Improving global health equity by helping clinics do more with less

The startup macro-eyes uses artificial intelligence to improve vaccine delivery and patient scheduling.

The startup macro-eyes is bringing new techniques in machine learning and artificial intelligence to global health problems like vaccine delivery and patient scheduling with its Connected Health AI Network (CHAIN).

The startup macro-eyes is bringing new techniques in machine learning and artificial intelligence to global health problems like vaccine delivery and patient scheduling with its Connected Health AI Network (CHAIN).

Courtesy of macro-eyes

More children are being vaccinated around the world today than ever before, and the prevalence of many vaccine-preventable diseases has dropped over the last decade. Despite these encouraging signs, however, the availability of essential vaccines has stagnated globally in recent years, according the World Health Organization.

One problem, particularly in low-resource settings, is the difficulty of predicting how many children will show up for vaccinations at each health clinic. This leads to vaccine shortages, leaving children without critical immunizations, or to surpluses that can’t be used.

The startup macro-eyes is seeking to solve that problem with a vaccine forecasting tool that leverages a unique combination of real-time data sources, including new insights from front-line health workers. The company says the tool, named the Connected Health AI Network (CHAIN), was able to reduce vaccine wastage by 96 percent across three regions of Tanzania. Now it is working to scale that success across Tanzania and Mozambique.

“Health care is complex, and to be invited to the table, you need to deal with missing data,” says macro-eyes Chief Executive Officer Benjamin Fels, who co-founded the company with Suvrit Sra, the Esther and Harold E. Edgerton Career Development Associate Professor at MIT. “If your system needs age, gender, and weight to make predictions, but for one population you don’t have weight or age, you can’t just say, ‘This system doesn’t work.’ Our feeling is it has to be able to work in any setting.”

The company’s approach to prediction is already the basis for another product, the patient scheduling platform Sibyl, which has analyzed over 6 million hospital appointments and reduced wait times by more than 75 percent at one of the largest heart hospitals in the U.S. Sybil’s predictions work as part of CHAIN’s broader forecasts.

Both products represent steps toward macro-eyes’ larger goal of transforming health care through artificial intelligence. And by getting their solutions to work in the regions with the least amount of data, they’re also advancing the field of AI.

“The state of the art in machine learning will result from confronting fundamental challenges in the most difficult environments in the world,” Fels says. “Engage where the problems are hardest, and AI too will benefit: [It will become] smarter, faster, cheaper, and more resilient.”

Defining an approach

Sra and Fels first met about 10 years ago when Fels was working as an algorithmic trader for a hedge fund and Sra was a visiting faculty member at the University of California at Berkeley. The pair’s experience crunching numbers in different industries alerted them to a shortcoming in health care.

“A question that became an obsession to me was, ‘Why were financial markets almost entirely determined by machines – by algorithms – and health care the world over is probably the least algorithmic part of anybody’s life?'” Fels recalls. “Why is health care not more data-driven?”

Around 2013, the co-founders began building machine-learning algorithms that measured similarities between patients to better inform treatment plans at Stanford School of Medicine and another large academic medical center in New York. It was during that early work that the founders laid the foundation of the company’s approach.

“There are themes we established at Stanford that remain today,” Fels says. “One is [building systems with] humans in the loop: We’re not just learning from the data, we’re also learning from the experts. The other is multidimensionality. We’re not just looking at one type of data; we’re looking at 10 or 15 types, [including] images, time series, information about medication, dosage, financial information, how much it costs the patient or hospital.”

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