A wave of retirements, a mass quarantine or a lack of interest in working in the field: there are numerous reasons why a higher-than-average number of medical practices could close at the same time. The system can usually compensate for some of these shortcomings. But there is a point where the situation can become critical. Until now, it was not possible to predict precisely where this point is. A research team at the Complexity Science Hub Vienna (CSH) now proposes a method to identify (tipping) points at which adequate medical treatment of the population can no longer be guaranteed: the first stress test for a healthcare system .
In their most recent publication in Nature Communications, the scientists use data from Austria to show how many resident doctors and medical specialists can leave before patients cannot find a new doctor within a reasonable distance. However, the authors stress that their method can be used in any country that has the relevant data available.
How the stress test works
For their simulations, the scientists had access to anonymized Austrian patient data from 2018. They combined it with publicly available data on doctors’ office hours. To make their multidimensional dataset more accessible, the CSH visualization team also developed an interactive visualization.
Our data show that patients do not choose doctors at random but through naturally emerging networks.”
Michaela Kaleta, CSH researcher, one of the first authors of the work
For example, if a gynecologist goes on vacation, his patients usually go to another particular doctor as a substitute, and vice versa. If the gynecologist herself retires, it is very likely that her patients will permanently switch to this colleague.
“We see that these patient-doctor networks and patient flows are surprisingly constant, especially in rural areas,” says Jana Lasser (Graz University of Technology); the complexity scientist, first shared author, has been involved in this work since her time at the CSH.
Researchers now feed real-world networks into a computer model and “shock” the system by removing doctors, as in a disease wave, for example. “We can take one network node, i.e. the doctor avatar, out of the system at a time and see where patients are moving within the network, and at what point sufficient healthcare can no longer be provided” , says Lasser.
Ophthalmologists in Styria and Vorarlberg
The scientists found that the resilience of local health care depends on more than just the number of doctors in that region, known as “physician density.”
“Take ophthalmology in Styria and Vorarlberg as an example,” explains Lasser. “According to our 2018 data set, both Austrian provinces had about the same number of employed ophthalmologists per person, i.e. about the same density of doctors. However, we see that a shock in Vorarlberg can be four times larger than in Styria. before patients no longer find the right eye treatment.” According to this model, in Styria 7% of ophthalmologists can be removed before health care becomes critical, compared to 28% in Vorarlberg.
“Our stress test shows very precisely in which regions of Austria the healthcare system is resilient and where the healthcare authorities should take urgent measures to improve the situation,” says Lasser. “Regional differences in Austria can be quite significant.”
Stress testing allows for better long-term planning
“In many areas, such as the banking sector, stress tests have long been carried out to better prepare for crises,” emphasizes project leader Peter Klimek (CSH and Medical University of Vienna). “For healthcare, a stress test has been missing for a long time.” However, the pandemic has shown that medical care can quickly reach critical limits when large numbers of staff are not available at once.
“We will also have to deal with these crises in the future,” says Klimek. “This is why all countries face the challenge of rebuilding their healthcare systems: not only must they be kept affordable, but they must also be restructured to make them more resilient to shocks.” The approach presented here is one way to achieve this goal. “We strongly recommend that health authorities look at how well their networks can absorb shocks. We can only find the balance between cost efficiency and crisis resilience when we know the weaknesses and strengths of the system” , concludes Klimek.
Source:
Complexity Science Hub Vienna
Journal reference:
Kaleta, M., et al. (2022) Stress testing the resilience of the Austrian healthcare system using agent-based simulation. Communications of nature. doi.org/10.1038/s41467-022-31766-7.