Rajan Kohli, CEO of CitiusTech. Inspiring new possibilities for the health ecosystem with technology and human ingenuity.
Most healthcare leaders I meet are investing heavily in new data systems and AI. Many of these initiatives will underperform or fail because they haven’t addressed two fundamentals: trust and clinical context.
The challenge isn’t technology. It’s a trust gap.
As technology advances, traditional controls for building intelligent systems are falling away. Today, there is no universal language for trust.
Promising new systems fail because users don’t trust them. Care teams, claims processors and even clinical directors verify everything manually because no one is willing to gamble on incomplete or misleading data when lives and compliance are on the line. It adds to the cognitive burden that the new system had promised to reduce in the first place.
Multiply that hesitation across thousands of transactions, and you understand why so many pilots look good in a demo but never scale. And it’s exactly why trust has to be engineered, not assumed.
Trust is measurable if you build for it.
I’ve learned that traditional analytics metrics like precision, recall or F1 simply don’t capture the risk. Healthcare needs its own standards, and organizations need to quantify trust through hard questions:
• Is the output faithful to the underlying medical record, down to each coded diagnosis, symptom or treatment (clinical match scoring)? If a patient’s chart lists five critical conditions and a summary only captures three, that means 40% of essential clinical data is missing.
• Does it catch the nonnegotiables (agreement scoring)? For example, if a patient is allergic to penicillin, does the system report that fact or miss it?
• Is it factual or making assumptions (hallucination scoring)? Does it provide answers strictly adhering to the context provided? Or does it make a few things up? Does it provide the fidelity a workflow needs? Would it support clinical decisions with verbatim accuracy?
• Is it consistent (consistency scoring)? Will processing the same patient file 10 times yield the same core conclusions?
• Can it cite the source accurately for the response generated (citation scoring)? Are the responses verbatim to source where the answer is derived from? What are the use case specific thresholds of citation accuracy?
• Is the language used for generating responses consistent with a certain style and word precision of a clinical guideline (language scoring)?
Many modern data platforms tolerate more unstructured, unclean data, making these guardrails matter even more. Without them, speed just amplifies risk. When you hardwire these checks into your processes, front-line teams see the difference and they begin to trust the system enough to stop double-checking every output.
Context is the second barrier.
Even with robust trust metrics, organizations can’t succeed if AI systems don’t understand their clinical, regulatory and operational context. Healthcare decisions aren’t simple question-and-answer transactions; they’re multistep judgments grounded in local pathways, specialty guidelines, payer policies and embedded clinician knowledge.
Much of the technology now deployed was never designed for this. You can build the best general-purpose engine in the world, but if it doesn’t understand your clinical and regulatory nuances, it’s going to disappoint. I keep seeing the same failures: models that handle text beautifully but miss why a cardiology protocol differs from an oncology pathway; automations that look clean on a dashboard but stumble when they encounter a subtle reimbursement rule.
Many teams have tried bolting on traditional search and retrieval architectures, hoping that would solve the context problem. It hasn’t. Healthcare carries too much implicit context for retrieval alone to close the gap.
The way forward is through codifying your expertise.
This is why I talk about knowledge platforms. It’s not just a technical term; it’s how you make your organization’s expertise machine-consumable. That means taking clinical pathways, specialty guidelines and local compliance rules that today live in PDFs and expert minds and turning them into structured and governed knowledge assets.
Some health systems are already feeding cardiac care guidelines into systems that build dynamic knowledge graphs, so next steps surface inside the EHR without a physician having to search. Others are encoding local approval rules, so their summaries align with payer criteria right out of the gate.
The important distinction is to build and plug them in a way that enables smart contextualization inside of the workflows, without the need to search or jump to another workflow.
It’s slower up front, but it can pay off in fewer denials, safer care and less wasted time second-guessing systems that seem like black boxes.
The enterprise difference involves embedding trust and context by design.
None of this works at scale without a deliberate shift in how organizations govern these systems. In my experience, organizations mature through clear stages.
At first, projects are ad hoc. Everyone builds their own solutions, often relying on vendor assurances. Eventually, organizations move to defining internal metrics, creating playbooks for clinicians, engineers and data scientists, and understanding what each guardrail does and doesn’t do.
Then comes embedding these trust and context measures directly into every new AI project. At this point, clinical match scoring, agreement checks, hallucination controls and consistency expectations become entry criteria. If a proposed system can’t prove it meets your standards, it doesn’t launch.
At the highest levels, organizations are continuously responsive. They use feedback from users, metrics from production systems and automated checks to adapt. Because trust isn’t static; it’s something you uphold daily.
What is the real measure of success?
Any organization can buy the latest data platforms, but not all will put in the work to codify their knowledge, build playbooks and engineer trust at scale.
Finding success means establishing enterprise-wide standards for what trust looks like and measuring it in ways that go beyond generic “accuracy.” It means building systems that think like your best people, not like a generic training dataset. And it means pushing past pilots to embed these checks into every project, with guardrails that evolve as your needs and risks change.
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