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Improving global health equity by helping clinics do more with less | MIT News

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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. Sibyl’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.”

Around the time the founders began working with Stanford, Sra joined MIT’s Laboratory for Information and Decision Systems (LIDS) as a principal research scientist. He would go on to become a faculty member in the Department of Electrical Engineering and Computer Science and MIT’s Institute for Data, Systems, and Society (IDSS). The mission of IDSS, to advance fields including data science and to use those advances to improve society, aligned well with Sra’s mission at macro-eyes.

“Because of that focus [on impact] within IDSS, I find it my focus to try to do AI for social good,’ Sra says. “The true judgment of success is how many people did we help? How could we improve access to care for people, wherever they may be?”

In 2017, macro-eyes received a small grant from the Bill and Melinda Gates Foundation to explore the possibility of using data from front-line health workers to build a predictive supply chain for vaccines. It was the beginning of a relationship with the Gates Foundation that has steadily expanded as the company has reached new milestones, from building accurate vaccine utilization models in Tanzania and Mozambique to integrating with supply chains to make vaccine supplies more proactive. To help with the latter mission, Prashant Yadav recently joined the board of directors; Yadav worked as a professor of supply chain management with the MIT-Zaragoza International Logistics Program for seven years and is now a senior fellow at the Center for Global Development, a nonprofit thinktank.

In conjunction with their work on CHAIN, the company has deployed another product, Sibyl, which uses machine learning to determine when patients are most likely to show up for appointments, to help front-desk workers at health clinics build schedules. Fels says the system has allowed hospitals to improve the efficiency of their operations so much they’ve reduced the average time patients wait to see a doctor from 55 days to 13 days.

As a part of CHAIN, Sibyl similarly uses a range of data points to optimize schedules, allowing it to accurately predict behavior in environments where other machine learning models might struggle.

The founders are also exploring ways to apply that approach to help direct Covid-19 patients to health clinics with sufficient capacity. That work is being developed with Sierra Leone Chief Innovation Officer David Sengeh SM ’12 PhD ’16.

Pushing frontiers

Building solutions for some of the most underdeveloped health care systems in the world might seem like a difficult way for a young company to establish itself, but the approach is an extension of macro-eyes’ founding mission of building health care solutions that can benefit people around the world equally.

“As an organization, we can never assume data will be waiting for us,” Fels says. “We’ve learned that we need to think strategically and be thoughtful about how to access or generate the data we need to fulfill our mandate: Make the delivery of health care predictive, everywhere.”

The approach is also a good way to explore innovations in mathematical fields the founders have spent their careers working in.

“Necessity is absolutely the mother of invention,” Sra says. “This is innovation driven by need.”

And going forward, the company’s work in difficult environments should only make scaling easier.

“We think every day about how to make our technology more rapidly deployable, more generalizable, more highly scalable,” Sra says. “How do we get to the immense power of bringing true machine learning to the world’s most important problems without first spending decades and billions of dollars in building digital infrastructure? How do we leap into the future?”

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Executive Interview: Steve Bennett, Director Global Government Practice, SAS 

Steve Bennett of SAS seeks to use AI and analytics to help drive government decision-making, resulting in better outcomes for citizens.   

Using AI and analytics to optimize delivery of government service to citizens  

Steve Bennett is Director of the Global Government Practice at SAS, and is the former director of the US National Biosurveillance Integration Center (NBIC) in the Department of Homeland Security, where he worked for 12 years. The mission of the NBIC was to provide early warning and situational awareness of health threats to the nation. He led a team of over 30 scientists, epidemiologists, public health, and analytics experts. With a PhD in computational biochemistry from Stanford University, and an undergraduate degree in chemistry and biology from Caltech, Bennet has a strong passion for using analytics in government to help make better public better decisions. He recently spent a few minutes with AI Trends Editor John P. Desmond to provide an update of his work.  

AI Trends: How does AI help you facilitate the role of analytics in the government?  

