Different countries around the world have very different ways of providing health care. In order to learn from these varied systems—each with its own unique goals and priorities—and to compare their outcomes, researchers must devise new methodologies of working with highly sensitive data to overcome not only language differences, but vast organizational, operational and infrastructure differences between countries. 

Brown’s new Center for Health System Sustainability (CHeSS), led by Professor Irene Papanicolas, aims to standardize data from across global health systems, then compare them in order to inform policy choices and improve health care value and patient care.

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Welcome to Humans in Public Health. I'm Megan Hall.

In the past few years, the field of public health has become more visible than ever before, but it's always played a crucial role in our daily lives. Each month, we talk to someone who makes this work possible. Today, Irene Papanicolas.

Often we're talking to one another. And, you know, we take for granted that something is the way it is, because that's how we've always known it, you talk to someone else from a different country, you're like, Oh, that's not how it's done in your system? I mean, anecdotally, this happens to me all the time.

Irene has lived all over. She grew up in Greece, went to school and worked in the U.K., and now lives in the U.S.

I moved here last year, and my kids had to go for a check up. And I was surprised at how long the check up was, how often check ups happen here. It's so different from what I was used to in the U.K. In the U.K, after two and a half years, you don't have recommended check in with the pediatrician – ever.

Really?

That's it, that’s the end. Two and a half years.

Oh my gosh

You go to the doctor if you're sick, but you don't have a wellness visit every year.

But they're not measuring you, They're not checking your eyes. They're not weighing you like they do with kids

No, that stops. In Australia, it's the same. I think but after five years, not 2.5, and then Germany, and Sweden have not every year but every three years.

The idea behind Irene’s work with the other researchers’ work is pretty straightforward. They’re noticing differences like this between different health care systems, and they want to see what sort of an impact it has. What are the advantages and disadvantages of these systems?

It's like we're experimenting with care delivery to see what happens. And what all we're trying to do is understand if that makes a difference.

But here’s where it gets messy. Actually comparing how these differences affect people’s health in different countries, isn’t straightforward. There aren’t simple experiments you can run, and it requires so much data.

So how do you collect all that data? And how do you standardize all that data? It sounds like a gigantic headache.

Yeah, it's worse than it sounds. Because all of this is patient level data, which can't leave any of the countries. It can't leave any of the computers it's based on, it's highly protected and secure. So we're trying to do all these analyses, but we can never put the data together.

Irene is the director of a new center at Brown called the Center for Health System Sustainability, which serves as a home for this network of international researchers. They call themselves ICCONIC-

ICCONIC stands for the international collaborative on costs, outcomes, and needs in care. So it's got two Cs.

They all work together to compare the data from their home countries, so they can make big global comparisons about how health care systems work.

So what we do is we have a research partner in every one of these countries, who is very familiar with the data that they have. And we agree, what is the patient we want to capture?

The idea is to find patients across the world who are dealing with the exact same health problems, and then see how their treatment compares. As one of their first projects, they started with something simple:

One of the first things we looked at was a patient who has a hip fracture, and an older patient breaks their hip in France, the U.S., the UK, Canada, New Zealand, you know, the list goes on? What does their care look like over the course of a year?

To make this comparison work, Irene and the researchers had to come up with a category that allows them to see every hip fracture patient across all the countries.

This one is relatively easy, because it's almost certainly going to be characterized by emergency admission, somebody breaks their hip, they go to the hospital to have it treated.

No matter what country you're in?

No matter what country you’re in.

But other definitions can get complicated when you’re looking at data from different countries.

What is the hospital visit and what is rehab? which also differs across countries. So where a hip fracture patient gets rehab is not the same across countries. So in the U.S., they'll go to a skilled nursing facility in Germany, it'll happen in the hospital in other countries that happens at home with the nurse. So how do you find that in the data?

That’s where working with people in other countries becomes really helpful.

