Readers Write: Healthcare Needs a Data Liquidity Disruption

Healthcare Needs a Data Liquidity Disruption
By Sriram Devarakonda

Sriram Devarakonda, MSEE is co-founder and CTO of Cardamom.

Healthcare has long promised that data would transform research, precision medicine, and patient outcomes. Yet progress remains painfully slow. Data silos and fear-driven restrictions keep critical information trapped in systems that were designed more to contain than to share.

Real transformation in targeted care, population health, and clinical research won’t come from yet another interoperability initiative or API. It requires a more fundamental shift: a data liquidity disruption that treats data as something meant to move, not sit still.

What’s holding healthcare back?

Healthcare’s challenges have evolved dramatically over the past three decades, and they will continue to change just as profoundly in the decade ahead. Thirty years ago, the priority was basic connectivity: enabling continuity of care across disparate systems through point-to-point integrations, with HL7 playing a foundational role.

Ten years ago, the rise of web and mobile technologies demanded a modernized approach to interoperability, giving rise to newer API-based standards, such as FHIR, that enabled digital health innovation.

Today, and looking forward, the focus has shifted yet again. Healthcare’s most pressing challenges, from cancer to diabetes to Alzheimer’s, require the effective use of data and AI at scale, challenges that impact millions of lives and drive national healthcare costs. Solving them demands more than messaging standards alone. Our future cannot depend on HL7 and FHIR by themselves. It requires true data liquidity, real-time intelligence, and platforms that are designed for learning health systems.

Before we delve into how we prepare for the future, we should look at a few reasons that data liquidity is a challenge today.

• Proprietary mindsets. Healthcare systems and vendors have long viewed data as an asset to guard, not a resource to share. Competitive, contractual, and legal anxieties create barriers that go beyond technology. They are cultural and structural.
• Fragmented data standards. Despite progress with HL7 and FHIR frameworks, true standardization remains elusive. Data formats, definitions, and governance models still vary widely, making even “standard” exchanges complex and time-consuming to implement.
• Privacy and compliance fears. With HIPAA, GDPR, and a growing patchwork of state regulations, organizations often err on the side of caution. The result is a compliance-first posture that, while understandable, often stifles innovation and progress.
• Legacy infrastructure. Many health systems are still operating on decades-old IT foundations that were designed for billing and clinical care, not for modern data exchange. Retrofitting these systems to support real-time data liquidity is costly and complex.
• Sheer complexity of technologies. A large barrier to progress is the sheer number of different technology systems even within the same ecosystem. EHRs, ERPs, and countless vendor-managed applications add an additional layer of complexity that’s challenging to overcome.

Why a disruption is inevitable and necessary

Healthcare’s approach to data is slowing progress. Patients want connected experiences, researchers need faster access to data, and providers and payers are under pressure to deliver better outcomes.

Other industries already allow data to flow securely in real time, enabling smarter decisions and personalization. Healthcare must make the same shift, from owning data to stewarding it, and from locking it away to sharing it responsibly. Those who adapt will lead; those who don’t will fall behind.

Preparing for the data liquidity era

How can healthcare organizations prepare for the inevitable disruption?

• Invest in platforms, not point solutions. Healthcare systems must invest in modular, cloud-based platforms that allow for data to move freely and securely. That means creating enterprise-shared data access on modern data platforms that can evolve alongside transactional systems that are not frozen in time.
• Embrace interoperability as a strategy, not a checkbox. Compliance-driven interoperability creates connections, not capability. Treating data sharing as a strategic asset is what turns exchange into impact, fueling innovation, partnerships, and better care coordination.
• Move from data control to data accountability. As data moves more freely, data maturity becomes even more critical. Clear standards for data quality, consent, and usage help ensure that liquidity doesn’t come at the expense of privacy or ethics. AI has a large role to play here when it comes to interpretation and standardization.
• Standardize clinical workflows. The more healthcare organizations can standardize their clinical workflows and protocols now, the fewer challenges they will have later. Clear, consistent processes make it easier to adopt new tools, train staff, and share data safely.
• Align data strategy to business and clinical outcomes. Data liquidity drives real, downstream impact on both business and clinical outcomes. When tied to clear, measurable goals, such as reducing denials, accelerating clinical trial enrollment, or improving patient throughput, it becomes a powerful, provable source of ROI.
• Reimagine the patient’s role. Patients are no longer passive data points; they are active and willing participants. Giving them control over their health data and the ability to share it across providers, researchers, and care teams will accelerate innovation while fostering transparency, trust, and improved outcomes.

The ripple effects of data liquidity

When healthcare achieves true data liquidity, the impact will be profound. Researchers will be able to identify patterns across populations in days, not years. Providers can make more informed decisions at the point of care. Health systems will predict and prevent crises before they occur. Most importantly, patients will benefit from a system that understands them as whole individuals, not just episodes of care that are scattered across disconnected databases.

Healthcare is long overdue for the same data transformation other industries have already embraced, one that allows data to move freely, connect seamlessly, and create value wherever it goes.

The road to disruption won’t be easy, but it is necessary. The barriers to data movement have been standing for too long and the cost of inaction is too high.