Health informatics Archives

When GenAI Ideas translate to practice with DHTI

If you’ve ever worked in healthcare, you know the feeling: you have a brilliant idea—something that would save time, reduce frustration, or make patient care smoother—and then… nothing happens. Not because the idea is bad, but because turning it into real software feels like trying to build a spaceship out of sticky notes.

That’s the gap vibe coding is trying to close. And with tools like DHTI, that gap is finally starting to shrink, and it resembles more of a conversation like below!

npx dhti-cli copilot --model gpt-5.3-codex --skill elixir-generator --prompt "Generate an elixir glycemic_advisor that summarizes diabetic patients' latest lab results and medications"

npx dhti-cli copilot --model gpt-5.3-codex --skill start-dhti --prompt "Start the glycemic_advisor elixir and display in CDS-Hooks sandbox"

Let’s walk through what vibe coding is, why it matters, and how DHTI makes it surprisingly doable—even if you’ve never written a line of code in your life.

So… what exactly is vibe coding?

Think of vibe coding as building software the same way you’d brainstorm with a colleague over coffee. You don’t start with code. You start with the vibe of what you want.

Instead of saying:

“I need a function that queries a FHIR endpoint and transforms the JSON.”

You say:

“I want a little helper that pulls a patient’s meds and tells me if anything looks risky.”

And the system starts shaping that idea into something real.

Vibe coding is:

Talking to the computer like you’d talk to a person
Iterating as you go
Letting the AI handle the technical scaffolding
Staying focused on the idea, not the syntax

It’s not magic. It’s just finally letting people who understand healthcare shape the tools they need—without having to become software engineers first.

Why healthcare needs this more than anyone

Healthcare is full of smart, creative people. But it’s also full of complexity: clinical workflows, privacy rules, specialized language, and data standards that feel like they were designed by a committee of cryptographers.

Even when clinicians know exactly what they want, translating that into something a developer can build is… hard. And developers, for their part, often spend more time deciphering clinical nuance than writing code.

Vibe coding cuts out the translation layer.
It lets clinicians express ideas in their own words.
It lets AI turn those ideas into working prototypes.
And it lets developers focus on polishing and deploying—not guessing.

But vibe coding alone isn’t enough. Healthcare needs structure. It needs guardrails. It needs standards.

That’s where DHTI comes in.

Meet DHTI: the “let’s actually build this” engine

DHTI is an open‑source reference architecture built specifically for healthcare GenAI applications. If vibe coding is the conversation, DHTI is the workshop where the ideas get shaped into something sturdy.

DHTI gives you a ready‑made foundation for building GenAI healthcare tools. It understands healthcare standards, provides synthetic data, supports agentic workflows, and helps you turn natural‑language ideas into real, testable applications.

In plain English:
DHTI makes vibe‑coded ideas actually work in healthcare environments.

Here’s how.

DHTI speaks healthcare, so you don’t have to

Most AI tools can generate code, but they don’t understand the rules of healthcare. They don’t know what FHIR is supposed to look like. They don’t know how CDS‑Hooks cards plug into clinical workflows. They don’t know what’s safe, what’s allowed, or what’s interoperable.

DHTI does.

So when someone says:

“Can you build something that checks whether a patient with diabetes is overdue for an A1c?”

DHTI can assemble the pieces:

A FHIR query
A little reasoning chain
A card that could show up in the EHR
A test environment to try it out

All without the user needing to know any of those words.

It lets non‑technical users build real workflows

Healthcare tasks aren’t simple. They involve multiple steps, multiple data sources, and multiple decisions. DHTI is built for that.

A clinician might say:

“I want something that looks at a patient’s skin images, compares them to previous ones, and drafts a note.”

DHTI can turn that into:

A workflow that loads images
A reasoning step that describes changes
A draft note
A preview card

It’s not just generating text—it’s building a mini‑application.

It makes experimentation safe and fast

One of the biggest barriers in healthcare innovation is simply being able to try things. Real patient data is locked down (as it should be). EHR systems are hard to access. And IT teams are stretched thin.

DHTI solves this by including:

Synthetic data that looks realistic but contains no PHI
A ready‑to‑use FHIR server
Prebuilt agent templates
A local environment you can spin up quickly

This means you can test ideas without waiting for approvals, access, or integration.

You can play.
You can explore.
You can see what works.

And that’s where the best ideas come from.

