Emad Rizk

Digital therapeutic devices that manage chronic conditions with the touch of a screen. Artificial intelligence tools that detect kidney disease before clinical symptoms develop. The use of genomics to diagnose and treat rare conditions in children. These advancements and more are catching the attention of executives across the healthcare industry-but as dazzling as these discoveries are, for the most part, the experience of healthcare remains rooted in practices that were designed decades ago. What is holding the industry back? Data fragmentation is largely to blame, including a lack of robust longitudinal data and a single patient identifier.

Health plans, providers, and life science organizations are sitting on decades of data that are siloed in claims adjudication systems, electronic health records, and research documentation. Achieving next-level value in healthcare requires that health plans and providers mutually leverage these rich sources of data not just to deliver high-value care, but also to reduce administrative expense, which accounts for 

% of total healthcare expenditures. It’s also an extremely effective way to eliminate fraud and waste.

In 2020, the most disruptive moves in healthcare will be fueled by a commitment to achieving true interoperability across providers, payers, and all stakeholders. This will also involve sharing financial data from claims, clinical data from electronic health records, pharmacy, laboratory, demographic, and social determinant data to gain a comprehensive, longitudinal view of patients and providers that improves quality of care.

Ramping up risk stratification and detection of rare disease with artificial intelligence

. A subset of AI called deep learning combs through multiple data sources to predict health risks in real time, such as the risk of 

. While risk stratification isn’t a new concept, the potential to use AI to speed delivery of life-saving interventions in behavioral health, substance abuse, chronic disease, and more is strong in 2020-especially if health plans and providers stay committed to sharing clinical and financial data to support intelligent analysis. One study shows AI can accurately predict the risk of hospitalization and associated costs by identifying complex relationships among 

“large, sparse, high-dimensional, and noisy data.”

 The impact: significant reductions in cost and the resources required to identify individuals for intervention.

Top Healthcare and IT Predictions for 2020 and Beyond

The coming year will also see more extensive moves toward using AI to identify rare diseases by spotting patterns of genetic variants that spur disease development. This is an area with strong potential for improved care management and reduced costs, given that 

of rare diseases have a genetic origin, yet it takes 4.8 years, on average, to 

. One study from Germany found researchers could use AI to more quickly detect 105 rare diseases in children by 

 as well as the child’s clinical symptoms and genetic makeup.

Moving from retrospective to prospective payment review

. Payment integrity will take center stage in 2020 as health plans seek to minimize the provider abrasion that can result from payment recovery. This is especially true in an era of consolidation, where health plans that do not address the root causes of payment problems-such as lack of visibility into payer contract terms, ambiguous payment policies, and inability to access benefits and eligibility information in real time-are 

. Payment integrity also supports greater alignment around value-based compensation and ensures members are billed the correct amount, strengthening provider and member satisfaction. For health plans, the financial return on investment in moving from retrospective to prospective payment review is also strong: 

 are overpaid by health plans, and the cost of retrospective recovery can total $25 per claim.

Robust information sharing strengthens the ability to leverage data analytics to identify common patterns that trigger claim payment review before claims are paid. For example, lack of CPT codes that have a one-to-one match with specific genetic tests complicates health plans’ ability to 

adjudicate and pay genetic testing claims

. This presents an opportunity for provider education that not only supports clean claim submission, but also reduces the administrative time associated with claim review and speeds payment-a win-win for health plans and providers. In 2020, many leading health plans will also invest in advanced analytics tools that support near-time clinical claim validation for more prompt communication between payers and providers as well as tools that ensure benefits information is up-to-date and easily accessible by providers.

Building comprehensive, longitudinal data sources

. The data needs of payers, providers, and life science organizations are significantly converging. Health plan needs are evolving in response to industry changes, with factors such as shifts to 

 driving demand for data-sharing across segments-and specifically innovation in data analytics. Providers are becoming risk-bearing entities. Life science companies need real world data for clinical research. As data becomes increasingly diverse and complex, accessing and analyzing disparate data sets is critical. Furthermore, the next stage of value-based reimbursement will require uniting provider and payer data to achieve a longitudinal view across payment appropriateness, population risk assessments, and quality outcomes measurement. In 2020, new tools will emerge that unify data from across the healthcare ecosystem and leverage AI-driven analytics to deliver actionable insights. Combining clinical, financial, and social determinants data, in both structured and unstructured formats, will be imperative for unlocking better and richer member insights with more complete, historic views.

Bringing together provider and payer data will be key for breaking down siloes and supporting greater partnership among health plans, providers, and other stakeholders. Uniting these data sets offers a shared, 360-degree view of each member’s health, allowing both parties to collaborate more effectively around care decisions to close gaps in care and optimize cost and quality. Combining clinical and financial data could also create new capabilities to better utilize social determinants of health (SDOH) data. 

 predict that in 2020, 40% of health systems, life science companies, and commercial payers will leverage SDOH in some way. However, as most SDOH data is generated outside of the healthcare space, these enhanced data aggregation and analysis capabilities will be critical for marrying myriad data sets and building a comprehensive view of each member throughout their healthcare journey.

In 2020, collaborative data sharing will fuel some of healthcare’s biggest advancements while protecting health plans, providers, pharmaceutical companies, and members from unnecessary expenses. By partnering to leverage data and analytics to address healthcare’s most complex challenges, healthcare leaders can accelerate care delivery transformation and improve value for all.

Emad Rizk, M.D., is chairman, president and CEO of 

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In 2020, the most disruptive moves in healthcare will be fueled by a commitment to achieving true interoperability across providers, payers, and all stakeholders.

