Reporter J. Duncan Moore spent a quarter shadowing professor Dan Adelman’s Healthcare Analytics Laboratory, seeing firsthand how students used their Booth education to address a pressing health-care need.
Marcia Kraniak—79 years old, from the South Side of Chicago—went home from the hospital eight weeks ago. She had been admitted to the University of Chicago Medical Center (UCM) with congestive heart failure and spent three days there. Fluid had built up in her body, and her heart was too weak to pump enough blood. She couldn’t move around easily. Two months after leaving the hospital, however, she’s doing well on her new regimen at home.
Shortly after Kraniak arrived in the UCM emergency department, hospital staff identified that her medical condition, her home life, and her mental state made it less than likely that she would get well after she went home. A new admissions algorithm predicted she might have to come back to the hospital soon. So the cardiology and nursing teams at UCM applied a special new protocol on her behalf. They gave Kraniak (a patient invented for this article) a detailed plan to take care of herself—including instructions to eat better, lay off the salt, and try to take a short walk every day—and simplified her medications to help her stay on her regimen and get well more quickly.
In the past, hospitals didn’t closely monitor whether patients had to be readmitted shortly after an original hospital visit. If patients returned with the same health issues, they got patched up again, the hospital got paid again, and nobody tracked how many patients made this boomerang trajectory.
The Affordable Care Act changed the game. Congress decided that Medicare, the federal insurance program for people over 65, will now penalize hospitals if a heart-failure patient comes back within 30 days. Under the Hospital Readmissions Reduction Program, hospitals now must report their rates of 30-day readmission. If their rates are worse than average or than would be statistically expected, Medicare will reduce government reimbursements across the board for all patients at that institution. Medicare also now publishes every hospital’s results on its website, medicare.gov/hospitalcompare, putting each hospital’s reputation for quality of care at risk.
For UCM, this presented a fresh business problem—and an opportunity to demonstrate that it truly can be “at the forefront of medicine,” per its slogan. Its heart-failure readmission rate in 2014 was 20 percent, better than the national rate of 22.3 percent, but not good enough. Cardiologists and nurses at the hospital devised the new protocol used on patients like Kraniak to try to reduce those readmissions.
But was this new protocol effective? For University of Chicago Booth School of Business professor Dan Adelman, this was just the kind of problem he’d been seeking. Each spring quarter, he offers the Healthcare Analytics Laboratory, a real-world exercise in big-data research for a carefully selected cadre of MBA students.
“The health-care industry is a wreck. It desperately needs our help,” he told his 24 students on April 1, the first day of class. He had persuaded Chicago hospitals and doctors to share their data on four hard-to-solve problems. (For more on these projects, see “Data and Diagnostics.”) Now it was up to the students to drill through the numbers and figure out the answers.
Teaming Up with Big Data
Adelman has conceived his course to develop another skill set besides data analysis and health-care-industry knowledge: the soft skills of teamwork, collaboration, presenting, public speaking, and communication. Proficiency in these interpersonal skills is increasingly a must-have to be successful in business.
“I’ve got the only class I’ve heard of that does big data and brings in the teaming skills as well,” Adelman said. “It’s not an easy combination. That’s why it’s a laboratory. This is a safe place, a practice venue.” Students have to apply for the course, demonstrate interest in the health-care industry, and satisfy specific prerequisites, including statistics, regression analysis, simulation, and operations-management and business-process fundamentals. They have to write an essay and submit a resume.
“The jobs these students are going to get are realistically close to what they are doing for me here,” said Adelman, “from performing data analysis to creating client presentations to finding solutions as part of a team. How you function in a working environment like that is critical. I want to put students into a habit of self-reflection, so that down the road it will guide them through their careers and life in general.”
Each year since he launched the course about five years ago, Adelman has obtained highly proprietary datasets from leading Chicago hospitals. For Booth MBA students, these datasets provide a window into the complex business problems hospitals face, and the chance to tackle them in teams of six. (Learn more about the UCM heart-failure readmissions team in “Cardio Training.”)
