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What Can Different Health Care Systems Learn from One Another? | School of Public Health

What Can Different Health Care Systems Learn from One Another? | School of Public Health


Irene Papanicolas, the director of CHeSS, argues that it is unhelpful to ask which health system is “better.” She says that many comparisons of health systems falsely reduce complex metrics to singular causes, and ignore significant differences between apparently similar systems. “What is the best health care system?” she asks, “Is it the system that has the best population health outcomes, the best cancer care, the best patient experience, that spends the least, or that is free at the point of care?” 

Papanicolas, who is professor of health services, policy and practice, points out that Switzerland, Germany and the Netherlands all have private insurance, yet perform better than the U.S. on life expectancy and spending. “If you take 10 different health system outcomes, there’s no country that’s going to be consistently the best at all of them.”

Faced with this complexity, CHeSS and its partners are working to frame questions in ways that yield the sort of empirical findings that can inform policy. Where are health care prices increasing fastest? Do some drug combinations significantly increase the risk of dangerous falls in older adults? Are fewer new drugs made available when national health authorities balance health benefit against cost?

Research across Borders

Enrique Bernal-Delgado is a founder and senior scientist at the Data Sciences for Health Services and Policy research group at the Institute for Health Sciences in Aragon (IACS).

Public health researchers are accustomed to narrowly tailoring their research questions, tidying up the messy world of data in order to isolate a dependent variable—comparing the same disease, similar patients and corresponding care interventions. But the many profound differences between national health systems present unique challenges for researchers who wish to compare systems treating different populations shaped by different cultures, using different resources, deployed by professionals with different training.

Enrique Bernal-Delgado has been collaborating on international health data comparisons for more than 25 years. Bernal-Delgado founded the Data Sciences for Health Services and Policy research group at the Institute for Health Sciences in Aragon (IACS), in Spain, where he now serves as senior scientist. He helped to pioneer the “federated research” methodology that is at the core of CHeSS’s work, in which research “nodes” in different countries can produce findings in response to the same query while maintaining stringent national data privacy standards.

Health data is highly sensitive, which typically prevents researchers from sharing rich data sets across national boundaries. In the absence of a shared data infrastructure and research methodology, researchers are limited to comparing aggregated data. But these sorts of coarse comparisons are often open to interpretation: Even when similar terminology is used, is a “hospitalization” in Sweden comparable to one in the United States?

“So they go to the hospital, how long does it take to have surgery?” says Papanicolas. “How long do they spend in the hospital? Where do they go when they leave the hospital? Do they have rehab? How long do they have rehab? Where does rehab happen? Is it in an institution? Is it at home? Do they have a primary care physician? How often do they see their primary care physician for the year that follows? Do they go back to the hospital? How many times? What drugs are they on? Does their drug regimen change?” This sort of conceptual slippage makes it hard to answer the important questions. “What does their care look like over the course of a year?” 

a hand with a test tube on a blue backgroundIn 2018, before joining Brown’s faculty, Papanicolas led the formation of the International Collaborative on Costs, Outcomes, and Needs in Care (ICCONIC), which now brings together research teams in 12 different countries to meet this research need and shape policy through partnership.

To do rigorous comparisons using richer data from linked sources—including time-dependent variables—required a new methodology, one that satisfied national legal requirements as well as what Bernal-Delgado calls “layers of interoperability.”

“Semantic interoperability is not just about speaking the same language—we don’t speak the same language!” says Bernal-Delgado. “But we need the concepts to be the same.” This often means working with clinicians to establish a rigorously consistent vocabulary. Organizational and technologic interoperability ensure that each partner is able to conduct data queries in a consistent and meticulous fashion. “It’s like making a recipe, but you have a different cook for each component of the meal,” says Liana Woskie, who also collaborates with Papanicolas. 

Setting up this federated research infrastructure took an entire year, but Papanicolas hopes that things will move faster going forward: “I can accept the first project taking a year, if the second one takes a month.” 

Natural Experiments

Very occasionally, health systems experience an identical shock that makes comparison a little simpler. During the COVID-19 pandemic, a new virus tested every national health system, yielding widely differing responses. Countries pursued different policies to treat people and contain the spread of the virus, policies shaped by underlying features and capacities of their health systems. With support from The Health Foundation, a British charity, Papanicolas and her partners are studying whether lockdowns and other public health interventions, like school closures and stay-at-home orders, disrupted care. But unlike pundits comparing Sweden to America, they are comparing similar patients in different systems.



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