Written by Rikin Mathur
Interoperability is a critical healthcare issue and a term frequently mentioned in industry discussions. At TMV, we understand the vital importance of interoperability across the ecosystem. Many companies in our portfolio leverage this capability to enhance care, automate workflows, and achieve various other goals. Before delving deeper into the topic, let’s begin with an overview: what exactly is interoperability?
Understanding Data Interoperability
Interoperability is the capability of different systems or components to share information and effectively use that shared information to improve decision-making. Interoperability is not a novel concept, it’s been around for decades since data and computers gained prominence. Before 2020, interoperability in healthcare was restricted and faced significant challenges, including a lack of common data formats, vendor lock-ins in which EHR vendors created systems intentionally not interoperable with other vendors’ products to achieve exclusivity, and a lack of understanding among entities around sharing patient data effectively. However, recent developments have opened a large market for interoperability technology. On May 1, 2020, the Centers for Medicare & Medicaid Services (CMS) published the Interoperability and Patient Access Final Rule, prioritizing patient needs by providing timely and user-friendly access to their health information. This rule aims to enhance interoperability and patient access to health data. Most recently, the Office of the National Coordinator released their newest proposed rule affecting health IT developers and the broader healthcare ecosystem. The rule among many positive changes for EHR and healthcare software systems, allows APIs, third-party software, to better integrate with larger EHR systems and most importantly, helps better facilitate provider-public health exchange and provider-payer exchange.
But what does this mean and look like for patients? Imagine your own experience with healthcare data. You receive notifications from different systems: perhaps your primary care physician (PCP) uses Athena for check-up results and your cardiologist Epic for treatment updates. Maybe you’ve had imaging done, and your records are kept in a separate system, entirely. You’ve gotten vaccines through your PCP, but also at your local pharmacy. You use an Apple Watch or Oura ring to track fitness and sleep, adding yet another layer. Each system holds important health information but doesn’t communicate with the others, creating a fragmented and often frustrating experience. Interoperability aims to connect these systems, allowing seamless data sharing for a more cohesive healthcare experience.
Interoperability in healthcare ensures that information is accessible to authorized users without barriers, ultimately improving patient care and operational efficiency. As we will discuss more, much of the progress in interoperability has been because of the advancement of data engineering and AI. Innovation today is happening across multiple healthcare sectors including improving clinical decision-making through comprehensive data aggregation and analysis, reducing costs by minimizing redundant tests and administrative burdens, utilizing predictive analytics for proactive care management, and ensuring effective data exchange through automated testing and validation of system interoperability.
Reflective of the opportunity to drive meaningful innovation, funding for US-based health data infrastructure and interoperability startups reached $2.2 billion in 2021, nearly tripling the $736 million raised in 2020. Although funding has cooled since then, startups still raised $883 million in 2022, a nearly 20% increase over 2020, and $265 million in the first quarter of 2023.
The Opportunity
The time to build interoperability solutions in healthcare is now due to several converging factors. Governments and regulatory bodies are increasingly mandating interoperability as discussed above, which has opened several outlets such as standardized APIs and EHR integrations to allow data to flow freely. Technological advancements across cloud computing and generative AI provide groundbreaking tooling to manage the data and effectively contextualize it. The COVID-19 pandemic has accelerated the adoption of digital health solutions for better clinical trial design, more accessible and effective care, and rapid data exchange, all of which can be highly improved by interoperability. Furthermore, the exponential growth of healthcare data necessitates seamless integration and analysis to provide holistic care and better clinical decision-making. Currently, about 30% of global data is produced by healthcare. By 2025, the data growth rate in this sector is expected to hit an annual compound rate of 36%. This growth is 10% faster than financial services, and 11% faster than media and entertainment.
The importance of interoperability has surfaced both on a commercial level and with individuals. 58% of health systems are now supportive of broad and open health information sharing under several permitted purposes, including treatment, payment and operations, and research.
Different Levels of Interoperability
Healthcare data encompasses various types, including clinical data (EHRs, lab results, medical imaging, patient history, prescriptions, and clinical notes) used for diagnosis, treatment planning, monitoring, and research; administrative data (billing records, insurance information, appointment schedules, and patient demographics) supporting operations like billing and scheduling; claims data (insurance claims and reimbursement records) for processing claims and detecting fraud; pharmacy data (medication prescriptions and dispensation records) tracking medication use and interactions; patient-generated health data (wearables, home monitoring devices, surveys, and self-reported information) for chronic condition monitoring and patient engagement; and public health data (disease registries, immunization records, and epidemiological data) for tracking health trends and planning interventions. Data flows within healthcare providers via internal networks and EHR systems, between providers through Health Information Exchanges (HIEs), to and from patients via portals and devices, to payers for claims processing, to public health agencies for monitoring, and for research to improve care quality and efficiency.
Let’s take a deeper look to understand interoperability. There are four levels in healthcare on which interoperability exists:
Foundational Interoperability: This basic level allows one system to send and receive data from another, but the receiving system might not interpret the data meaningfully. While data transfer is accurate, its utility is limited without further processing or translation. An example is Cerner, which enables basic data exchange between healthcare providers, ensuring that patient data can be shared for functions such as lab results and medication orders.
Structural Interoperability: Ensures that data exchanges between systems can be interpreted at the data field level, preserving the structure and format of data (e.g., HL7, CDA). An example of a company driving structural interoperability is Cohere Health, which improves the prior authorization process by ensuring data is exchanged in standardized formats and common medical terminologies.
