OUR STORY
Founded on a shared frustration
Mirova Health was established in 2021 by a small team with backgrounds spanning clinical informatics, health services research, and applied data science. The founding conversation was a direct one: healthcare organisations across Singapore were being approached by AI vendors making claims that their data could not support — and clinical teams lacked the internal expertise to evaluate those claims critically.
We started as an advisory practice because we believed the most pressing need wasn't another AI product — it was for honest, independent guidance. Our work helps organisations understand what their data actually contains, what is realistically achievable with machine learning, and how to navigate Singapore's evolving regulatory landscape for AI in healthcare.
Today we work with hospitals, polyclinics, and health system teams across the island. Every engagement is scoped individually. We do not productise our services or apply the same framework regardless of context — because healthcare data rarely fits a standard mould.
Our Mission
To help healthcare organisations in Singapore make well-informed decisions about AI — grounded in what their data can support and what the evidence base for specific applications actually looks like.
Our Approach
We work as a collaborative partner, not a vendor. Our process involves close input from your clinical and informatics teams at every stage, and we prioritise outputs that are legible to non-technical stakeholders — because decisions get made by people, not models.
Our Position
We are deliberately small and independent. We do not take referral fees from AI vendors and have no financial interest in directing clients toward any particular product or platform. Our only interest is in helping you arrive at a well-reasoned position.
THE TEAM
People behind the work
Dr Linh Tran
FOUNDING DIRECTOR
Clinical informaticist with twelve years of experience across public hospital systems in Singapore and Vietnam. Leads all clinical data modelling engagements and advisory methodology.
Rajan Nair
ADVISORY LEAD
Former health system strategist with experience in digital transformation programmes at major Singapore public health clusters. Specialises in governance and implementation advisory.
Siew Ching
DATA SCIENCE LEAD
Applied machine learning practitioner with a background in biostatistics and health services research. Responsible for all analytical modelling and data quality assessments.
HOW WE WORK
Standards we hold ourselves to
Every engagement reflects the principles we believe AI work in healthcare should be built on.
Data Privacy & PDPA Alignment
All engagements are structured around Singapore's Personal Data Protection Act. We document data handling arrangements before any analysis begins and do not retain identifiable information.
MOH AI Guidelines Compliance
Our advisory practice is informed by MOH's Artificial Intelligence in Healthcare Guidelines and the IDSC framework — helping clients understand what responsible AI deployment looks like in Singapore's regulatory context.
Independent Review Principle
We do not accept referral fees or commercial arrangements from AI vendors. Our assessments reflect only what your data and objectives support — not what any third party wishes to sell.
Plain-Language Communication
Every written output is reviewed to ensure it can be understood by clinical administrators and non-technical decision-makers — not just data scientists.
Collaborative Working Model
We work alongside your clinical informatics and data teams rather than replacing them. Knowledge transfer is a priority — we want your team to be more capable after an engagement, not more dependent on us.
Honest Limitation Reporting
Model outputs are always accompanied by a clear statement of limitations, conditions for appropriate use, and what the analysis cannot determine. We do not present findings with more confidence than the data supports.
EXPERTISE & CONTEXT
Healthcare AI advisory in Singapore
Singapore's healthcare system sits at an advanced stage in its digital transformation journey, with structured national programmes — including the Health IT Master Plan and the National AI Strategy — creating both opportunity and governance obligations for AI adoption. Within this context, health system administrators and clinical informatics teams face a specific challenge: how to evaluate AI-assisted tools and data science applications critically, without the internal technical capacity to do so independently.
Mirova Health's work addresses this gap directly. Our clinical data analysis practice draws on established machine learning methods — including supervised classification, regression modelling, and survival analysis — applied to de-identified datasets from electronic medical record systems, operational management platforms, and administrative health databases. All analysis is conducted within the data governance agreements of the engaging institution.
Our advisory services are particularly relevant to organisations navigating Singapore's regulatory environment for AI in clinical settings. The Ministry of Health's AI in Healthcare framework, alongside PDPA obligations and the Health Sciences Authority's requirements for Software as a Medical Device, creates a layered compliance landscape that requires careful, institution-specific interpretation — not a generic checklist.
The Health Data Readiness Assessment addresses an often-overlooked prerequisite: understanding what your organisation's data actually contains before committing resources to AI development or vendor engagement. Data completeness, coding consistency, system interoperability, and access control infrastructure all affect what analytical approaches are feasible — and a realistic view of these factors often changes the direction of a programme significantly.
WORK WITH US
Ready to begin a conversation?
We welcome enquiries from healthcare organisations at any stage of their AI journey — whether you're evaluating a first engagement or reassessing an existing programme.
Get in Touch