Research

Resuscitating a lifelong dream with data science

By Sarmila Basu, Oct 05, 2017

Back in high school, I had no idea that data science could save lives.

I wanted to be a doctor because I wanted to make a difference, and what better way to make a difference than by saving lives?

That was my thinking until they told me I would have to cut up cadavers to become a doctor. That ended that, I would become a statistician and other people would be the ones who would make the ultimate difference by keeping us all alive.

Or so I thought.

Now I know better. I shouldn’t have overlooked the power of data.

Thousands of people die every year from a common infection that you can get when you go to the hospital. It’s called Clostridium Difficile 101 (or CDIFF for short), and people get infected with it 500,000 times per year, and devastatingly, it kills 29,000 people per year in the U.S.

It’s an ugly infection, putting the most vulnerable at risk of dying from dehydration brought on by seemingly endless bouts of diarrhea. People get infected when antibiotics wipe out their good, infection-fighting bacteria. The elderly, young, and those with compromised immune systems are most at risk but anyone can get it.

So how are data scientists like those on my Data and Decision Sciences team in Microsoft IT able to help?

We’re working with hospitals to predict when a patient is at risk of infecting CDIFF. A simple sounding thing, but knowing the answer to this question can help hospitals take life-saving precautions to help these at-risk patients.

When a patient is brought into the ER, we use artificial intelligence-driven modeling to assess their risk, an assessment that scores them both on how likely they are to get infected by CDIFF and, if they are likely to get it, how likely it is to lead to their death.

It scores them for age, medical history, antibiotics usage, and a long list of other factors. We’re working with two hospitals in the United States’ Midwest region to refine our model. And using old data, we’re right 85 percent of the time. That number is gradually climbing as we pour more data and insights into the model, allowing it to learn and become more accurate. We’re hoping to get it into the mid- to upper-90s and then make it broadly available for all hospitals to use.

Assessing an individual person’s risk enables hospitals to adopt procedures for taking care of these high-risk patients, costly precautions that can be reserved for those at the most risk.

But there is a human side too.

CDIFF is spread easily. When hospital staff and patients are casual with their hygiene–you can imagine how it would be hard to always remember to wear fresh gloves and to wash as thoroughly as needed every single time.

The story changes, however, when you can look at a patient’s chart and see that she is at high risk of dying if you aren’t as vigilant as possible. It’s human nature for people to do everything they can to protect that person.

And by now you are probably wondering, if data science can help hospitals fend off CDIFF, what else can it do?

Lots.

We’re working with other hospitals to find other ways to help them save lives. Some of the things we’re working on isn’t as exciting – forecasting when a hospital will run short on beds is dry stuff, but crucially important when it comes to making sure there are enough of them on hand during a crisis. Also not flashy? Predicting which patients are going be readmitted for the same problem in less than 30 days, but getting ahead of these readmissions can save hospitals millions of dollars and keep patient costs down.

We’re working hard to find new ways that data can do what I once thought was impossible, to save lives, and as you can imagine, do lots of other cool stuff.

We invite you to come back soon to learn more about the work we’re doing with data.

Learn more about the power of data analytics and machine learning by reading about how we’re using data to help save kids who are in danger of dropping out of school and how we are using data to help manage our buildings at Microsoft. If you want to know how you can do these kinds of things at your company, read about how I started my role at Microsoft and how I built my analytics team.

This blog post was originally published in Microsoft’s IT Showcase.

About the author

Author photo

Dr. Sarmila Basu is Chief Data Scientist at Microsoft IT, leads the MSIT Data Sciences (DDSG) group and oversees both internal and external global customer engagements. Dr. Basu is instrumental in Microsoft’s forward-looking development efforts to integrate the company’s Data Science expertise with the Sales, Services and Product Group. In this role, she has been heavily influential in the direction and execution of the company’s commitment to leveraging Advanced Analytics solutions using Microsoft technology. Using this Data Science strategy maximizes management’s ability to make data driven decisions and solve challenging business problems.

Dr. Basu is a Ph.D. in Economics, and has 20 years of senior management experience working with Fortune 500 companies in Telecom, Financial Services and IT. She is passionate about Data Science and the business value it delivers. Sarmila firmly believes there is an art to Data Science. It requires more than Advanced Analytics depth and technical expertise to make a material difference in how we help our company and customers.

Dr. Basu is a noted and sought after conference speaker. Work from Dr. Basu’s team is featured on Microsoft’s IT Showcase. You can find Dr. Basu’s writings on Linkedin. When not at work, Sarmila is committed to her philanthropic pursuits. These interests include Economic Empowerment and as a board member for the Arts.


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