Artificial intelligence is already making its mark on healthcare — streamlining administrative tasks, sharpening diagnostic accuracy, and improving clinical workflows. Yet for all its promise, AI in healthcare remains largely fragmented, with tools operating in isolation rather than as part of a connected, intelligent system.

Ayush Jain, CEO of Mindbowser and author of The Zero Hiccup Way, believes the next leap forward lies not in building smarter individual tools, but in weaving them together into a truly interoperable ecosystem — one where data, insights, and decisions flow seamlessly across patients, providers, payers, and institutions.

In this conversation with Rajeev Choudhury of Drug Today Medical Times, Jain explores what it will take to move from siloed AI to systemic intelligence: the governance structures, architectural principles, and cultural shifts required to make it a reality. From FHIR standards and HIPAA compliance to coaching the next generation of health-tech founders, he offers a grounded, strategic vision for a healthcare future where technology works quietly in the background — and patients and clinicians reap the benefits.

Standalone AI tools have delivered some wins in diagnostics and imaging — so what's fundamentally missing that makes you say the real revolution requires interoperability at the ecosystem level?

There’s no denying that independent AI tools have resulted in quantifiable improvements in areas like administrative workflows, medical imaging and diagnostics. But the basis of healthcare is not autonomous functions. Effective care requires patients, providers, payers, devices and clinical systems to work together as part of a complex network.

The problem is that many AI solutions today only work in one department or use case, which limits the impact of the insights they provide. “The real potential is to bring these systems together so that information can flow easily across the healthcare ecosystem.

Interoperability allows clinicians to share and act on AI-driven insights in real time, providing a more holistic view of the patient and better coordination across care teams.

How do you define an 'interoperable AI ecosystem' in healthcare — and where does it begin and end in terms of stakeholders, data sources, and decision layers?

An interoperable AI ecosystem in healthcare is a connected framework where data, systems and AI models can securely exchange information and generate insights across the entire care continuum. It is not about deploying individual AI tools, but about creating a unified intelligence layer that enables better decisions at every stage of healthcare delivery.

This network starts with the patient and includes everyone involved in their care, like doctors, insurance companies, and researchers. It uses information from many places, such as medical records, health apps, and wearable devices.

Interoperability is useful in many ways, from helping doctors plan treatments to making hospitals run more smoothly and handling insurance claims faster. To make this work, there must be clear rules and strong security to ensure patient data is shared safely and that everyone can trust the system.

From your experience at Mindbowser driving digital transformation, what are the three biggest architectural mistakes health systems make when trying to connect disparate AI tools?

One of the biggest mistakes is creating AI solutions in silos. Many health systems are implementing individual AI tools for diagnostics, workflows, documentation or patient engagement without building a unified data architecture underneath. This results in broken systems that cannot properly communicate with each other.

The second error is to ignore interoperability standards from the start. If AI systems are not designed from the start with standardised healthcare protocols and APIs, they are expensive, slow and operationally disruptive to integrate later.

The third big mistake is to only look at model performance and ignore governance and workflow integration. Even the most accurate AI tools are useless if they are not embedded into clinical workflows, supported by appropriate data governance, security controls and clear accountability around decision making.

Interoperability requires unprecedented collaboration between hospitals, payers, pharma, and regulators — how do you build the trust and governance structures that make that even possible?

To build interoperability at scale, you must first implement clear governance frameworks before deploying technology. All stakeholders - hospitals, payers, pharma companies and regulators - need defined rules around data ownership, access, consent, accountability and compliance. Without that foundation, collaboration quickly falls apart.

The second critical issue is the utilisation of common data-sharing standards and transparent audit processes. Stakeholders are more likely to collaborate when they have a clear view of how data is used, by whom, and whether such uses are compliant with regulatory and ethical requirements.

And most importantly, trust is built when interoperability delivers value for all involved. Hospitals need better clinical outcomes, payers need operational efficiency, pharma companies need research insights and regulators need compliance visibility. 

The ecosystem works only when governance structures balance innovation with security, privacy, and shared accountability.

 In your book 'The Zero Hiccup Way,' you talk about scaling technology companies without friction — how does that philosophy translate to rolling out an AI ecosystem inside a risk-averse healthcare institution?

