-cutting policies around unnecessary medical services. It also is capable of turning vast amounts of unstructured data into meaningful and actionable data that can then be used by federal agency backend systems to improve overall fraud, waste and abuse prevention capabilities.

III. Risk AdjustmentRisk adjustment is another area where machine learning is introducing new insights that are invaluable to stratifying coding data. What has traditionally been a very manual process can now be automated. Machine learning software is capable of extrapolating massive amounts of structured and unstructured data from medical records to improve risk adjustment accuracy—making it easier for coders to diagnose and certify patient encounters. Over time, the machine learns and can automatically flag or make adjustments based on trends uncovered while analyzing copious amounts of patient record data.

IV. Prior AuthorizationsBoth providers and payers benefit from leveraging machine learning to improve prior authorization processes. Claims data, EHR data and other types of handwritten or image-based anecdotal patient information can be analyzed through machine learning technology to better manage the administrative side of prior authorizations for medication and medical procedures. A 2017 American Medical Association (AMA) survey found it takes about 20 hours each week for providers to handle prior authorization requests. That’s a part time job. The AMA has also reported that almost 90 percent of physicians are troubled by providing timely care due to prior authorization delays. Machine learning can cut down on the double-digit number of hours it takes most providers to fulfill prior authorization requests by simply reading and auto-populating large amounts of patient data.

V. Regulations and ComplianceHealthcare regulations are abundant and complex. From HIPAA to ACA and MACRA and the FDA, to state waivers and drug guidance, regulations are splintered across various healthcare segments including insurance, pharmacy, medical and so forth. Yet the underlying functional challenge is the same—how to make sense of all the regulatory and legal requirements there are to conform with, keep pace with changes and updates, and then dutifully ensure and demonstrate compliance. Machine learning can help take on the arduous task of sifting through the unstructured data of regulatory text and extracting metadata to further decompose the requirement. By adding some structure and classification to the data, machine learning algorithms can identify new changes and alert reviewers. Machine learning can help streamline regulatory-based processes and also automatically create an easily reportable audit trail.

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Scaling Humans with Smart MachinesWhile the human element of healthcare cannot and should not ever be replaced, machine learning is fast becoming a key driver that is enabling healthcare enterprises to truly accelerate business value and ultimately improve patient care and outcomes.

It is also so closely tied to artificial intelligence (AI), big data and cognitive analytics. And the healthcare industry is primed to usher in advancements in all of these areas, which will undoubtedly translate into huge cost, productivity and time benefits. In fact, machine learning is just one piece of building out a more robust and comprehensive data strategy that is continually being fine-tuned as machines learn, compute, automate and reason with less and less human intervention.

Any trepidation that machines can replace humans, especially in healthcare, should be quelled under the pretext that these smart machines are actually working for them—not against them. The most appealing benefit of employing these types of technologies is they enable healthcare organizations to scale their human workforce, rather than spend their time on administrative burdens. Employees will instead be able to focus on maximizing the data they need to do their job which is ultimately delivering the most optimal care for their patients.

SmartDoc™ an Integrated Data-First Approach to Medical Record Document ManagementThe Centers for Medicare and Medicaid Services (CMS) estimates that over $60 billion in American taxpayer money is lost to fraud, waste, abuse and improper payments. NCI SmartDoc™ and NCI SmartView™ are specifically designed to enhance the federal government’s capability to identify and stop fraudulent Medicare claims.

NCI SmartDoc™ enables government agencies to manage complex needs by automating the capture and processing of documents, thus helping agencies increase productivity, reduce costs and improve services. It is an ideal solution for government agencies with limited resources that need to streamline their current processes. SmartDoc™ is capable of handling all document types, from structured to unstructured, and is exceptionally suited to fit government requirements.

SmartDoc™ is an innovative document capture and extraction solution, using the most comprehensive intelligent document recognition (IDR) software in the industry, and powered by sophisticated machine learning algorithms. It can transform structured and unstructured data from millions of pages of static medical records into meaningful data. SmartDoc™ combines IDR technology, medical records expertise, and best-in-class security and information systems to deliver a new, disruptive innovation to government healthcare fraud and payment accuracy programs. Integrated with SmartDoc™ is SmartView™, a medical record viewer with advanced document management capabilities. Together, these tools make up a complete end-to-end medical document processing and review system.

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Features of SmartDoc™: � NCI SmartDoc™ provides a single-source

access to documents and can quickly transform unstructured data locked in documents into meaningful information.

� Incorporates Machine Learning (ML) software that identifies medical record document types and extracts key information. This ensures medical records are available to reviewers categorized and organized into various record types.

� Browser-based system makes accessing medical records and documents fast and effortless.

� Imports, classifies, sorts, extracts and exports data from paper, fax, electronic documents and other digital sources.

� Medical records and other documents can be searched by keyword or phrase, improving business processes and generating better accuracy, compliance and customer satisfaction.

� Effectively control business process by validating extracted data.

� Comprehensive reports are available to enhance analysis and optimization of parameters used during document assembly and extraction.

� Compatible with NCI SmartView™, a full-featured viewer that provides bookmarking, annotation and keyword search capability.

� Fully integrated and secure solution, configurable to support custom medical record processing requirements and scalable to support future needs.

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Benefits of SmartDoc™: � Reduced operating costs through automated

document preparation and classification with minimal manual data entry.

� Improved quality of information driving critical business processes.

� Accelerated business processes with immediate access to all information and supporting documentation.

� Stronger compliance control through enforced electronic retention and redundancy.

� Reduced paper costs — no need to account for lost, duplicated, shipped, handled or filed papers, etc.

� Accelerated time to value and return on investment.

� Seamless integration with back-end systems.

� Fully secure solution, verified by multiple successful security control audits.

� Real-time visibility of the process available on NCI’s custom dashboard reporting solution.

Navigate, Collaborate, InnovateNCI is a leading provider of enterprise solutions and services to U.S. defense, intelligence, health and civilian government agencies. The company has the expertise and proven track record to solve its customers’ most important and complex mission challenges through technology and innovation.

With core competencies in delivering cost-effective solutions and services in areas such as:

� Advanced analytics

� Agile digital transformation

� Artificial intelligence

� Cyber security and information assurance

� Engineering and logistics

� Fraud, waste and abuse detection

� Hyperconverged infrastructure

Coupled with a refined focus on strategic partnerships, NCI is committed to bringing commercial innovation to missions of critical importance.

Headquartered in Reston, Virginia, NCI has approximately 2,000 employees operating at more than 130 locations worldwide.

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For More Information, Contact:NCIcontactnci@nciinc.comwww.nciinc.com

About the Author Michael Ezzo is the Director of Software Engineering for NCI’s Agile and Analytics (AAS) Sector. He is directly responsible for the design, development and implementation of software solutions to maximize business value for NCI federal customers. NCI currently supports CMS healthcare programs in 38 states with a staff of more than 700 professionals.