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.
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.