“The goal is to offer deep exposure to industry issues,” said Adelman, more formally known as the Charles I. Clough Jr. Professor of Operations Management, but at heart, an operations guy with a PhD in industrial engineering and operations research from Georgia Tech. “Not by lecture or case method, but by giving students the opportunity to actually go about solving the business problems of healthcare.”
Making the Nitty Gritty Pretty
It’s April 15, two weeks into the course. Adelman has told the student UCM heart-failure readmissions team that they will be giving a presentation next week on data visualization. He starts with the basics: “Tell us about your dataset. How big? How many patients?”
Then he ups the ante to ensure that the team engages their audience: “Draw eight to ten visualizations of data. Use histograms. Use color. Kick it up a notch. Make your graphics the most interesting graphics they can be.” He shows a YouTube video of a TED talk by Swedish doctor and statistician Hans Rosling. It’s a dynamic collage of facts and motion graphics that tells the story of Asia’s economic ascendancy over the West. This is the standard Adelman sets for his students. The team has their work cut out for them: distilling several years of discharge data and thousands of patient records, each containing hundreds of clinical variables, into a compelling 15-minute visual narrative.
Cardio Training: Meet the Team Members
Pat Ward, a PhD student in economics and a former Air Force officer, selected the students who would
address the UCM heart-failure readmissions issue and served as their mentor...READ MORE
Pat Ward, a PhD student in economics and a former Air Force officer, selected the students who would
address the UCM heart-failure readmissions issue and served as their mentor. He chose these six students (of 24 in the course) to contribute "a huge gamut of skills," ranging from a PhD statistician to a statistics neophyte, and offering experience in industries from medicine to marketing:
Adam Vohra, a third-year student at the UC School of Medicine pursuing a joint MD/MBA degree
Ai Nguyen, BS '05, a pharmacy manager at Walgreens
Hyuna Yang, a statistician at a pharmaceutical company in the Chicago suburbs
Katerina Steele, AB '03, an associate director at a pharmaceutical-marketing agency
Kristin Francoz, a former health-policy researcher at a think tank
Yizhen Dong, formerly in strategic planning at a biotech company
At a team meeting, Hyuna Yang, a pharmaceutical statistician, explains how she has cleaned up the data using a statistical program called R. She displays rows and columns on the screen.
“Hyuna, you’re a goddess,” says Katerina Steele, AB ’03, who works in pharmaceutical marketing.
“No, I’m not. I’m just good at coding,” Yang answers.
“We might be able to streamline the data by looking at medication,” Steele suggests. “For example, anybody taking a cardiovascular drug versus those not taking one.”
Yang: “If they don’t take it, it’s a 0? Or a 1?”
“It’s a 0,” says Ai Nguyen, BS ’05, a Walgreens pharmacy manager.
Pat Ward, a current PhD student and the team’s mentor, raises the question of human comprehension, especially among an elderly population. “What was confusing to patients,” he says, “was when the medication was changed after they went home.”
“In the interest of time, maybe we should start from a working hypothesis,” says Kristin Francoz, a health-policy researcher, “to help us focus on what meds we want to look at.”
Nguyen adds: “I’d like to look at how many prescriptions people are being discharged with. If it’s 11 meds, that’s a lot to manage. It comes down to adherence. You try to make it as simple for them as possible.”
The team composes a work agenda on the white board for the days leading up to their presentation. They each bring their particular area of expertise or primary interest into the discussion.
“Maybe we should factor in respirations,” says third-year medical student Adam Vohra, who is seeking a joint MD/MBA degree, before qualifying, “but it’s usually not documented that well.” They debate, argue, and eventually agree on other variables to include in the analysis: weight, pulse, blood pressure, temperature, fluids, and diagnosis at admission compared with diagnosis at discharge. They have this data for two cohorts of patients: those who received the new intervention, and the control group of patients who got the old, less-intensive protocol.