Steve Bennett, Director of Global Government Practice, SAS

Steve Bennett: Well, artificial intelligence is something we’ve been hearing a lot about everywhere, even in government, which can often be a bit slower to adopt or implement new technologies. Yet even in government, AI is a pretty big deal. We talk about analytics and government use of data to drive better government decision-making, better outcomes for citizens. That’s been true for a long time.   

A lot of government data exists in forms that are not easily analyzed using traditional statistical methods or traditional analytics. So AI presents the opportunity to get the sorts of insights from government data that may not be possible using other methods. Many folks in the community are excited about the promise of AI being able to help government unlock the value of government data for its missions.  

Are there any examples you would say that exemplify the work? 

AI is well-suited to certain sorts of problems, like finding anomalies or things that stick out in data, needles in a haystack, if you will. AI can be very good at that. AI can be good at finding patterns in very complex datasets. It can be hard for a human to sift through that data on their own, to spot the things that might require action. AI can help detect those automatically.  

For example, we’ve been partnering with the US Food and Drug Administration to support efforts to keep the food supply safe in the United States. One of the challenges for the FDA as the supply chain has gotten increasingly global, is detecting contamination of food. The FDA often has to be reactive. They have to wait for something to happen or wait for something to get pretty far down the line before they can identify it and take action. We worked with FDA to help them implement AI and apply it to that process, so they can more effectively predict where they might see an increased likelihood of contamination in the supply chain and act proactively instead of reactively. So that’s an example of how AI can be used to help support safer food for Americans. 

In another example, AI is helping with predictive maintenance for government fleets and vehicles. We work quite closely with Lockheed Martin to support predictive maintenance with AI for some of the most advanced airframes in the world, like the C-130 [transport] and the F-35 [combat aircraft]. AI helps to identify problems in very complex machines before those problems cause catastrophic failure. The ability for a machine to tell you before it breaks is something AI can do.   

Another example was around unemployment. We have worked with several cities globally to help them figure out how to best put unemployed people back to work. That is something top of mind now as we see increase unemployment because of Covid. For one city in Europe, we have a goal of getting people back to work in 13 weeks or less. They compiled racial and demographic data on the unemployed such as education, previous work experience, whether they have children, where they live—lots of data.  

They matched that to data about government programs, such as job training requested by specific employers, reskilling, and other programs. We built an AI system using machine learning to optimally match people based on what we knew to the best mix of government programs that would get them back to work the fastest. We are using the technology to optimize the government benefits, The results were good at the outset. They did a pilot prior to the Covid outbreak and saw promising results.    

Another example is around juvenile justice. We worked with a particular US state to help them figure out the best way to combat recidivism among juvenile offenders. They had data on 19,000 cases over many years, all about young people who came into juvenile corrections, served their time there, got out and then came back. They wanted to know how they could lower the recidivism rate. We found we could use machine learning to look at aspects of each of these kids, and figure out which of them might benefit from certain special programs after they leave juvenile corrections, to get skills that reduce the likelihood we would see them back in the system again.  

To be clear, this was not profiling, putting a stigma or mark on these kids. It was trying to figure out how to match limited government programs to the kids who would best benefit from those.   

What are key AI technologies that are being employed in your work today? 

Much of what we talk about having a near-term impact falls into the family of what we call machine learning. Machine learning has this great property of being able to take a lot of training data and being able to learn which parts of that data are important for making predictions or identifying patterns. Based on what we learn from that training data, we can apply that to new data coming in.  

A specialized form of machine learning is deep learning, which is good at automatically detecting things in video streams, such as a car or a person. That relies on deep learning.  We have worked in healthcare to help radiologists do a better job detecting cancer from health scans. Police and defense applications in many cases rely on real time video . The ability to make sense of that video very quickly is greatly enhanced by machine learning and deep learning.  

Another area to mention are real time interaction systems, AI chatbots. We’re seeing governments increasingly seeking to turn to chatbots to help them connect with citizens. If a benefits agency or a tax agency is able to build a system that can automatically interact with citizens, it makes government more responsive to citizens. It’s better than waiting on the phone on hold.   

