macro-eyes is rooted in research. From large-scale investigations that extend over months to micro inquiries or an experiment that runs at speed for an intense week. R&D is part of each day for all of us. And we often have no other choice but to build what hasn't been built before; we wish this was not so frequently the case.



Intelligent Patient Similarity


Identify cohort-specific risks and the most effective course of care, based in practice-based evidence from patients like your patient

Personalize care

Risk-stratify patients

See how similar scenarios were resolved

Intelligent Patient Similarity >



Predictive Supply Chain for Devices

devices admin dashboard imac.jpg

Streamline purchasing to focus on value

identify the medical devices that deliver optimal outcomes at greatest value for each patient type

Predictive Supply Chain for Devices >



Countries that are fast-emerging will relentlessly, often brilliantly, skip the present and jump to the future...

Global Health >



Health disparities exist at every level. The personalization of healthcare ennobles the individual by the system.

Health Equity >


In rare cases, pure research will develop into pragmatic products. And it is hardly ever a straight line. Research shapes how we see the world and refines our model of where we believe we can make an impact.

We have examined and chosen not to address many problems in healthcare. Research should lead far more frequently to negation than it does to affirmation. If that is not the case, then the research is not bold enough; there is not sufficient risk.

macro-eyes works with a global community of experts in global health, design and user experience and with leading figures in AI.


These relationships come out of a common focus on how machine learning must broaden and strengthen the pattern recognition than is inherent to domain experts across health: from clinicians to health workers; from a leader at the ministry of health charged with supply chain redesign to those on the front-lines of care charged with bringing in populations who are difficult to reach.

Medicine is pattern recognition. It is differentiating signal from noise. We want technology to leverage expertise and bring muscle and scale to the human ability to recognize and act on meaningful pattern in the data that describes health and the context for care.