Every day, tens of thousands of patients seek care for new or existing conditions. Behind the scenes, a complex network of information about health records, benefits, coverage, eligibility, authorization, and other aspects plays a crucial role in the type of medical treatment patients will receive and how much they will spend on prescription drugs. This results in large amounts of data being produced, stored, and exchanged every second, which also leads to inefficiencies and gaps in access between patients, providers, and payers due to inconsistencies in the implementation of healthcare data interoperability standards. These inefficiencies contribute to the increasing waste in the US healthcare system and challenges in delivering cost-effective quality care.
For over 20 years, the industry has been discussing how to address this challenge without a clear resolution. In 2020, the Centers for Medicare and Medicaid Services (CMS) published a rule requiring healthcare systems to enable easy exchange of information between patients, providers, and payers. This rule laid out an interoperability journey that supports seamless data exchange, enabling future functionalities and incremental use cases. Since 2021, healthcare insurance companies, also known as payers, have the obligation to comply with the interoperability requirements set in 2020. These requirements enable the exchange of important data between healthcare payers and providers. Establishing a clear interoperability framework is foundational to enabling administrative simplification, one of the provisions of the Health Insurance Portability and Accountability Act of 1996 (HIPAA). This provision aims to reduce paperwork and streamline business processes across the healthcare system, leveraging technology to save time and money. Given the high levels of burnout among physicians and clinicians, this provision is timely and relevant.
Combining data interoperability with artificial intelligence (AI) has the potential to bring about transformational changes to the US healthcare system, creating a real-time interconnected ecosystem. Data interoperability is imperative because it enables all stakeholders in the healthcare ecosystem to easily exchange information, allowing payers and providers to partner together and deliver high-quality, cost-effective care. The return on investment (ROI) resulting from these efficiencies can be significant for healthcare payers. However, realizing the benefits of interoperability can be challenging due to the number of requirements and standards that need to be assessed and complied with, including the implementation of the Fast Healthcare Interoperability Resources (FHIR) standard.
CMS recognizes the importance of FHIR in advancing interoperability and reducing administrative burden. Adoption rates and enterprise architecture implementation patterns for FHIR vary across the industry. IBM offers a four-level maturity framework for interoperability implementation. The first level focuses on integrating data from disparate healthcare sources to create the initial Longitudinal Patient Record (LPR). The second level expands the capabilities of the FHIR data platform to perform calculations for Data Exchange for Quality Measures (DEQM). The third level breaks the existing silos in the healthcare system and promotes combining physical health and behavioral health silos. The fourth level supports the five key provisions to improve health information exchange, including the automation of manual processes.
Interoperability is crucial in transforming prior authorization, a process implemented by healthcare payers to address high-cost medical procedures and medications. Real-time information exchange between payers and providers is necessary to ensure that the care being provided is medically necessary and compliant with clinical quality guidelines. However, inconsistent adoption of interoperability standards, physician burnout, and delays in obtaining approvals have caused friction among stakeholders. AI, particularly machine learning and natural language processing, can automate the process of validating medical necessity and compliance with guidelines. Generative AI offers further capabilities in handling unstructured data. Challenges remain in how clinical data is captured and stored, as well as in how medical necessity criteria and clinical quality guidelines are created and stored.
IBM offers a comprehensive strategy and approach to guide healthcare clients in driving value through end-to-end digital transformation. The company leverages existing investments in IBM technologies and software development capabilities to fill gaps and provide customized solutions. Partnering with IBM can help payers and providers execute successful interoperability and prior authorization transformations, bringing together the best of market offerings with IBM’s technology and consulting capabilities.