Semantic Interoperability: The highest level of interoperability enables systems to exchange, interpret, and use data effectively with standardized terminologies and data formats. Tempus exemplifies this by using AI to standardize and integrate clinical and molecular data, ensuring accurate interpretation for personalized cancer treatment and improving patient outcomes.
Organizational Interoperability: Involves governance, policies, social, legal, and organizational considerations to facilitate secure and seamless data exchange among entities. Innovaccer achieves this by creating a unified data platform that integrates information across hospitals, clinics, and insurers, standardizing and harmonizing data, ensuring regulatory compliance, and enabling coordinated care through AI-driven analytics.
The Role of AI: Current Interoperability Challenges in Healthcare, and How AI Can Overcome Them
Today, healthcare entities face several challenges when it comes to interoperability:
- Data Silos and Lack of Standardized Formats: Healthcare data often exists in silos across different departments and varied formats and standards limiting the potential for comprehensive data analysis and coordinated care.
- Privacy and Security Concerns: Interoperability initiatives must navigate strict privacy regulations like HIPAA. Ensuring that data is exchanged securely while maintaining patient confidentiality is a significant challenge.
- Technological Barriers and Legacy Systems: Many healthcare organizations use outdated systems that are not designed to interoperate with modern technologies. Integrating these legacy systems with new solutions can be technically complex and costly.
- Regulatory and Compliance Issues: Navigating the regulatory landscape is challenging as healthcare organizations must comply with multiple federal, state, and local regulations, which can vary significantly and change frequently.
AI is crucial in healthcare interoperability because it can integrate diverse data sources like electronic health records and medical imaging, ensuring seamless communication between systems. This technology also enhances decision-making and patient outcomes through machine learning and predictive analytics and can extract valuable information from unstructured data as well as automate routine tasks, reducing errors. AI can play a huge role in solving interoperability challenges in the following ways:
- Improved Clinical Decision-Making: AI algorithms can aggregate and analyze data from multiple sources, providing clinicians with comprehensive insights for better decision-making.
- Reduced Healthcare Costs: Efficient data exchange reduces redundant tests and procedures, leading to cost savings. Interoperability minimizes administrative burdens and enhances resource utilization, contributing to overall cost efficiency.
- Predictive Analytics and Risk Stratification: AI can analyze integrated data to predict patient risks and outcomes, enabling proactive care management. Predictive models can identify high-risk patients and suggest interventions to prevent complications.
- Interoperable Imaging and Diagnostics: AI can standardize and interpret medical images and diagnostic data, facilitating collaboration across different systems. Enhanced image analysis and sharing improve diagnostic accuracy and patient care.
- Interoperability Testing and Validation: AI tools can simulate data exchange scenarios to test and validate interoperability between systems. Automated testing ensures that systems can effectively communicate and share data as intended.
The Interoperability Market
We are seeing development and innovation in interoperability across multiple use cases and pain points that largely fall under the three domains: Insights, Infrastructure, and Clinical workflows.
The companies highlighted below solve issues on one or more of the interoperability levels highlighted:
- Data Platforms and Intelligence: Komodo Health, where I spent 5 years working across product and data science teams, provides a robust platform for healthcare data aggregation and analytics. Their platform integrates data from various healthcare sources, including claims, reference datasets, and clinical trials, to create a comprehensive and cohesive view of patients in the US, which the company calls the healthcare map. By leveraging advanced data analytics and machine learning, Komodo Health enhances data transparency and enables healthcare stakeholders to make evidence-based decisions.
- Longitudinal insights and improved care: Novellia, a TMV portfolio company, focuses on achieving semantic interoperability in healthcare by using AI to integrate and standardize diverse clinical, market research, and insights data. Their platform ensures that data from various sources is consistently interpreted and utilized, enabling comprehensive insights for patients. By leveraging this holistic and longitudinal view of a patient, consumers get the value of seeing all of their data across the health system to inform better care, and pharmaceutical companies achieve a better picture of how patients are using their medications, which leads to faster innovation cycles.
- Clinical Decision Making: IBM Watson Health offers a suite of AI-driven tools designed to support clinical decision-making and enhance patient care. Leveraging advanced analytics and natural language processing, IBM Watson Health can analyze vast amounts of medical data, including clinical notes, research papers, and patient records to deliver personalized treatment recommendations. These tools assist healthcare providers in diagnosing diseases, selecting appropriate therapies, and managing patient care more effectively.
- Data exchange and infrastructure: Rhino Health revolutionizes healthcare data infrastructure with its federated learning platform, enabling secure AI model training across multiple datasets without sharing sensitive information. By integrating seamlessly with existing systems like EHRs and imaging systems, the platform ensures interoperability and scalability to handle growing data volumes. Rhino Health prioritizes security and compliance with robust encryption and access controls, meeting healthcare regulations like HIPAA. This approach facilitates collaboration among healthcare providers and researchers, accelerating innovation and improving patient outcomes while maintaining strict data privacy standards.
Interoperability in healthcare is essential for enhancing patient care, improving operational efficiency, and reducing costs by enabling seamless data exchange, better clinical decision-making, and more personalized care. Despite challenges such as data silos, privacy concerns, technological barriers, and regulatory complexities, advancements in AI and machine learning are paving the way for more effective data integration and usage. As healthcare continues to evolve, the collaboration among providers, payers, patients, and technology vendors will be crucial in overcoming these challenges and driving forward a more connected and efficient healthcare ecosystem.
At TMV, we are actively investing in data interoperability tools serving each of the key stakeholders in this ecosystem. If you or someone you know is building in this space, please drop us a note.
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