Small Talk With Phan Manh Hung, CEO of Omi Health Tech

What is Small Talk?

Small Talk is our short Q&A with founders from the JetBrains Startup Program. They answer a handful of questions in their own words about what they’re building, why they started, and what they’ve figured out along the way. No pitch, no polish – just a two-minute read to meet the person behind the product.

This time, we’re talking to Phan Manh Hung, CEO of Omi Health Tech, a Vietnam-based company building data infrastructure that links the country’s hospitals to its Ministry of Health. He walks us through the move from a service-based to a product-based model, why the Java core is where the IDE matters most, and the regulatory update his engineers shipped in days rather than weeks.

Phan Manh Hung, CEO of Omi Health Tech

Omi Health Tech is the product brand of Ominext JSC and part of OmiGroup, a healthcare software group that has built systems for Japanese clients since 2012. Based in Vietnam, their team of around 10 engineers builds the Omi Health Platform (OHP), an HL7 FHIR platform that carries clinical data from hospitals up to the Ministry of Health, and Omi LGSP, which connects health-sector data to Vietnam’s National Data Exchange Platform. The team joined the JetBrains Startup Program in 2025 and built their Java core in IntelliJ IDEA Ultimate.

TL;DR

• Omi Health Tech grew out of OmiGroup’s 10+ years of experience building healthcare software for Japan. The company was founded about three years ago to build national health data infrastructure for their home market: Vietnam.
• Going from a service-based to a product-based model meant major refactoring. On top of that, they had to adhere to new data protection and AI laws. The Java core that maps clinical data is where the right IDE mattered most.
• With IntelliJ IDEA Ultimate, multi-file refactors became a normal part of the sprint, and the Java team completed part of an update to consent handling for patient-data access in days rather than weeks.

What does Omi Health Tech build?

We build foundational digital health platforms for Vietnam. The main one is the Omi Health Platform, which standardizes clinical data from hospitals, clinics, and labs and propagates it up to the provincial Departments of Health, and from there to the Ministry of Health’s national health record system. The second platform, Omi LGSP, connects that health data to Vietnam’s National Data Exchange Platform, so it can take part in cross-government public services. We’ve been writing this kind of software for Japanese clients since 2012, and about three years ago we established a separate product team to build for our home market. So this is really a shift from a services posture to a product posture.

What was the hardest part of that shift?

Honestly, mindset as much as code. People coming from the services side are used to shipping the customer’s spec. Product work needs more aggressive refactoring and real ownership of the architecture, and that shift is only easy when your tools make refactoring cheap and safe. On top of that, the regulation moved fast. Vietnam’s data protection law and a new AI law both came into effect within a few months of each other, so we had to update data-access layers, audit trails, and consent flows across multiple repositories on a very tight timeline.

Where have JetBrains tools made the biggest impact?

The core of our platform is a Java and Spring Boot layer that maps and transforms clinical data. That’s schema-heavy, refactor-intensive code in a regulated codebase, and it’s the part where a lighter editor leaves a lot of navigation and refactoring value on the table. Reviewers were spending more time than they wanted catching things a Java-aware IDE would have caught. So we gave that team IntelliJ IDEA. The Startup Program made that easy: no budget conversation, no quarterly re-justification. For everyone else, WebStorm and PyCharm are there for the engineers who want them. They’re available, not mandatory, and that has worked for us.

What features of IntelliJ IDEA are hard to find elsewhere?

Refactoring depth. If you rename a field that flows through our resource providers, custom mappers, and database entities, IntelliJ IDEA does it in one operation. Elsewhere, that same change is a careful sequence of finds-and-replaces. We use Find Usages and Type Hierarchy to check what a change could affect before touching code that handles clinical data. And the database tools let the Java team inspect the live schema without a separate SQL client. These are the features we’d miss first if they disappeared.

Can you recall a moment when using IntelliJ IDEA clearly paid off?

Early in 2026, a regulatory update meant we had to add explicit consent capture to every read of identifiable patient data. That change ran through the resource providers, the audit interceptor, and the gateway. The Change Signature and Find Usages features enabled our Java engineers to implement this change in days rather than weeks, with no production rollback. We’re confident that side of the work would have taken substantially longer and carried a higher rollback risk without IntelliJ IDEA.

How do you talk about the impact internally?

We try not to overclaim. The IDE is one factor among many. But there are honest things we can point to. Code review on that Java core is noticeably faster because reviewers focus on design rather than catching mechanical mistakes. Multi-file refactors went from a quarterly event to a normal part of a sprint, which in a regulated codebase matters more to us than raw velocity. New Java engineers reach a meaningful first pull request within a day or two, reliably enough that we plan onboarding around it. And for our ISO 27001 audits and CMMI assessments, the history the IDE keeps has made collecting evidence much more straightforward.

What does the near future look like for you?

We have around 10 engineers on the product team today, and we’d like to have 20 by the end of 2026. As we add more FHIR resource handlers and more compliance-driven refactors, the Java core is where IntelliJ IDEA remains the daily driver. And as AI-assisted coding becomes more central, we value the IDE more for reviewing and refactoring AI-generated code than for typing code from scratch. We plan in twelve-month windows rather than five-year slogans, so we’ll grow the toolchain the same way: one deployment at a time.

Read the full case study here.

JetBrains Startup Program FAQ

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