I’ve got the only class I’ve heard of that does big data and brings in the teaming skills as well.
“It’s a pretty large change we’ve initiated,” Dr. Corey Tabit, a cardiology fellow and the project sponsor at UCM, had told Adelman back in February. “We’ve focused on standardizing the heart-failure care at the hospital, at home, and also in the emergency department.”
When patients are admitted to the hospital with a diagnosis of acute heart failure, they are evaluated by nurses and are assigned either to the preexisting course of treatment (the control group) or to the new intervention. Patients in the new pathway are given an intensive education program, specifically designed by the nurses to be easy to follow after discharge from the hospital. Pharmacists, nutritionists, occupational and physical therapists, and social workers also spend time with the patients and offer management strategies for their condition. All efforts aim at keeping the patient healthy enough to live comfortably at home.
“It isn’t one specific intervention; it’s the bundle, the whole program,” said UCM cardiologist Rupa Mehta Sanghani, who worked with Tabit on the project. “The idea is that the more of them you do, the lower your readmission rate.”
In Tabit’s early evaluation, the new protocol appeared to show an 80 percent reduction in 30-day readmissions compared with the control group. But he recognized the need for a more sophisticated analysis—which is where Booth came in.
An Economic Disaster Area Needs Help
A data analytics lab course could be designed around any industry: technology or manufacturing, media or telecoms. But healthcare generates so much information and is so resource intensive, that “there is this massive interest in using big data to improve health-care delivery,” Adelman said. Healthcare is such an economic disaster area in the United States that cost-effective solutions in the sector need to be found. It’s a growing area of interest at Booth. “More and more students want to get into healthcare,” Adelman said.
When Steele, on the UCM student team, was originally choosing among MBA programs, Adelman’s class “sealed the deal for me at Booth,” she said. “This was a professor offering a class in exactly what I want to be doing.”
Matthew L. Primack, ’13, (XP-82), vice president of clinical institutes and business development at Advocate Condell Medical Center in Libertyville, Illinois, sponsored a project several years ago. “How you analyze data is fairly straightforward,” he said. “What you do and how you implement the changes based on the data is a very, very different art form. What Dan Adelman did very well was that he made sense of the data analytics and turned it into actionable opportunities.”
By early May, the Healthcare Analytics Laboratory heart-failure readmissions team zeroes in on determining actionable opportunities out of 36 variables—in demographics, clinical data, and treatment information—for thousands of patients. The students have run about 1,000 iterations of their statistical program in an attempt to discern a small subset of variables that would be most likely to predict which patients entering the hospital are at highest risk of returning after discharge.
“We are better able to predict low risk than high risk,” Nguyen said. “We can predict 80 percent of those who won’t be readmitted, but only 60 percent of those to be readmitted.” The students need to try more variables to get a more precise picture of what issues predict future readmissions.
“We know from the literature that the time of greatest risk is right after discharge from the hospital; then the risk goes down,” Vohra said. “Then it goes back up at some point later in time. The problem is, we don’t have a good predictor of when that comes.”
The team won’t get their final data dump from the hospital until mid-May, which forms the basis of the analysis they have to present at the final class on June 3. Only a week later, they have to present their findings to the hospital sponsors.
“If you have the right data, and you know what this data is about, the statistical analysis can be done very smoothly. You know what you have to do,” said Yang, the statistician. “The difficult part is identifying the right data and doing the quality control.”
How you analyze data is fairly straightforward. How you implement changes based on the data is a very different art form.
At some schools, Adelman said, the students come to class, complete a
project, and they’re done. “They aren’t held accountable for the
discipline-based learning they receive.” He’s developing what he
considers “more of a Chicago version of experience-based learning, which
leverages our unique strengths.” Students in his course not only must
master content-anchored statistics and analytics work, but as part of
the soft-skills training they have to draft team contracts, write
personal reflections, and submit peer feedback. Communication skills are
key. Every two weeks, each team makes a presentation to update the rest
of the class on their research. Adelman critiques the content, flow,
and persuasiveness, as well as the student’s speaking style and body
For student Yizhen Dong, an early
presentation, when Adelman had a public-speaking coach on site, was an
eye opener. “I say a lot of ‘ums,’ and I fill in words that don’t add
any value to the presentation,” Dong realized. “It’s something I’m
trying to work on.”
