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

Abstract

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.

Foundations of complexity economics

“…There are now general texts on the subject [89][90][91][92][93][94][95][96] and research across subfields such as macroeconomics [97][98][99] , labour economics 100,101 , institutional economics 102 , environmental economics [103][104][105][106] , finance [107][108][109] , economics of disease transmission 110,111 , distribution of firms' sizes 112 , scaling laws 113,114 , ergodicity in economics 115 , technological innovation [116][117][118] and economic development 119 . If there are trends, they are towards more behavioural realism, grounding models on large data sets 120 , using computer experiments to study and design systems, and understanding how macro patterns emerge from micro assumptions.…”

Stress-testing the Resilience of the Austrian Healthcare System Using Agent-Based Simulation

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