How far along would you say the government sector is in its use of AI and how does it compare to two years ago? 

The government is certainly further along than it was two years ago. In the data we have looked at, 70% of government managers have expressed interest in using AI to enhance their mission. That signal is stronger than what we saw two years ago. But I would say that we don’t see a lot of enterprise-wide applications of AI in the government. Often AI is used for particular projects or specific applications within an agency to help fulfill its mission. So as AI continues to mature, we would expect it to have more of an enterprise-wide use for large scale agency missions.  

What would you say are the challenges using AI to deliver on analytics in government?  

We see a range of challenges in several categories. One is around data quality and execution. One of the first things an agency needs to figure out is whether they have a problem that is well-suited for AI. Would it show patterns or signals in the data? If so, would the project deliver value for the government?  

A big challenge is data quality. For machine learning to work well requires a lot of examples of a lot of data. It’s a very data-hungry sort of technology. If you don’t have that data or you don’t have access to it, even if you’ve got a great problem that could normally be very well-suited for government, you’re not going to be able to use AI.  

Another problem that we see quite often in governments is that the data exists, but it’s not very well organized. It might exist on spreadsheets on a bunch of individual computers all over the agency. It’s not in a place where it can be all brought together and analyzed in an AI way. So the ability for the data to be brought to bear is really important.   

Another one that’s important. Even if you have all of your data in the right place, and you have a problem very well-suited for AI, it could be that culturally, the agency just isn’t ready to make use of the recommendations coming from an AI system in its day-to-day mission. This might be called a cultural challenge. The people in the agency might not have a lot of trust in the AI systems and what they can do. Or it might be an operational mission where there always needs to be a human in the loop. Either way, sometimes culturally there might be limitations in what an agency is ready to use. And we would advise not to bother with AI if you haven’t thought about whether you can actually use it for something when you’re done. That’s how you get a lot of science projects in government.  

We always advise people to think about what they will get at the end of the AI project, and make sure they are ready to drive the results into the decision-making process. Otherwise, we don’t want to waste time and government resources. You might do something different that you are comfortable using in your decision processes. That’s really important to us.  As an example of what not to do, when I worked in government, I made the mistake of spending two years building an outstanding analytics project, using high-performance modeling and simulation, working in Homeland Security. But we didn’t do a good job working on the cultural side, getting those key stakeholders and senior leaders ready to use it. And so we delivered a great technical solution, but we had a bunch of senior leaders that weren’t ready to use it. We learned the hard way that the cultural piece really does matter. 

We also have challenges around data privacy. Government, more than many industries, touches very sensitive data. And as I mentioned, these methods are very data-hungry, so we often need a lot of data. Government has to make doubly sure that it’s following its own privacy protection laws and regulations, and making sure that we are very careful with citizen data and following all the privacy laws in place in the US. And most countries have privacy regulations in place to protect personal data.  

The second component is a challenge around what government is trying to get the systems to do. AI in retail is used to make recommendations, based on what you have been looking at and what you have bought. An AI algorithm is running in the background. The shopper might not like the recommendation, but the negative consequences of that are pretty mild.   

But in government, you might be using AI or analytics to make decisions with bigger impacts—determining whether somebody gets a tax refund, or whether a benefits claim is approved or denied. The outcomes of these decisions have potentially serious impacts. The stakes are much higher when the algorithms get things wrong. Our advice to government is that for key decisions, there always should be that human-in-the-loop. We would never recommend that a system automatically drives some of these key decisions, particularly if they have potential adverse actions for citizens.   

Finally, the last challenge that comes to mind is the challenge of where the research is going. This idea of “could you” versus “should you.” Artificial intelligence unlocks a whole set of areas that you can use such as facial recognition. Maybe in a Western society with liberal, democratic values, we might decide we shouldn’t use it, even though we could. Places like China in many cities are tracking people in real time using advanced facial recognition. In the US, that’s not in keeping with our values, so we choose not to do that.   

