Anupam “Bapu” Jena, M.D., a medical resident at Massachusetts General Hospital in Boston, recalls noticing that a significant number of cardiologists were away from the hospital during the week of the annual cardiology conference.
He wondered if this fact might influence the health outcomes of patients who had a cardiac event that week each year, and as a researcher who studies how random events affect health, he used the data to were collected and investigated.
Surprisingly, when more heart surgeons left town, mortality rates fell by 10 percentage points.
“There is a philosophy in medicine that less is more,” said Jenna, the Joseph P. Newhouse Professor of Health Policy at Harvard Medical School, a physician at the Massachusetts General Hospital School of Medicine, and a faculty researcher at Massachusetts General Hospital. says. National Bureau of Economic Research. His hypothesis is that fewer interventions may improve outcomes for some patients.
This was one of several examples Jenna presented during a session at Learn Serve Lead: The AAMC Annual Meeting held Nov. 9 in Atlanta, Georgia. It showed how random acts leading up to a city can have an impact. Health effects.
“I spend most of my time researching issues at the intersection of medicine and economics,” Jena explained. “It’s like Freakonomics Encounter medicine. ”
Jena posed a variety of questions to the audience and explained the brainstorming process he and his team go through when deciding which phenomena to study.
“The exercises we’re going to do are spending several hours each week with my colleagues,” Jena said.
Although he has published many observational studies and highlighted them in co-authored books, Random medical actions: The hidden forces that drive doctors, influence patients, and shape our health.without clear policy solutions, Jena shared one impact of his research.
After he used data analysis to show that children born in the summer were less likely to receive a flu shot than those born in the fall, the pediatrician’s office wrote him a letter saying, The city will begin hosting flu vaccination clinics for children celebrating their birthdays in the summer. October.
“Most of the ideas we have are not good ones, and most of them don’t make it into papers,” Jena says. “All you need is one good idea, and that’s enough.”
Jena encouraged clinicians in the audience to be curious and use observation and creative thinking to look at old problems anew.
And finally, Jena promised not to study data on patient outcomes during the week of the AAMC annual meeting.
Before the meeting AAMC News We spoke to Jena about the inspiration behind the experiment and what she learned about randomness in health and medicine through her research.
What inspired you to continue your research at the intersection of economics and medicine?
I fell into it by accident. When I was an undergraduate at the Massachusetts Institute of Technology, I worked in a lab and planned to pursue an MD and PhD in basic science. And as I was traveling around the country interviewing for MD-PhD programs, the program director at the University of Chicago said to me, Do I want to get a PhD in economics instead?’ I had never thought about it before. But I went to the economics department that afternoon, met with some professors, applied a week later, and got into economics and medical school just a few months later.
You explore a variety of “random acts” in your book. Can you describe the process you and your colleagues go through when deciding which phenomena to study?
One is that natural experiments are very common in the field of economics as a tool to study phenomena. It is less common in medicine and epidemiology. So part of my interest in these tools comes from my professional background in economics, but also from the specific types of questions I was interested in. Freakonomics-meets-medical style questions. I was influenced by my PhD advisor, Steve Levitt. Freakonomics. He was doing interesting research in the field of crime and other social behavior. I thought, “Wouldn’t it be interesting to apply similar thinking to medicine and health, using large-scale data, natural experiments, and creative questions?” And we took off from there.
You and your co-authors explore everything from how a child’s month of birth affects their chances of getting a flu shot to how doctors’ politics affect the type of care patients receive. We investigate a wide range of natural experiments. How do you decide what factors or phenomena to study?
Much of the research in this book is based on stories I have experienced that most people would probably overlook. I mentioned my birth month and the flu. It happened because I was at the pediatrician’s office and couldn’t get a flu shot. [yet] My son has a birthday in August so I can use it. [and his annual checkup was scheduled close to his birthday, as is customary]. Flu shots aren’t typically available at pediatricians’ offices until early September, so if my son had been born a few weeks later, he likely would have gotten the shot at his annual checkup. [Instead, as Jena explains in his book, he had to make an additional visit to the pediatrician for his son’s flu shot. This extra visit creates a barrier to care for many families whose children have August birthdays, reducing flu-vaccine coverage, his experiment found.]