It smooths the path from prototype to production

Prototyping is fun. Deploying is not.

Healthcare IT teams have to think about:

Security
Compliance
Standards
Maintenance
Integration
Auditing

DHTI is built with these realities in mind. Because everything is structured, modular, and standards‑aligned from the start, IT teams don’t have to rebuild the prototype from scratch. They can refine it, secure it, and deploy it.

This is the difference between “cool demo” and “something translatable to practice.”

The Copilot SDK: your agent, packed right into the app

One of the most exciting pieces of this ecosystem is the Copilot SDK, making the AI agent directly available in DHTI—no external tools, no switching windows, no juggling platforms.

You can:

Build the agent
Test it
Tweak it

All in one place.

For vibe coding, this is huge. It means the conversation that creates the tool can happen inside the tool itself. Clinicians can test ideas in the same interface where they’ll eventually use them. Developers can refine behavior without rebuilding infrastructure.

It’s a tight, elegant loop.

Why this moment matters

Healthcare has always been full of ideas. What it hasn’t had is a way to turn those ideas into working software without months of meetings, requirements documents, and integration headaches.

Vibe coding changes the front end of innovation.
DHTI changes the back end.

Together, they make it possible for:

Clinicians to prototype ideas
Researchers to test hypotheses
Developers to build faster
IT teams to deploy safely
Organizations to innovate sustainably

It’s not just a new tool.
It’s a new way of building.

Last but not least, thank you, Hanson Professional Services, for supporting this project! Version 1 will debut at the Medical Informatics Europe Conference 2026 in Genoa, Italy, taking place May 26–28, 2026. Read more about DHTI and try it today! It is free and open-source. See the repository link below. Please comment/share if you find this useful!

DHTI: a reference architecture and agent harness for Gen AI in healthcare!
https://github.com/dermatologist/dhti
0 forks.
20 stars.
13 open issues.

Recent commits:

Published by Bell Eapen on March 4, 2026 | Permalink

Design Science Research in Healthcare: Bridging the Gap Between Ideas and Impact

In the world of healthcare research, the dominant paradigm has long been empirical and observational—studies that measure, compare, and validate phenomena to uncover truths. But what if the goal isn’t just to understand the world, but to change it? That’s where Design Science Research (DSR) comes in—a paradigm that’s less about observing and more about building, solving, and transforming. As a health informatics researcher working at the intersection of consultancy and academia, I’ve found DSR to be the most powerful lens for translating ideas into practice.

Image Credit: kevineriley, CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0, via Wikimedia Commons

What Is Design Science Research?

Design Science Research, as defined by Hevner et al. (2004), focuses on the creation and evaluation of artifacts—tools, frameworks, models, or systems—that solve identified problems. Unlike traditional behavioral science, which seeks to explain phenomena, DSR aims to design solutions and assess their utility. In healthcare, this means building decision support tools, workflow optimizers, or data integration platforms that directly improve clinical or operational outcomes.

Hevner’s seminal work laid out seven guidelines for DSR, including the need for a clear problem definition, artifact relevance, rigorous evaluation, and contribution to knowledge. These principles have since guided a wave of innovation in health informatics, where the complexity of real-world systems demands more than just theoretical insight—it demands actionable design.

Why Traditional Research Falls Short in Healthcare Innovation

Traditional healthcare research often struggles with the “translational gap”—the chasm between discovery and implementation. A novel algorithm might predict sepsis with high accuracy, but without integration into clinical workflows, it remains a paper exercise. Similarly, a new policy framework might promise equity, but without tools to operationalize it, it’s just words.

This is where DSR shines. It doesn’t stop at the idea; it builds the bridge. It asks: What artifact can embody this idea? How will it be used? What constraints must it navigate? And most importantly: Does it work in practice?

Living at the Intersection: Consultancy Meets Research

My work often begins with a specific organizational challenge—say, a hospital struggling with fragmented dermatology referrals. This is the consultancy mode: solving a particular problem for a particular client. But as a researcher, I’m also asking: Is this problem part of a broader class? Can the solution be generalized?

This dual lens allows me to design artifacts that are both context-sensitive and theoretically grounded. For example, a referral triage tool built for one clinic might evolve into a modular framework for dermatology decision support across multiple institutions. That’s the essence of DSR: solving classes of problems through iterative design, evaluation, and abstraction.

