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

March 7, 2020
Emad Rizk

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

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 8.3% 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.

Here are three trends to watch:

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 premature death due to chronic disease. 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.

Related: 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 80% of rare diseases have a genetic origin, yet it takes 4.8 years, on average, to obtain a diagnosis. One study from Germany found researchers could use AI to more quickly detect 105 rare diseases in children by analyzing a portrait of a child’s face 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 at risk of losing provider contracts. 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: 3% to 7% of commercial claims 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 managed care and value-based contracts, consumerism, and market consolidation 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. Frost & Sullivan analysts 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.

A Game Changer for Value

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 Cotiviti.

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