The chance to combine data analytics
with the soft skills—presentation, team dynamics, and storytelling—is
unique, said Harry L. Davis, Roger L. and Rachel M. Goetz Distinguished
Service Professor of Creative Management, who pioneered experience-based
learning at Booth in the 1980s. “This is, in a sense, a start-up, with
really interesting opportunities for you here at Booth that you cannot
get anywhere else,” Davis told students in the first class session.
Preparing for the Big Reveal
the June 3 class presentation, the UCM team has identified that, in the
roughly six months the hospital’s new protocol has been in place, the
intervention patients each received an extra 90 minutes of care. The
cost savings from the intervention would be $385,000 a year—the
equivalent of three new nurse practitioner hires.
students have also distilled the predictive algorithm to eight yes-or-no
questions for the hospital staff to assess on each patient’s admission.
They have found the questionnaire to achieve the same predictive effect
as the far more complicated medical models cited in the published
literature on this topic.
Adelman appreciates their data
insights—and he also knows this is a dress rehearsal of sorts for the
team’s presentation next week at the hospital. The professor offers some
directorial advice: “Start thinking how the different components fit
together, and you can build some drama.”
Showing Them the Money
8 a.m. on Wednesday, June 10. In a small lecture hall at UCM, the
cardiology team, as well as the hospital’s C-suite executives, awaits
answers—from Booth students. The students wear jackets and ties, skirts
and heels; the vascular surgeons are still in their scrubs.
sets the stage: “UCM has an above-average rate of readmissions. But
even if you control for poor case mix, it’s still in the middle of the
pack.” Each student takes the floor in turn and advances the narrative.
They look sharp; they sound knowledgeable and well rehearsed. The entire
presentation has momentum and confidence, poise, éclat.
unveils the key finding around the intervention protocol: “It was
effective . . . but not that effective. OK, so now we can all go home
and relax.” She has the cardiologists laughing. The students lay out the
numbers, explain the analysis, and give recommendations to the senior
executives. They recommend that to achieve statistical significance, the
program would need 420 participants in the intervention group and 420
in the control group. That would take about a year.
listens, rapt, and says he couldn’t be more pleased. “The quality of
analysis is just superb. It’s easily the equivalent of what we would
have gotten from a consulting firm. The capabilities of these students
Sanghani says she can’t believe how much
was accomplished between January and June. “I would love to have you
guys keep going with the data.”
“Is this class every quarter?” interjects cardiologist Kirk T. Spencer. “Because we have lots of problems.”
Searching for the Next Problem
Adelman is currently seeking the kinds of problems that will challenge
his next cohort of students when the course begins again this spring.
Meanwhile, the UCM cardiology team continues to build on the insights
from the Booth report. “The model they came up with was very
impressive,” Tabit said. “It has performed remarkably well. It has only
eight variables, so it’s easy to work with. We have put it into a larger
risk-stratification scheme. It’s by far the most important piece of the
The Booth student findings led the hospital to
streamline their process and better predict patient risk. In the July
through October period, readmissions fell to between 8 and 9 percent.
UCM Medicare payment penalties are down to 0.38 percent for fiscal 2016
from 0.54 percent in 2015. “For $100 per patient, I can reduce
readmissions by 50 percent,” Tabit said. Assume each readmission costs
the hospital around $10,000. “That’s a tremendous cost savings to the
health system. The Booth project let us figure out which patients need
these aggressive interventions and which don’t.”
is rightfully proud. “The analyses were just impressive,” he said.
“They certainly lived up to the school’s reputation. Any sponsors
sitting there had to be thinking, ‘Damn, these guys are good.’”
—By J. Duncan Moore