The Role of Translational Designers in Health Research Teams

Most health research teams are rich in domain expertise—clinicians, epidemiologists, policy analysts. But they often lack what I call translational designers: people who can take a promising idea and make it work in the messy, constraint-laden world of healthcare delivery.

These designers are fluent in both theory and practice. They understand stakeholder needs, data limitations, regulatory constraints, and user experience. They build prototypes, test them in real settings, and refine them based on feedback. Without them, even the best ideas risk dying in the valley of death between research and implementation.

Making DSR Accessible and Impactful

One challenge with DSR is that it can feel abstract or overly technical. But at its heart, it’s a human-centered approach. It starts with people—their needs, frustrations, and goals—and builds solutions that fit their world. It’s not about perfect algorithms; it’s about useful artifacts.

To make DSR more accessible, I often use metaphors. I tell students: “Traditional research is like mapping the terrain. DSR is like building the bridge.” Or: “Empirical studies ask ‘what is?’ DSR asks ‘what could be?’” These reframings help shift the mindset from passive observation to active creation.

Final Thoughts: Designing the Future of Healthcare

As healthcare becomes more complex, data-rich, and digitally mediated, we need more than observational studies. We need designers—people who can build, test, and refine solutions that work in practice. Design Science Research offers a rigorous, impactful way to do just that.

Whether you’re a clinician with an idea, a researcher with a model, or a technologist with a prototype, DSR provides the scaffolding to turn insight into innovation. And if you’re looking for inspiration, check out DHTI—it’s a living example of how design can drive transformation.

DHTI: a reference architecture and agent harness for Gen AI in healthcare!
https://github.com/dermatologist/dhti
0 forks.
20 stars.
13 open issues.

Recent commits:

Published by Bell Eapen on November 11, 2025 | Permalink

II. VSAC-on-FHIR

My enhancement now extends support beyond VSAC, enabling the use of FHIR-compliant terminology servers for private or custom-defined Value Sets. This added feature allows healthcare organizations to leverage their own FHIR-based terminology repositories, improving flexibility for institutions that need localized or proprietary clinical vocabularies while maintaining compliance with existing standards. Users can specify a FHIR Base URL to direct queries toward non-VSAC terminology servers, ensuring broader accessibility to domain-specific terminologies.

Published by Bell Eapen on May 30, 2025 | Permalink

I. CQL to ELM translator API with SpringBoot

Clinical Query Language (CQL) is a flexible, domain-independent query language designed to support clinical decision-making by enabling intuitive and standardized queries without requiring extensive technical knowledge. It works with any data model, integrates with widely used programming languages, and relies on the Expression Logical Model (ELM) as an intermediary format to ensure consistency with existing healthcare data standards. The open-source CQL-to-ELM Translator, built in Java, facilitates seamless execution of CQL queries by converting them into ELM representations, supporting various customization options and integrating with FHIR, QDM, and QUICK models to enhance clinical data interoperability.

Published by Bell Eapen on May 29, 2025 | Permalink

Translational Research in Digital Health and Gen AI

Translational research is the process of turning scientific discoveries into practical applications that can benefit society. It involves bridging the gap between different stages of research, from basic to applied, and between different stakeholders, such as researchers, clinicians, policy makers, and industry. Translational research aims to accelerate the transfer of knowledge and technology from the laboratory to the bedside, from the bench to the market, and from the ivory tower to the community.

Image credit: DoD Architecture Framework Working Group, Public domain, via Wikimedia Commons.

One of the key features of translational research is pragmatism, which means focusing on real-world problems and solutions, rather than abstract theories and models. Pragmatism also implies being flexible and adaptable to the changing needs and contexts of the target users and environments. Translational researchers are not satisfied with publishing papers in academic journals; they want to see their work make a difference in people’s lives and health outcomes.

Translational Research Methods & Techniques

To achieve this goal, translational researchers need to adopt a variety of methods and techniques that can help them design, develop, evaluate, and implement digital health solutions in an effective and efficient way. These methods and techniques include:

User-centered design, which involves understanding the needs, preferences, and behaviors of the potential users and stakeholders of a digital health solution and involving them in the co-creation and evaluation of the solution.
Rapid prototyping, which involves creating low-fidelity or high-fidelity prototypes of a digital health solution and testing them with the users and stakeholders in an iterative way, to obtain feedback and improve the solution.
Pilot testing, which involves conducting a small-scale trial of a digital health solution in a real-world setting, to assess its feasibility, acceptability, usability, and preliminary effectiveness.
Randomized controlled trials, which involve comparing the effects of a digital health solution with a control condition (such as usual care or another intervention) in a large and representative sample of participants, to determine its efficacy, safety, and cost-effectiveness.
Implementation science, which involves studying the factors and strategies that influence the adoption, integration, and sustainability of a digital health solution in a real-world setting and developing and evaluating interventions to enhance these processes.
Health economics, which involves analyzing the costs and benefits of a digital health solution from different perspectives, such as the users, the providers, the payers, and the society.

