  Patients do not access physicians at random but rather via naturally emerging
networks of patient flows between them. As retirements, mass quarantines and
absence due to sickness during pandemics, or other shocks thin out these
networks, the system might be pushed closer to a tipping point where it loses
its ability to deliver care to the population. Here we propose a data-driven
framework to quantify the regional resilience to such shocks of primary and
secondary care in Austria via an agent-based model. For each region and medical
specialty we construct detailed patient-sharing networks from administrative
data and stress-test these networks by removing increasing numbers of
physicians from the system. This allows us to measure regional resilience
indicators describing how many physicians can be removed from a certain area
before individual patients won't be treated anymore. We find that such tipping
points do indeed exist and that regions and medical specialties differ
substantially in their resilience. These systemic differences can be related to
indicators for individual physicians by quantifying how much their hypothetical
removal would stress the system (risk score) or how much of the stress from the
removal of other physicians they would be able to absorb (benefit score). Our
stress-testing framework could enable health authorities to rapidly identify
bottlenecks in access to care as well as to inspect these naturally emerging
physician networks and how potential absences would impact them.
