Motivation

Resident doctors can be hard to find

aging population (retirement)

rural depopulation

short-term shocks (COVID-19)

Which regions and specialisations are
most at risk of severe shortages?

Image credit: Yurii Karvatskyi

Naive approach

Counting doctors per patient and region.

Issues with this approach:

heterogeneous doctor capacity

heterogeneous doctor distribution & accessibility

different scenarios: fast (COVID) vs. slow (retirement) shocks

More sophisticated approach

Encode accessibility and distribution of doctors in a patient sharing network and simulate patient dynamics for different scenarios.

Assumptions:

When patients loose their doctor, they are more likely to go to a doctor they had already visited in the past.

When patients don't find a doctor for a long time, they "drop out" of the (resident) healthcare system.

Constructing a patient sharing network

Records of 103 million visits to resident doctors in Austria in 2018.

Doctor specialty

Doctor location

Number of patient visits & patient IDs

Maximum capacity estimation via opening hours.

Opening hour information collected from herold.at

Assign median capacity of top 10% doctors to all doctors with similar opening hours.

Constructing a patient sharing network

Characteristics of selected specialities

Patient relocation simulation (one speciality)

Initial condition: all patients have a doctor

1. Some doctors are removed to simulate a shock.

2. Each patient that lost their doctor selects a new doctor with probability p, where p ~ N patients shared with original doctor.

3. Each doctor accepts new patients until the capacity is filled.

4. Steps 2. and 3. are repeated for a maximum of 10 times or until each patient has a new doctor.

5. Patients that haven't found a new doctor are lost.

"Fast" shocks

Recovery after removing 15% of doctors

Recovery after removing 15% of doctors

Impact of fast shocks on different specialists

"Slow" shocks

Lost patients and capacity as doctors are removed

System limits: How many doctors can we remove?

Removing one doctor
in each time step until
1% of patients are lost.

System limits: How many doctors can we remove?

Removing one doctor
in each time step until
1% of patients are lost.

System limits: How many doctors can we remove?

Removing one doctor
in each time step until
1% of patients are lost.

System limits: How many doctors can we remove?

Removing one doctor
in each time step until
20% of capacity remains.

System limits: How many doctors can we remove?

Removing one doctor
in each time step until
20% of capacity remains.

System limits: How many doctors can we remove?

Removing one doctor
in each time step until
20% of capacity remains.

Summary of results

The patient sharing network encodes accessibility and doctor distribution.

Comprehensive health record data allows us to simulate a situation that is directly applicable to the situation in Austria.

Simulating fast and slow shocks allows us to find different limits of the healthcare system, differentiated by speciality and region.

Stress-testing the resilience of the Austrian healthcare system using agent-based simulation Kaleta & Lasser et al., Nature Communications (2022).