That means any government agency thinking about doing an AI project needs to think about values up front. You want to make sure that those values are explicitly encoded in how the AI project is set up. That way we don’t get results on the other end that are not in keeping with our values or where we want to go.  

You mentioned data bias. Are you doing anything in particular to try to protect against bias in the data? 

Good question. Bias is the real area of concern in any kind of AI machine learning work. The AI machine learning system is going to perform in concert with the way it was trained on the training data. So developers need to be careful in the selection of training data, and the team needs systems in place to review the training data so that it’s not biased. We’ve all heard and read the stories in the news about the facial recognition company in China—they make this great facial recognition system, but they only train it on Asian faces. And so guess what? It’s good at detecting Asian faces, but it’s terrible at detecting faces that are darker in color or that are lighter in color, or that have different facial features.  

We have heard many stories like that. You want to make sure you don’t have racial bias, gender bias, or any other kind of bias we want to avoid in the data training set. Encode those explicitly up front when you’re planning your project; that can go a long way towards helping to limit bias. But even if you’ve done that, you want to make sure you’re checking for bias in a system’s performance. We have many great technologies built into our machine learning tools to help you automatically look for those biases and detect if they are present. You also want to be checking for bias after the system has been deployed, to make sure if something pops up, you see it and can take care of it.  

From your background in bioscience, how well would you say the federal government has done in responding to the COVID-19 virus? 

There really are two industries that bore the brunt, at least initially from the COVID-19 spread: government and health care. In most places in the world, health care is part of government. So it has been a big public sector effort to try to deal with COVID. It’s been hit and miss, with many challenges. No other entity can marshal financial resources like the government, so getting economic support out to those that need is really important. Analytics plays a role in that.  

So one of the things that we did in supporting government using what we’re good at—data and analytics in AI—was to look at how we could help use the data to do a better job responding to COVID. We did a lot of work on the simple side of taking what government data they had and putting it into a simple dashboard that displayed where resources were. That way they could quickly identify if they had to move a supply such as masks to a different location. We worked on a more complex AI system to optimize the use of intensive care beds for a government in Europe that wanted to plan use of its medical resources. 

Contact tracing, the ability to very quickly identify people that are exposed and then identify who they’ve been around so that we can isolate those people, is something that can be greatly supported and enhanced by analytics. And we’ve done a lot of work around how to take contact tracing that’s been used for centuries and make it fit for supporting COVID-19 work. The government can do a lot with its data, with analytics and with AI in the fight against COVID-19. 

Do you have any advice for young people, either in school now or early in their careers, for what they should study if they are interested in pursuing work in AI, and especially if they’re interested in working in the government? 

If you are interested in getting into AI, I would suggest two things to focus on. One would be the technical side. If you have a solid understanding of how to implement and use AI, and you’ve built experience doing it as part of your coursework or part of your research work in school, you are highly valuable to government. Many people know a little about AI; they may have taken some business courses on it. But if you have the technical chops to be able to implement it, and you have a passion for doing that inside of government, you will be highly valuable. There would not be a lot of people like you. 

Just as important as the AI side and the data science technical piece, I would highly advise students to work on storytelling. AI can be highly technical when you get into the details. If you’re going to talk to a government or agency leader or an elected official, you will lose them if you can’t quickly tie the value of artificial intelligence to their mission. We call them ‘unicorns’ in SAS, people that have high technical ability and a detailed understanding of how these models can help government, and they have the ability to tell good stories and draw that line to the “so what?” How can a senior agency official in government, how can they use it? How is it helpful to them? 

To work on good presentation skills and practice them is just as important as the technical side. You will find yourself very influential and able to make a difference if you’ve got a good balance of those skills. That’s my view.  

I would also say, in terms of where you specialize technically, being able to converse in SAS has been recently ranked as one of the most highly valued jobs skills. The specific aspects of those technical pieces that can be very, very marketable to you inside and outside of government. 

Learn more by visiting Steve Bennett’s Linkedin page and the SAS public sector analytics webpage.

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