www.theverge.com Open in urlscan Pro
151.101.65.52  Public Scan

Form analysis
0 forms found in the DOM

Text Content

HERE’S HOW AN ALGORITHM GUIDES A MEDICAL DECISION

Sepsis Watch helps flag a potentially deadly reaction to infection
By Nicole Wetsman | Mar 23, 2022, 8:30am EDT
Illustrations by Grayson Blackmon

Artificial intelligence algorithms are everywhere in healthcare. They sort
through patients’ data to predict who will develop medical conditions like heart
disease or diabetes, they help doctors figure out which people in an emergency
room are the sickest, and they screen medical images to find evidence of
diseases. But even as AI algorithms become more important to medicine, they’re
often invisible to people receiving care. 

Artificial intelligence tools are complicated computer programs that suck in
vast amounts of data, search for patterns or trajectories, and make a prediction
or recommendation to help guide a decision. Sometimes, the way algorithms
process all of the information they’re taking in is a black box — inscrutable
even to the people who designed the program. But even if a program isn’t a black
box, the math can be so complex that it’s difficult for anyone who isn’t a data
scientist to understand exactly what’s going on inside of it. 

Patients don’t need to understand these algorithms at a data-scientist level,
but it’s still useful for people to have a general idea of how AI-based
healthcare tools work, says Suresh Balu, program director at the Duke Institute
for Health Innovation. That way, they can understand their limitations and ask
questions about how they’re being used. 

Some patients can get a little jumpy when they hear algorithms are being used in
their care, says Mark Sendak, a data scientist at the Duke Institute for Health
Innovation. “People get really scared and nervous, and feel threatened by the
algorithms,” he says. 

Those patients can be nervous about how hospitals are using their data or
concerned that their medical care is being directed by computers instead of
their doctors. Understanding how algorithms work — and don’t work — may help
alleviate those concerns. Right now, healthcare algorithms have very narrow
uses: a computer program isn’t going to make major medical decisions, but it
might help a doctor decide whether a set of medical scans needs closer
scrutiny. 

To help demystify the AI tools used in medicine today, we’re going to break down
the components of one specific algorithm and see how it works. We picked an
algorithm that flags patients in the early stages of sepsis — a life-threatening
complication from an infection that results in widespread inflammation through
the body. It can be hard for doctors to identify sepsis because the signs are
subtle, especially early on, so it’s a common target for artificial
intelligence-based tools. This particular program also uses mathematical
techniques, like neural networks, that are typical of medical algorithms.  

The algorithm we’re looking at underpins a program called Sepsis Watch, which
Sendak and Balu helped develop at Duke University. Sepsis Watch went online in
2018 after around three years of development. Today, when someone is
hospitalized at a Duke hospital, the program could be keeping an eye on them. 

Here’s what it’d be doing.

Many factors play into a person’s risk of developing sepsis, including age,
immune-compromising health conditions, and length of hospital stay. Signs from
all over the body can tell a doctor when it may be occurring, including things
like blood pressure, heart rate, and body temperature.

In order to make a prediction, the Sepsis Watch program first needs consistent
data about a patient.



Let’s take a hypothetical patient, Alex — a 58-year-old hospitalized with an
infection. Some of Alex’s data is easy to capture and feed into the program,
like their age, preexisting conditions (kidney disease) or previous medications
(cholesterol medication) — which won’t change while they’re in the hospital bed.



But other metrics are more dynamic. Alex’s blood pressure can change through the
course of the day. So could their oxygen levels, heart rate, or glucose levels.
And not all of those things are measured at regular intervals.

The program, though, doesn’t care if it’s unreasonable to expect a blood test on
Alex every hour — it needs the data regardless. So the first step it takes is to
fill in the gaps on those inconsistent metrics.

It uses a method called a Gaussian process to do that.



Say Alex only had their glucose levels measured at 2:30AM, 6AM, 3PM, and 9PM.



The mathematical tool uses information from those measurements, as well as
information about Alex’s other vital signs, to fill in the gaps.



It figures out the best way to draw a line through all the existing data points,
and adds in any missing numbers along that line. With a smooth line of evenly
spaced data points in place, it can now see what Alex’s glucose level should
have been at regular intervals throughout the day.



All that data can now be packaged, and fed into the neural network.



The neutral network underpinning Sepsis Watch is a complex series of algorithms
that search for patterns in messy data. It’s made up of a series of “nodes” that
each take in data, process it, and send it on to the next node before eventually
spitting out an answer.



Before it was put into use on patients, the neural network got extensive
training. It was fed hundreds of examples of data from patients who had sepsis
and patients who did not.



Each time it received new data, the network refined the way it worked with that
data until it could reliably pick out what patterns and combinations of data
meant someone was likely to have sepsis.



After that training, it’s ready to get real-time information from Alex. (These
are just a few examples of the dozens of types of information it receives).



Data gets fed into nodes of the neural network, which give different weights to
the information.



Each node gets additional information from a memory bank, where the neural
network stores all the information it learned from previous times the program
ran on Alex.



For an analysis at 10PM, then, each node would also pull on the memory of the
decisions it made at 9PM and 8PM for Alex.



Multiple nodes make up each layer of the neural network



And that all goes to the next layer, with its own nodes and memory banks



And then to the next layer



After the data is processed by that complex sequence of algorithms, the neural
network spits out a score between 0 and 1 — a probability that Alex has sepsis
at that particular moment in time. The closer the score is to 1, the more likely
it is that Alex has sepsis.

This is a simplification of an incredibly complex mathematical program that, in
reality, is made up of dozens of nodes and built from detailed equations. But at
the end of the day, it’s still giving doctors and nurses one simple number that
can help them care for patients.




Asking doctors and nurses to incorporate the Sepsis Watch alerts into their
routines — and asking them to make choices and decisions based on what it told
them — wasn’t a seamless process. The teams using the program had to figure out
the best ways to communicate with each other about the information it gave them
and how to adjust the way they engaged with patients and each other. 

At all stages, human choices played and continue to play a big role in how
sepsis is managed. People choose what type of mathematical tools scaffold the
program and what type of data trains it. People also have to decide on
thresholds for what counts as a low, medium, or high risk of sepsis based on the
output of the program.

Doctors and nurses are still the ones making treatment decisions after a Sepsis
Watch alert comes in. They call in prescriptions, monitor patients to see how
they respond, and decide if and when they should keep using an algorithm to keep
an eye on them. 

“At that level,” Balu says, “you’re relying on clinical judgment and clinical
experience.”

But so far, the effort to integrate Sepsis Watch seems to have been worth it:
deaths from sepsis seem to be down at the Duke hospitals, and Duke is working on
a more rigorous evaluation of how well the program contributed to that decline.
That could make people more comfortable with the technology, too.

Algorithms don’t fix problems in medicine. They’re human tools — they can
reflect and exacerbate problems and bake in bias if the people developing the
tools aren’t careful. But for better or worse, they’re going to be more and more
ubiquitous. That’s why it’s so important to examine them carefully. “It’s
important to have rigorous evaluations and validations,” Sendak says. “And to
have transparency — about the data, about the performance, and about strengths
and weaknesses.”

Chorus
 * Terms of Use
 * Privacy Notice
 * Cookie Policy
 * Do Not Sell My Personal Info
 * Licensing FAQ
 * Accessibility
 * Platform Status

 * Contact
 * Tip Us
 * Community Guidelines
 * About
 * Ethics Statement

Vox Media Vox Media Vox Media logo. Advertise with us Jobs @ Vox Media © 2022
Vox Media, LLC. All Rights Reserved