CORE ADVANTAGES
Six reasons health systems work with us
Deep Clinical Knowledge
Our team combines clinical informatics, health services research, and applied data science. We do not apply generic data science methods to healthcare problems — we understand what the clinical environment requires.
Singapore Regulatory Fluency
Familiarity with MOH's AI in Healthcare guidelines, PDPA obligations, and the HSA's SaMD framework means clients do not need to educate us on the regulatory landscape — we work within it from day one.
Vendor-Independent Stance
We have no commercial relationship with any AI vendor or platform provider. Our recommendations reflect your data and goals — not a sales target or preferred partnership arrangement.
Written Outputs at Every Stage
Each engagement produces written documents you can present to your board, governance committee, or clinical leadership — not just presentations or verbal briefings that leave no record.
Structured, Predictable Process
Every service has a defined scope, timeframe, and output. You know what you are receiving before work begins, and we do not expand scope without prior agreement and revised documentation.
Capacity Building, Not Dependency
Our working model involves your team throughout each engagement. The goal is to leave your organisation better equipped to evaluate and manage AI applications independently — not to create ongoing reliance on external consultants.
EXPERTISE
Professional depth in healthcare data
Clinical data has characteristics that general-purpose data science does not account for — missing values driven by clinical workflow, coding variation across departments, and outcome definitions that require domain expertise to specify correctly. Our team's background in clinical informatics and health services research means these issues are identified and addressed methodically, not treated as minor preprocessing steps.
- Clinical informatics and health services research backgrounds
- Experience with EMR systems used across Singapore public health clusters
- Familiarity with ICD coding practices, SNOMED, and HL7 FHIR standards
"We understood the clinical context of the data — not just what the columns were called, but what they actually meant in a ward setting."
— TYPICAL CLIENT OBSERVATION
ML
Supervised & unsupervised methods
EHR
Electronic health record fluency
FHIR
Interoperability standards
Gov
Governance-first approach
TECHNOLOGY & METHODS
Tools chosen for the clinical context
We use established, well-documented machine learning methods — not the most complex or novel approach, but the most appropriate one for your data and objective. In healthcare, interpretability and stability matter as much as predictive accuracy. A model that a clinical team can understand and interrogate is more valuable than a black-box system with marginally better metrics.
- Model selection based on interpretability requirements, not benchmark scores
- All analytical tools documented and reproducible
- Infrastructure-agnostic — we work within your environment
CLIENT SERVICE
Collaboration, not delivery
Our engagements are structured as collaborative processes. We meet regularly with your clinical informatics and relevant clinical leads, share interim findings, and adjust our approach based on what emerges during the work. You are not waiting for a final report — you are involved throughout.
- Named point of contact for every engagement
- Regular progress updates throughout the engagement
- Post-engagement debrief and Q&A session included
Clear communication
Plain language throughout — technical findings translated for clinical and administrative audiences.
Timeline adherence
Scope and timeline agreed before work begins; any changes require written agreement from both parties.
One business day response
All enquiries and interim questions responded to within one working day during an active engagement.
What you receive at each price point
Written assessment report covering data quality, completeness, access controls, and AI compatibility.
Series of structured advisory sessions with written outputs at each stage; covers vendor, workflow, and governance.
End-to-end analytical engagement: scoping, modelling, and a plain-language report with clear use-case caveats.
VALUE & PRICING
Transparent scope, honest pricing
Our services are priced to reflect the actual time and expertise involved — not inflated to create the impression of complexity. Fees are agreed before work begins, and there are no charges for scope items that were not included in the original agreement. For organisations at an early stage, the Data Readiness Assessment offers a low-commitment way to understand what is realistically achievable before committing to larger programmes.
HOW WE COMPARE
A different kind of AI advisory
Not all healthcare AI advisory is equivalent. Here is what distinguishes a contextualised, clinical-first approach.
DIMENSION
TYPICAL PROVIDERS
MIROVA HEALTH
Clinical domain knowledge
Generic data science background; limited healthcare exposure
Vendor relationships
Often have preferred vendor partnerships or reseller arrangements
Singapore regulatory context
General GDPR / compliance knowledge; may not cover Singapore specifics
Output format
Slide decks and verbal presentations; limited written record
Limitation transparency
Results presented with high confidence; caveats minimised
WHAT SETS US APART
Distinctive features of our practice
Pre-engagement scope lock
Before any work begins, we produce a written scope document that defines exactly what the engagement covers, what it does not cover, the timeframe, and the deliverables. Neither party can change this without a documented amendment. This protects your budget and our time equally.
The "can we actually do this?" conversation
We include a structured feasibility check as the first step in any data or modelling engagement. If your data does not support the intended analysis, we will tell you clearly — and suggest what a realistic alternative looks like. We will not take on work we do not believe will produce something useful.
Findings presented to your clinical team
Every engagement includes a session where we present findings directly to clinical stakeholders — in language that does not require a data science background to follow. Decision-makers should understand what they are being asked to act on.
Internal capability transfer
Where possible, we document our methods in enough detail that your informatics team can extend, replicate, or audit the work independently. We are not trying to become an ongoing necessity — we are trying to leave your organisation more capable than we found it.
RECOGNITION & MILESTONES
Where we stand
4+
Years of healthcare AI advisory in Singapore
37
Healthcare organisations engaged across Singapore
94%
Client satisfaction rate across completed engagements
SG
Member, Singapore Medical Informatics Association (SMIA)
TAKE THE NEXT STEP
Put these benefits to work for your organisation
A preliminary conversation costs nothing and often clarifies more than a detailed written brief. We welcome enquiries from healthcare organisations at any stage of their AI planning process.
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