STRIATA built by Macro-Eyes

Effective emergency response requires visibility into the health system as networked whole. Today, health systems are opaque.

What's the condition of each site? What's the state of cold storage, back-up power, commodities, personnel? How does that capacity translate to the capacity to provide essential care? Who will seek care during crisis and where will they likely access it? What resources will likely be consumed at each node within the system? 

STRIATA is proven, central command for emergency response: technology that predicts the current capacity of the system across each node, insight into the behavior of each catchment population and recommendations for what the system will require to respond rapidly and efficiently. 

STRIATA provides national, and health facility specific optimization of scarce resources.

Matching supply to demand is the objective of every supply chain. Precision matching supply to demand, when lives are in the balance, is the key to effective pandemic response. To achieve this, systems must learn from a broad set of data that describes the network and learn from data in the public domain which describes communities. Today, supply chains are merely transactional - and backwards looking. Supply chains have hindered the world's ability to react to novel pandemics and disasters. 

The technology systems that support pandemic response (and healthcare delivery writ large) is designed to document what already happened. Transactional systems cannot keep pace with the need of COVID response or the fast sweeping health concerns of the future. Systems that describe yesterday are not as useful as systems that indicate what will happen next.

STRIATA is a proven platform that precision matches scarce supply to dynamic demand. Learning at the speed of data. STRIATA brings relevant, predictive insight into the hands of decision makers to drive the efficient, equitable distribution of resources.

STRIATA is central intelligence for the distribution of health commodities.

1. Predict Behavior. Who will show up for care? Who won’t? When you know who is at greatest risk of not showing for care, you can pinpoint engagement to promote care seeking behavior. When you know where populations will seek care you can fully support demand.
2. Forecast Supply. What will be used where? Macro-Eyes AI delivers precision forecasting from individual sites to national demand forecasting. Precision forecasting buys time to solve the stockout and time to reallocate valuable supply that will otherwise go to waste.
3. Infer Infrastructure. To intervene effectively, to send resources where they are needed most, leaders must know the likely condition of each health facility on the map. From infrastructure to staffing to equipment – each component drives capacity.

STRIATA deploys supervised and unsupervised machine learning systems across satellite imagery and the public internet, natural language and statistical abstracts to understand environment, access, and demand.

STRIATA serves vulnerable populations in complex, low data environments. From Sierra Leone to California, STRIATA is focused on improving health care delivery for the underserved. 

Underserved communities lack the physical and digital infrastructure to support rapid disaster response. STRIATA provides actionable intelligence that is achievable within the constraints of even extremely resource-constrained settings. More equitable distribution, more people will receive more care more quickly. Areas hardest hit will recover rapidly and at a lower cost.

Supply chains are usually the largest cost of any business. It is also where there is the most money to be saved. For the communities we serve, saving a dollar really means saving a life. Funds are always short and any dollar saved generates opportunity for impact elsewhere. 

STRIATA empowers decision makers in governments and is in use today in Nigeria, Mozambique, Tanzania, Sierra Leone, and in California.

Macro-Eyes is an AI company focused on supply chain. We believe the world has remarkable technologies that save lives and significant resources, but requires new methods for care delivery. Supply chain is the foundation for rapid pandemic response - essential for government leadership and community health workers.

STRIATA is intelligence in real time that enables rapid response to health emergencies and provides essential infrastructure for enhanced care delivery.  

The use of publicly available data enables rapid response during health emergencies. It enables scale at significantly lower costs with repeatable models across “universal” data sets – satellite imagery, the public internet.

STRIATA has been developed for - and deployed in - some of the most challenging environments in the world. The platform has been and continues to be used to improve the allocation of health commodities in the US, Tanzania, India, Zambia, Nigeria, Sierra Leone, and Mozambique. STRIATA is providing intelligence across more than 7000 health facilities that serve tens of millions of patients. In 2020, Macro-Eyes AI analyzed data from 1,087,535 patients, 2645 health facilities, and 8,029,159 medical events. STRIATA and the Macro-Eyes team are ready for scale. 

