[Source: Proceedings of the National Academy of Sciences of the United States of America, full page: (LINK). Abstract, edited.]
Quantification of the resilience of primary care networks by stress testing the health care system
Donald Ruggiero Lo Sardo, Stefan Thurner, Johannes Sorger, Georg Duftschmid, Gottfried Endel, and Peter Klimek
PNAS first published November 11, 2019 / DOI: https://doi.org/10.1073/pnas.1904826116
Edited by Timothy George Buchman, Emory University School of Medicine, Atlanta, GA, and accepted by Editorial Board Member Simon A. Levin October 1, 2019 (received for review March 27, 2019)
We shock a full-scale simulation model of a national health care system by locally removing health care providers. We measure resilience of the system in terms of how fast and to what extent it can recover its ability to deliver adequate health services to the population. The model is based on actual regional primary care networks in Austria, where all patients and physicians are represented as anonymized avatars that are calibrated with nationwide data. After removal of a critical fraction of physicians, networks generically undergo a transition from resilient to nonresilient behavior, where it is impossible to maintain coverage for all patients. These “stress tests” allow us to quantify regional health care resilience and identify systemically risky health care providers.
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.
coevolving networks – dynamics of collapse – robustness – quality of care – patient-sharing network
Keywords: Public Health.