I think we see many such phenomena in the real world. In terms of what makes us settle for a question, I think: [it’s] There are several points: One is that you need data that can answer that. Second, some experimentation is needed. Whether it’s surgery or not, medication or not, whatever it is, something that creates different paths that people happen to take. Third, look for outcomes that are measurable and significant. Therefore, getting the influenza vaccine is probably important. It could be life or death. The last thing is probably the most important criterion. That means I try to work on things that can be explained to everyone. You don’t need to be an expert in medicine or economics.
What kind of reactions do you get in conversations with colleagues, friends, and family who probably have no experience with natural experiments or economics?
There are various reactions. I have a close friend from my residency at Massachusetts General Hospital who always made fun of me every time I presented my research results. he said: Bapu has just published research on how water gets wet. ”
One worldview is, “Of course, if roads are closed, people can’t get to the hospital, and delays in treatment may lead to increased health problems, in this case mortality, due to the marathon.” . . If you think about it, it’s obvious. Of course, that’s not obvious when you think about designing for these kinds of large-scale events that involve massive infrastructure disruption. people don’t think about it. Everyone understands my question. Sometimes they actually say: Why study it? ”But I think for the most part, people think, How did you come up with that idea? It makes sense if you ask me, but I wouldn’t have thought about it. ‘That’s the dominant response, and that’s good, but if it wasn’t, I’d have a problem.
I’d also be interested in how cardiologists respond to your research showing that heart attack mortality rates decrease during the week of the cardiologist’s annual conference?
When that study was published, it actually Freakonomics podcast episode The president of the American Heart Association at the time had some interesting things to say about the study, saying that patients were not harmed by the cardiologist’s absence. He was right about that!
I think the tools of natural experiments were very familiar to economists. This enabled them to understand the research and believe in the results. In medicine, it’s even more difficult. Because you look at studies like this and think, “So what could be going on?” And if you’re a cardiologist and you’re looking at this, the last thing you want to think about is that outside of these meeting dates, you might be doing things that aren’t optimal for your patients. But clearly it has to be. Not everything we do in medicine is based on randomized controlled trials. And even if it does, it doesn’t always apply perfectly. And it would be a mistake to think that nothing better could have been achieved in medicine, or indeed in any profession.
There is a chapter on what makes a good doctor. What did you learn from your research?
The study doesn’t tell us much about what makes a good doctor, but perhaps what the study is telling us is whether a doctor is good, bad, or good at certain characteristics of a doctor. Even if you have a preconceived notion that something is bad, it may not be so. Be completely truthful. For example, are doctors trained at highly prestigious medical schools more competent? [patient] What’s the result? That’s a belief someone might have. Or you may believe that older doctors produce better outcomes than younger doctors. In fact, we found that, outside of surgery, older doctors tend to have worse outcomes than younger doctors.
But the point is not that we should choose a younger doctor, or a female doctor, or a doctor trained outside this country. Because we show that women and foreign-born doctors also have better outcomes. Instead, the solution is to say that if you’re afraid of seeing a younger doctor because you think he might be a little less experienced, that’s probably not a big concern.
What has your research brought to the field of medicine, and has it influenced the way you practice and live your life?
Most of my contribution is to bring this use of natural experiments to the fore, but also to inject creativity and fun into the kinds of research people can do. As for me as a clinician, it’s hard to say because I cover so many different subjects. I think there are some examples where we think differently. There’s this paper about left-digit bias, so people who are 79 years old think they’re in their 70s, and people who are 80 years old think they’re in their 80s.
Now, because of that research, I can look at someone when they’re in the hospital and have a very good idea of what their real age is and what their ability to withstand treatment and what their ability to not tolerate is. I can say that I have started to think more carefully and thoughtfully. It’s like being swayed by heuristics that can lead to cognitive biases in clinical decision-making. So I might be a little more careful about that.
One of the things I try to do in the medical field is spend more time with people. I have worked in many busy clinical settings. Often, the medical teams I work with, residents and interns, have so much to do that it is often difficult to spend time with patients. As I get older, I think it’s more important than it was 10 years ago.
What we highlight in this book are areas where people can go wrong in thinking about problems. Physicians may come to the wrong conclusions not because they are not working hard, but because when there is too much information to deal with and the time needed to make a decision is short, the decisions they make are biased. Because there is a possibility of it getting in. And we don’t know how much better clinical outcomes would actually be if we gave clinicians more time to make these decisions, if we gave them more time to talk to patients. However, because the amount of time clinicians spend with patients is becoming shorter rather than longer over time, I expect these issues we highlight to become more pronounced rather than less over time. I’m doing it.
