Evidence strategy
Define intended use, claims, measures, comparators, study stages, and the evidence needed for responsible decisions.
Digital health and responsible AI
Innovation should earn trust.
MCRC Health helps teams develop and evaluate digital health tools with evidence, safety, transparency, and real-world implementation in view from the beginning.
Capabilities
Evidence across the product lifecycle.
Co-design
Structure meaningful input from end users, clinicians, administrators, and people with lived experience.
Safety and validation
Plan staged testing of accuracy, appropriateness, tone, bias, risk responses, failure modes, and human oversight.
Implementation
Assess workflow fit, readiness, privacy, adoption barriers, monitoring, and the conditions required for sustained use.
A practical pathway
Match evidence to product maturity.
Define intended use
Clarify users, setting, claims, boundaries, foreseeable risks, and the decisions the tool may influence.
Test components
Evaluate deterministic features, retrieval, model outputs, safety behaviours, and known failure modes in controlled conditions.
Evaluate with users
Assess usability, acceptability, comprehension, workflow fit, equity, and performance across realistic scenarios.
Pilot and monitor
Introduce the tool in stages with clear accountability, incident processes, outcome measures, and criteria for adaptation or stopping.
Independent perspective
Useful whether you are building or buying.
Product teams
Connect technical development to clinical context, user needs, evidence claims, and staged validation.
Health organizations
Assess whether a proposed tool is ready, appropriate, governable, and likely to fit the intended workflow.
Research partners
Design studies that distinguish technical performance from user, implementation, and health outcomes.
Funders and decision-makers
Examine evidence maturity, risk, feasibility, and the conditions required for responsible scale-up.
Common questions
What responsible evaluation includes.
How should a health organization evaluate an AI tool?
Evaluation should examine intended use, user needs, output quality, safety risks, bias, privacy, workflow fit, implementation conditions, and real-world outcomes. The design should match the tool’s maturity and the consequences of error.
Does responsible AI replace clinical governance?
No. Responsible AI requires clear human accountability, defined escalation pathways, monitoring, transparent limitations, privacy safeguards, and clinical or organizational governance appropriate to the use case.
When should validation begin?
Before deployment. Early work can test bounded components and foreseeable failure modes, followed by multi-turn, user, workflow, and real-world testing as the system matures.
Can you evaluate a product you did not build?
Yes. An independent review can examine intended use, the available evidence, risks, evaluation design, implementation readiness, and the unanswered questions that should shape a pilot or procurement decision.
Developing or assessing a digital health tool?
We can help define an evidence and safety pathway appropriate to its maturity and intended use.
Start the conversation