Robust back-end data infrastructure has been developed for repeatable deployments and models and approaches have been built, tested, and deployed to work across a variety of data environments: on the cloud, running on government servers to preserve data sovereignty, on mobile device with and without network connectivity for use by field workers with intermittent network access, and within existing health logistics software.

The innovation is largely in how STRIATA makes the state of the art possible in low-resource settings. We believe advanced technology is most important where need is greatest. STRIATA deploys a set of unique tools for deriving relevant features from publicly available data [news, statistics, social media], satellite imagery, logistics information systems, and health record systems. The vast majority of data ingested into STRIATA is from sources in the public domain. The emphasis on publicly available data enables rapid deployment at scale, enabling project launch concurrent with negotiation over access to closely-held government data.

Today, a significant portion of vaccines globally are wasted – and stock-outs are common. Health systems both waste supply and deprive people of lifesaving care. Today, decision makers rely on data years out of date to design interventions into the health system. Today, health systems know many people won’t show up for care even though it may save their life – yet they don’t know which people will drop out of the health system. These are covid-19 issues and persistent public health issues.

Prediction is a way to help governments triage most effectively – allocating scare supply to demand. Repeatable, accurate prediction comes from systems that learn. STRIATA runs Macro-Eyes AI, proven capable of learning in challenging environments.

We are optimizing supply chains for COVID care and COVID vaccine distribution. Our assumption that when deployed at the national level this directly benefits the entire country population directly or within one degree of separation. 

Current: 100,000,000

Next year: In adding India and Nigeria as users of our COVID response technology we could improve the supply chain for nearly 1.6B people. We are working to deploy STRIATA across the Gulf of Mexico for disaster preparedness, if this is successful we would add another `100 million people. Total would be near 1.5B people impacted

In five years: macro-eyes is working on a 10 year project to bring improved supply chain optimization for health products across 40 countries. This would likely increase the population served to nearly 2.2B people. 

Macro-Eyes works with the Draper Richards Kaplan Foundation as a Global Social Impact organization to develop our framework for measuring impact. Impact is a balance of scale and efficiency. We measure the number of people our technology reaches, the number of countries we engage successfully, the reduction in cost to our customers as we increase implementations, speed of technology integration and we hope to measure our financial impact over time. The latter will take additional time in country – likely over 5 years – before a meaningful trend statistic can be captured.  The technology is measured against a baseline of the status quo, and statistical measures of model accuracy (internally as F1 or R-sqaured, externally as a percentage of accuracy of prediction based on preliminary retrospective analysis).

We have worked across 7 countries, reached more than 100 million people, reduced our implementation cost by nearly 50%, and accelerated our implementation time from 6 months in Sierra Leone to 8 weeks in the case of California.  Core Macro-Eyes AI generates, in extremely challenging settings, accuracy between 85% and 93% based on the location and query and in Tanzania generated a 97% reduction in forecasting error when compared to the existing distribution model.

24 people are involved full time in Macro-Eyes currently.

2 part time subject matter experts

4 consultants focused on Finance

macro-eyes has deep expertise in AI and technology development. The team includes AI experts, global health professionals, and software technologists from countries in North America, Europe and Africa. This team has deployed technology at scale in collaboration with pharmaceutical companies, global development companies, and the largest funding organization on the planet. Several have work directly in health systems and understand the frontlines from the ground. Board Members: Dr. Rebecca Weintraub - professor of Medicine at Harvard and Senior Fellow at Ariadne Labs - Prashant Yadav - Professor at INSEAD, Senior Fellow at Center for Global Development, and prev. Health Supply Chain Advisor for the Bill & Melinda Gates Foundation. AI Advisory board members from leading institutions around the world constitute a network of leadership and expertise unprecedented in the Global Health sector. 

Our team is comprised of highly technical engineers - nearly 50% of our staff our women. The Director of our AI/ML team is a female. Our Founder and Chief Scientist is a person of color. Our Board and Advisory team is 40 % women. 

The national average for technical companies is 26% women.