Translational researchers need to be aware of the latest advances and trends in digital health and related fields. One of the emerging paradigms in digital health is Gen AI. Gen AI refers to the development of artificial intelligence systems that can perform any intellectual task that a human can do, such as reasoning, learning, planning, decision making, and creativity. Gen AI has the potential to revolutionize digital health by enabling personalized, predictive, preventive, and participatory medicine, as well as enhancing the quality and efficiency of health care delivery and management.

Translational researchers play a crucial role in shaping the future of digital health and Gen AI. They act as translators, mediators, facilitators, and innovators between different disciplines, sectors, and domains. They also work as consultants for companies, organizations, and startups that want to develop, test, and implement digital health and Gen AI solutions. Translational researchers provide expert advice and guidance on the best practices and methods for designing, developing, evaluating, and implementing digital health and Gen AI solutions, as well as identifying and addressing the potential challenges and risks involved. Translational researchers also help to disseminate and communicate the results and impacts of digital health and Gen AI solutions to various audiences, such as academics, practitioners, policy makers, industry, and the public.

In summary, translational research is a vital and exciting field that aims to bring research papers into working artifacts, and to bridge the gap between digital health and Gen AI research and practice. Translational researchers adopt pragmatism as their guiding principle and use a variety of methods and techniques to design, develop, evaluate, and implement digital health and Gen AI solutions in real-world settings. Translational research is a practical endeavor that can make a positive difference in people’s lives and health outcomes.

Do you have a Gen AI research project that you need help with?

Published by Bell Eapen on March 23, 2024 | Permalink

Six things data scientists in healthcare should know

Healthcare, like most other fields, is eager to get on the data science bandwagon. Data scientists can make a huge difference in the way big data is utilized for clinical decision-making. However, there are paradigmatic differences in the way data scientists from quantitative fields view the world, compared to their clinical counterparts. This is especially true in the emerging fields of machine learning and artificial intelligence. This may lead to considerable inefficiencies. As a person trained in both fields, here is my take on this.

Data scientists should focus on the problem and not the solutions

Data scientists are excited about the latest GPT or BERT. Data scientists tend to refine the model a bit more using 10 more GPUs! In the process, they tend to solve problems that do not exist. From my experience practicing medicine in extremely resource-poor areas, simple solutions are valued more than BERT running on Kubernetes! This is true in the developed world as well, and many teams may have fundamental data needs that need to be tackled first.

Explanation comes before prediction

Emerging machine learning methods prioritize prediction accuracy compromising on explainability in the process. Clinicians, in most cases, cannot use nor trust a model that arrives at a conclusion without showing how it reached there. Hence, in the clinical domain, a simple logistic regression model may be more acceptable than a deep learning neural network. Parsimony is the key and a bit of feature selection to ensure parsimony will be appreciated always.

You need to know the clinical terminologies

A basic understanding of the clinical terminologies and terminology systems such as SNOMED and ICD is vital. It helps in understanding the clinical community better. Any healthcare analytics to consider variations in terminologies and adopt a standard system for consistency. Any tool that data scientists build for the clinical community should have support for terminology systems.

Biostatistics is more pervasive than you think

Most healthcare professionals are trained in biostatistics. Hence, the thinking leans towards population, sampling, randomization, blindings and showing a ‘statistically significant’ difference. Moving towards machine learning needs a paradigmatic shift. It may be useful to have a discussion on this at the outset.

Classes are of unequal importance

In healthcare, finding one class (e.g. cancer) is more important than the other class (e.g. no cancer). One class may need active intervention to save lives. Hence, sensitivity and specificity are of vital importance than accuracy!

Life is precious!

In healthcare, there is no room for error. Some decisions may have disastrous consequences while few others may save lives. As a data scientist in the healthcare domain, you should be cognizant of the fact that healthcare data is different from banking/airline data.

Bell Eapen MD, PhD.