I think we've all realized how important it is that we have those partnerships because the data reflect differences and in the systems themselves, the cultures of the systems, the nuances and everyday interactions that you wouldn't be able as a researcher unfamiliar with that country to really interpret or disentangle. We're trying to get as many people in the room as possible and often on our projects, you know, we're the researchers, but we bring in– if we’re working on hip fracture, we bring in an orthopedic surgeon, a primary care physician and a policymaker from each country to present results to them to say, we don't know why these look so different. Is this a data error? Or like, what's going on? Do you know? Like, why do you have so many, you know, days in rehab? Often we have no idea whether the variation we’re seeing is something we should expect, or is an artifact of some miscoding or problem in the data. And getting those voices in the room will help us understand, oh, the guidelines are different, or no, we do this this way. Or this is because of the billing system.

Once they sorted out all these definitions, each researcher gathers the data in their country to answer the same questions:

So they go to the hospital, how long does it take to have surgery? How long do they spend in the hospital? Where did they go after the hospital? The list goes on.

Then, comes the task of making sure all this data is formatted the same way, or as Irene calls it, “harmonizing the data”

Because they, you know, age could be measured in terms of – we've got your birth year, we've got your birthdate or we have your age, or we have an age range. And then we go and we harmonize that so that we have the same variable coded in the same way across all the datasets for all of the variables that we need.

Then, because the privacy laws prevent data from leaving its home country, each researcher has to analyze that data on their own.

Every country has to download the same statistical software, the same version to run the same code, because the data now has all been harmonized. And then we take those results, which are aggregated, so now can be shared. We take those, we bring them back to the lead site. So in this case, Brown

And then, finally, they can make conclusions about how these different health care systems compare.

This still sounds like a gigantic headache, or maybe, you know, unraveling a plate of spaghetti. So is, with all of these challenges, is it still possible to learn from other health systems? Is it still possible to make fair comparisons?

Yeah, I think we have enough examples of where we've been able to do that and find actually really interesting results. Even something as simple as if I go back to the hip fracture patient, how long a patient who has a hip fracture spends in institutional care across countries, are extremely different.

Their research found that U.S. patients spent the least amount of time in inpatient treatment compared to patients in other high-income countries. But they also spent the most time in rehab.

Assuming patients want to go home and spend more time at home than in an institution, understanding whether there's something we can learn from that and find what are the care processes that enable patients to go home? And can we put those in place in the systems where they're spending more time in an institution in a way that doesn't compromise their outcomes, that's a win win, those are likely to be both cheaper and probably preferred by most patients.

And these kinds of detailed comparisons can allow for more productive conversations about health care systems.

And it seems like in that way, it's more attainable than overhauling the entire U.S. health care system, like saying, oh, we need to run our health care system, the way England does. You can be tweaking within a system that already exists and see improvements in outcomes.

Yeah, that's the idea. We think that there are changes that we can make in, in practice or in the interfaces between care. So between like hospital and rehab, or primary care and outpatient care, that we can learn from other countries without changing the way the entire system is financed or organized. But that we can change the care and optimize the care for particular patient groups, by learning what other countries do.

You've been doing this work for a long time. But now you're at Brown, what does the new center at Brown change about what you're doing?

It brings a home to a lot of this work. This work happened, because we were a bunch of individuals interested in working together, but as it's grown, it needs more of a home. A center that can support really long term development of methods, producing a space where we can also make our results more accessible to a non-research audience. So we have dreams of putting up interfaces where we can make our aggregated data public, and people can go in and look at how care delivery varies from one place to the other or, the differences across countries and all of the research that we find. It becomes kind of a center point that we can house all of the research. So not the physical data, but all of the different parts and bring them together as a coordinating base and home for the work.

You’re obviously really passionate about this. What gives you your passion, despite all the headaches when it comes to harmonizing all this data?

So I don't think of them as headaches. Every time I find one of those things, I just think of it as like a fun problem to solve. So I guess I just find it fascinating? Every time there's one of these differences. It's like, wow, really, people do that so differently? And it's just like another curiosity that is interesting.

Irene, thank you so much for taking the time to talk with me.

Thank you so much for having me.

Irene Papanicolas is a professor of health services, policy and practice at the Brown University School of Public Health.

Humans in Public Health is a monthly podcast brought to you by the Brown University School of Public Health. This episode was produced by Nat Hardy and recorded at the podcast studio at CIC Providence.

I'm Megan Hall. Talk to you next month!