We have a goal to diversify our Director and above leadership to 50% women by the end of 2022.  We are working closely with (female) advisors one of whom leads an organization focused on developing women in leadership at Stanford. 

We have a goal to employ 30% people of color by the end of 2022.  We engage with various groups focused on black engineers like BlackinAI and National Society of Black Engineers during our posting and hiring process. We also work with an organization called Andela who train Africa based engineers.

We are seeking a broader range of deployment and funding partners. Our partners at the Bill & Melinda Gates Foundation and USAID are encouraging us to engage with funders to focus on capacity building and the scaling needs of the STRIATA technology as they have invested, and continue to invest in the catalytic deployment effort. 

We would benefit from expertise in public relations. We are a deeply technical team, working on the state of the art of technology [AI] that can be viewed as threatening in many settings. We believe deeply in the potential for technology to enable existing, scarce resources to reach more people in need. We see AI as compassionate technology. This story must be told. We would benefit from expertise in how to carefully open-source advanced technology. We are legally committed to making our technology available in support of the most vulnerable, globally. We also recognize that our technology cannot, nor should, be used 'out of the box' by anyone who downloads the code from the internet. We want to frame its use to make it most effective.

We are open to broad array of partners for funding, in country deployment, legal, or new business development. 

In February 2021, Macro-Eyes formed a partnership with Direct Relief and the California Primary Care Association to deploy STRIATA for the State of California. In 6 weeks, working solely from publicly available data, STRIATA delivered site-specific insight into each FQHC in California. STRIATA was made available to government decision makers and is being used by Direct Relief to guide and prioritize investments into the health safety-net. To invest for impact, you must know what’s in place. To improve health systems, you must be able to rapidly measure them.

The Robert Wood Johnson Foundation Prize would support efforts to increase the resilience of the health supply chain for FQHCs, making the health safety net itself more resilient.

State and federal agencies plan at the aggregate level, but effective primary care is site-specific. Today, no single agency maintains visibility into the US health safety-net or US primary care at the operational, site-level. This results in two crucial gaps: 

1. Decision makers struggle to design and implement the most effective interventions into the US primary care network. 
2. In emergency settings – COVID-19 is a tragic example – state and federal agencies must move quickly to allocate scarce resources. They need to know what’s already in place and where to position resources to reach key populations. Today, decision makers don’t have site-level data to determine where and how to deploy resources needed to save lives.

We propose to scale up STRIATA to describe the current state and capabilities of the US health safety net.

Macro-Eyes is an AI company focused on improving health supply chain for the most vulnerable. The team is led by MIT Professor Dr. Suvrit Sra and we employ engineers and scientists from countries around the world. Our company is growing expertise in the most advanced technology for health in the world to countries like Zambia, South Africa, Kenya, Germany, France, Cote d'Ivoire and Italy where we employ people. 

The technology is deployed in India, Zambia, Mozambique, Cote d'Ivoire, Tanzania, Sierra Leone, and the United States. AI for Humanity is a global vision and requires a truly global solution where AI improves lives and AI becomes more available through increased capacity. Macro-eyes does both. 

We would use this prize to increase capacity building and adoption of AI. We would do this in concert with our active deployment efforts funded by the Bill & Melinda Gates Foundation.

Macro-Eyes is advancing the state of the art in Human in the Loop Machine learning. We have deployed this technology in Mozambique to determine the availability of key services.  We also deployed this technology to capture on shelf availability and the movement of stock in pharmacies in India through simple image capture. Macro-Eyes builds and deploys supply chain intelligence technology to predict stock utilization, availability, and the capacity of existing health system infrastructure to house this technology. We aspire to bring these solutions together to complement our public data approach with our expertise engage with front line health works in real time insight collection about the supply chain. 

With this funding we would deploy the HIL and image capture approach for stock level management along with our active deployments of supply chain intelligence technology being funded by the Bill & Melinda Gates Foundation in Sierra Leone, DRC, and Burkina Faso.  These would demonstrate the impact of the combined technology at scale.