Quantification of the resilience of primary care networks by stress testing the health care system

Lo Sardo, D.R., Thurner, S., Sorger, J., Duftschmid, G., Endel, G., & Klimek, P. (2019). Quantification of the resilience of primary care networks by stress testing the health care system. Proceedings of the National Academy of Sciences 48 23930-23935. 10.1073/pnas.1904826116.

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Project: Smart Resilience Indicators for Smart Critical Infrastructures (SmartResilience, H2020 700621)


There are practically no quantitative tools for understanding how much stress a health care system can absorb before it loses its ability to provide care. We propose to measure the resilience of health care systems with respect to changes in the density of primary care providers. We develop a computational model on a 1-to-1 scale for a countrywide primary care sector based on patient-sharing networks. Nodes represent all primary care providers in a country; links indicate patient flows between them. The removal of providers could cause a cascade of patient displacements, as patients have to find alternative providers. The model is calibrated with nationwide data from Austria that includes almost all primary care contacts over 2 y. We assign 2 properties to every provider: the “CareRank” measures the average number of displacements caused by a provider’s removal (systemic risk) as well as the fraction of patients a provider can absorb when others default (systemic benefit). Below a critical number of providers, large-scale cascades of patient displacements occur, and no more providers can be found in a given region. We quantify regional resilience as the maximum fraction of providers that can be removed before cascading events prevent coverage for all patients within a district. We find considerable regional heterogeneity in the critical transition point from resilient to nonresilient behavior. We demonstrate that health care resilience cannot be quantified by physician density alone but must take into account how networked systems respond and restructure in response to shocks. The approach can identify systemically relevant providers.

Item Type: Article
Uncontrolled Keywords: coevolving networks; dynamics of collapse; robustness; quality of care; patient-sharing network
Research Programs: Advanced Systems Analysis (ASA)
Depositing User: Luke Kirwan
Date Deposited: 18 Nov 2019 07:49
Last Modified: 27 Aug 2021 17:32
URI: https://pure.iiasa.ac.at/16167

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