Health IT in 2020: 3 Trends That Will Disrupt the Market

Systematic collaboration between payers and providers is the key to providing better care at lower cost in the United States. Looking back at the past decade, once-emerging technologies such as electronic health record systems (EHRs), computerized physician order entry (CPOE), and auto-authorization and adjudication showed promise. The goal was to break down communication barriers between payers and providers and help everyone see the same comprehensive data about a patient’s health status. When this occurs, decisions about the patient’s best care-from both clinical and financial perspectives-are more easily and accurately made.

Now fairly mainstream, have these technologies fulfilled that promise? While they have succeeded in creating more digitized, shareable information than ever before, payer-provider collaboration is still thwarted by information silos that exist between organizations-even within the four walls of a single payer or provider organization.

The imperative for this collaboration has never been stronger, and new incentives have appeared to encourage both payers and providers to make better partnership a priority. For example, the National Committee for Quality Assurance (NCQA), which administers the HEDIS measure set that encapsulates the payer industry’s standards for quality improvement, is pushing hard for payers to incorporate electronic clinical data in their quality reporting. Providers, meanwhile, can increase their revenue by participating in value-based reimbursement programs such as the Merit-based Incentive Payment System (MIPS) and Advanced Alternative Payment Models (AAPM); successful participation in such models demands clinical and financial information-sharing with payers.

As we look out over the healthcare technology horizon, what tools are now the focus, and how do they solve some of the challenges of the past? Technology solutions supporting payer-provider collaboration fall into two primary categories: those that allow payers and providers to securely collect accurate and diverse types of data, and those that allow healthcare organizations to turn that data into collaborative platforms. With the resources, scale, and potential to see the most return on investment from these technologies, payers must lead the way in their implementation.

Breaking down the barriers to collaboration

With claim systems being some of the first data management technologies in healthcare, claims data has been a reliable source of truth for several decades. By examining a member’s claim history, a payer can quickly determine which providers a patient has seen and which procedures and tests have been performed, providing a complete claims picture across the care continuum.

Claims data offers some clinical information but not deep insights into the patient’s clinical status, care, and outcomes. Some of this gap has been filled through the proliferation of provider systems such as EHRs, but the challenge remains in gathering the longitudinal perspective of each person’s care as well as cost across the continuum over time. Did the patient receive the right treatment? Was the treatment cost effective and appropriate? Were the clinical and financial outcomes of the treatment expected and beneficial? Unstructured clinical data as well as the continued prevalence of paper-based information make these questions difficult to answer.

Therefore, the industry is now turning to tools such as natural language processing (NLP), which uncovers clinical and unstructured data to improve healthcare decision making. Organizations are also using optical character recognition (OCR) to bring more paper-based information into the digitized fold.

In addition, payers and providers both are increasingly recognizing social determinants of health, such as socioeconomic status, environment, and geographic location, for their importance in addressing an individual’s care needs holistically. Several large payers recently created the Aligning for Health (AFH) coalition with the goal of eliminating barriers to addressing these factors, noting that up to 90% of health outcomes are determined by them. Mechanisms for collecting and sharing this information are still fairly new but hot topics of discussion in healthcare organizations of all types. For example, health information exchanges (HIEs) enable social services providers to feed social determinant data directly into EHR systems, giving physicians a much broader view of the problems their patients face.

The good news is that as data has proliferated, the technologies for data management and integration have advanced as well, giving organizations the ability to effectively handle both massive quantities of data and diverse data types with equal ease. For example, a data lake, or centralized data storage repository, enables a health plan to assemble many disparate sets of data for different uses across its enterprise. And since the data can remain in its native format, it can be accessed at greater volume and speed than with a traditional enterprise data warehouse.

Data security remains a concern, with troubling data breaches unfortunately remaining a regular news item. However, the industry has made major investments in data security, with organizations such as HITRUST setting the standard for healthcare organizations to follow. A decade ago, data security concerns often prevented collaboration; today, organizations have a clearer path forward.

Meanwhile, blockchain technology is enabling consumers to securely share and even monetize their own data. Empowered by a requirement in the 21st Century Cures Act to require EHR systems to have open application programming interfaces (APIs), a new marketplace has opened for consumers to sell their own data directly to health data brokers.

Simply put, the reasons organizations have traditionally had for not sharing data with each other are breaking down, opening a new type of conversation between payers and providers.

Why is this relevant and important? Because when payers have clinical and financial data as well as the ability to analyze that data at an enterprise level, they can achieve a more comprehensive, 360-degree view of the value and cost of care.

However, that information still must be served up in such a way that it compels action. Analytic tools to pinpoint insights from data have been used by healthcare organizations for several decades. For much of that time, however, these tools have required a high degree of technical expertise to operate, making it difficult to extract insights that can be consumed by multiple disciplines and turned into tactical strategies for patient and provider outreach and communication.

To make insights more rapidly or even proactively accessible across their enterprise, payers need persona-based data visualization tools that adapt to the end user’s areas of concern and level of expertise. The chief financial officer and the chief medical officer both need to access the same data, but from different perspectives. Providers need tools such as workstations with single sign-on (SSO) technology that enable them to access insights as rapidly as possible at the point of care, so that their attention is not divided between the patients they are seeing and the technology they are using to help them.

The greatest challenge, however, is finding the right tools that work with the same set of data to enable true interaction with providers. Sharing data and insights is a good start, but two-way communication about what to do with those insights is what will drive down the cost of care while improving outcomes. This is truly the next frontier in healthcare.

Emad Rizk, MD, is president and CEO of Cotiviti.

Reasons organizations have traditionally had for not sharing data with each other are breaking down, opening a new type of conversation between payers and providers. Here’s how.

Author | Emad Rizk

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Health IT in 2020: 3 Trends That Will Disrupt the Market

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