The philosophy of The Zero Hiccup Way is that scale happens when you remove friction, not add complexity. In healthcare, that means AI should fit naturally into existing clinical and operational workflows rather than force people to change how they work.

For risk-averse institutions, the key is to start with low-risk, high-value use cases, keep humans in the loop, and build strong governance around security, compliance, and transparency. When AI consistently saves time, improves outcomes, and earns trust, adoption follows naturally and the ecosystem scales without disruption.

Data privacy and HIPAA compliance are already hard with one AI tool. How does the risk and compliance burden change — and how should it be managed — when you're orchestrating a full ecosystem?

The complexity of privacy, security and compliance inevitably rises as one AI application grows into an interconnected ecosystem. More systems, data exchanges and stakeholders create additional points that must be governed and monitored. But the solution is to build interoperability on a solid foundation of governance rather than limiting it. 

Role-based access controls, encryption, audit trails, consent management and ongoing system monitoring are all key components of a company's security-first strategy. Instead of being introduced later, compliance should be incorporated into the system from the start. Clear data governance guidelines and standards-based integrations also aid in ensuring that data is shared only when required and with the proper security measures in place.

We at Mindbowser think that trust is necessary for innovation in healthcare. In addition to ensuring adherence to laws like HIPAA, a well-managed ecosystem can make the environment safer and more open for patients, providers and healthcare institutions.

Standards like HL7 FHIR have been around for years, yet true interoperability still feels elusive. What's changed in AI capabilities or market conditions that makes you believe we're finally at an inflection point?

Standards like HL7 FHIR have laid the groundwork for interoperability, but standards alone cannot create connected healthcare ecosystems. Organisations have been working for a long time to get basic data exchange and digitisation of records in place. This is different now. The increasing pressure on healthcare organisations to deliver better outcomes, cut costs and address workforce shortages is building a stronger business case for seamless data sharing.

At the same time, connected data has become much more valuable owing to advances in AI. In ways that were previously unattainable, contemporary AI models are able to analyse data from various sources, find trends, produce useful insights and assist in decision-making. As an outcome, interoperability is now a strategic requirement rather than just a compliance goal. Healthcare organisations are finally able to transition from disorganised systems to truly connected, intelligent care delivery due to the convergence of more capable AI, cloud-based infrastructure and established data standards.

How should healthcare organizations measure ROI when the value of interoperability is often systemic and emergent — rather than tied to a single tool's output?

The biggest mistake is to judge interoperability by the performance of one tool or application. The real value of interoperability is how well the whole healthcare ecosystem works.

Healthcare organisations should focus on outcomes like reduced clinician workload, fewer duplicate tests, faster access to patient information, better care coordination, and an improved patient experience. These metrics tell us whether interoperability is making a tangible difference.

Interoperability is, in many ways, like building roads, not cars. Its value is not only in the infrastructure itself, but in the speed, efficiency and innovation it will enable across the healthcare system. The ROI ultimately is seen in better decisions, better outcomes and a more connected patient journey.

As a TEDx speaker and mentor, how do you coach health-tech founders who want to build for interoperability from day one, rather than bolt it on later when it's costly?

I encourage health-tech founders to think of interoperability as a product strategy, not a technical feature. If you design around open standards like FHIR from day one and build with integration in mind, you avoid expensive rework later.

My advice is simple: start with the workflows and data exchange requirements of providers, payers, and patients early on. The companies that win in healthcare are often not those with the most features, but those that connect seamlessly into the existing healthcare ecosystem. Interoperability should be part of the foundation, not an afterthought.

Looking 10 years ahead — what does the 'end state' of a truly interoperable AI healthcare ecosystem look like for a patient, a clinician, and a health system administrator on an average Tuesday?

In 10 years’ time, a truly interoperable AI healthcare ecosystem should be almost invisible to the people using it.

For patients, it would mean not having to give their medical history, test results or insurance information over and over again. Their health data would be portable from provider to provider, enabling more personalised and proactive care.

For clinicians, that would mean less time navigating systems and more time caring for patients. AI will provide the right information, insights and recommendations at the point of care to help them make faster and better informed decisions.

That would give health system administrators real-time insight into operations, resources and patient outcomes, and allow them to optimise care delivery more effectively.

Ultimately, the end state is not more AI or more technology, it is a healthcare experience where data, intelligence and workflows work together seamlessly in the background, making care more connected, efficient and patient-centric.