Social-Behavioral Modeling for Complex Systems
by Paul K. Davis, Angela O'Mahony, Jonathan Pfautz
35 A Complex Systems Approach for Understanding the Effect of Policy and Management Interventions on Health System Performance
Jason Thompson1, Rod McClure2 and Andrea de Silva3
1 Transport, Health and Urban Design Research Hub, Melbourne School of Design, University of Melbourne, Parkville, VIC, 3010, Australia
2 Faculty of Medicine and Health, School of Rural Health, University of New England, Armidale, NSW, 2351, Australia
3 Department of Epidemiology and Preventive Medicine, Alfred Hospital, Monash University, Clayton, VIC, 3800, Australia
Introduction
Traditionally, relationships in medical and health research are understood through the hypothetico‐deductive framework. First, hypotheses regarding observed or expected patterns between sets of people differentiated by group features are proposed. Hypotheses are then tested through comparison of features (e.g. gender, age, intervention, or treatment groups) on differences in outcome variables of interest (e.g. depression, well‐being, recovery, etc.). Over time, gathered evidence is then used to judge whether repeated observed associations among independent outcome variables are causal.
Hugely successful for understanding simple relationships, health researchers have relied heavily on this structure, of which the patient/problem/population–intervention–comparison–outcome (PICO) framework (U.S. National Library of Medicine 2018) is perhaps the most common example. The widespread adoption of PICO and related frameworks ...