Table 1. Selected papers.
| Author | Title | IC topic-of-interest | Model | Aspects of complexity | Optimization | Time frame |
|---|---|---|---|---|---|---|
| Alonge et al. 2017 [37] | Improving health systems performance in low- and middle-income countries: a system dynamics model of the pay-for-performance initiative in Afghanistan. | Pay for performance incentive scheme | SD | ° Dynamism ° Soft variables ° Interaction |
‘What if’; ‘How to’ scenarios | 5 to 8 years |
| Ansah et al. 2016 [38] | Projecting the effects of long-term care policy on the labor market participation of primary informal family caregivers of elderly with disability: insights from a dynamic simulation model. | Evaluating care management and interventions of chronic conditions—Performance evaluation of community health | SD | ° Dynamism ° Influence of historical occurrence ° Interaction |
‘What if’ scenarios | 17 years |
| Comans et al. 2017 [39] | The development and practical application of a simulation model to inform musculoskeletal service delivery in an Australian public health service | Health facility operations simulation—Planning Health force | DES | ° Individualization ° Influence of historical occurrence ° Interference ° Interaction |
‘What if’ scenarios | 5 years |
| Cooper et al. 2008 [40] | Use of a coronary heart disease simulation model to evaluate the costs and effectiveness of drugs for the prevention of heart disease | Evaluating care management and interventions of chronic conditions. | DES | ° Individualization ° Influence of historical occurrence ° Interference ° Simultaneity of events ° Interaction ° Dynamism |
‘What if’ scenarios | 20 years |
| de Andrade et al. 2014 [41] | System Dynamics Modeling in the Evaluation of Delays of Care in ST-Segment Elevation Myocardial Infarction Patients within a Tiered Health System. | Evaluating care management and interventions of chronic conditions. | SD | ° Dynamism ° Interaction ° Influence of historical occurrence |
‘What if’ scenarios | One care case: ~4hr |
| Fialho et al. 2011 [42] | Using discrete event simulation to compare the performance of family health unit and primary health care center organizational models in Portugal. | Performance evaluation of community health | DES | ° Individualization ° Influence of historical occurrence ° Interference ° Interaction ° Dynamism |
‘What if’; ‘How to’ scenarios | 1 week (1/52 year) |
| Gao et al. 2013 [43] | Tripartite hybrid model architecture for investigating health and cost impacts and intervention tradeoffs for diabetic end-stage renal disease | Evaluating care management and interventions of chronic conditions. | Hybrid | ° Dynamism ° Soft Variables ° Intelligent Adaptation ° Simultaneity of events ° Influence of historical occurrences ° Interaction ° Individualization |
‘What if’ scenarios | 1 year |
| Getsios et al. 2013 [44] | Smoking cessation treatment and outcomes patterns simulation: a new framework for evaluating the potential health and economic impact of smoking cessation interventions. | Tobacco harm policies. Market Control and Interventions | DES | ° Individualization ° Influence of historical occurrence ° Interaction ° Dynamism |
‘What if’ scenarios | Lifetime (since start smoking) |
| Goldman et al. 2004 [45] | Projecting long-term impact of modest sodium reduction in Los Angeles County | Evaluation of Public health intervention | Micro | ° Individualization ° Influence of historical occurrence ° Interaction |
‘What if’; ‘How to’ scenarios | 45 years* |
| Günal et al. 2011 [46] | DGHPSIM: Generic Simulation of Hospital Performance | Health facility operations simulation | DES | ° Individualization ° Interference ° Interaction ° Dynamism ° Influence of historical occurrence |
‘What if’; ‘How to’ scenarios | 2 years |
| Hill et al. 2017 [47] | A system dynamic modeling approach to assess the impact of launching a new nicotine product on population health outcomes. | Tobacco harm policies. Market Control and Interventions | SD | ° Dynamism ° Soft variables ° Interaction |
‘What if’ scenarios | 50 years |
| Homer et al. 2010 [48] | Simulating and Evaluating Local Interventions to Improve Cardiovascular Health | Evaluating care management and interventions of chronic conditions. | SD | ° Dynamism ° Soft variables ° Interaction |
‘What if’ scenarios | 50 years |
| Jones et al. 2006 [49] | Understanding diabetes population dynamics through simulation modeling and experimentation. | Diabetes Population Dynamics | SD | ° Dynamism ° Soft variables ° Interaction |
‘What if’ scenarios | 46 years |
| Kalton et al. 2016 [50] | Multi-Agent-Based Simulation of a Complex Ecosystem of Mental Health Care. | Health facility operations simulation | ABM | ° Individualization ° Simultaneity of events ° Influence of historical occurrence ° Interaction ° Emergence ° Dynamism |
‘What if’; ‘How to’ scenarios | 3 years |
| Kang et al. 2018 [51] | A system dynamic approach to planning and evaluating interventions for chronic disease management | Evaluating care management and interventions of chronic conditions. | SD | ° Dynamism ° Influence of historic occurrences ° Interaction ° Soft variables |
‘What if’; ‘How to’ scenarios | 10 years |
| Kotiadis 2006 [52] | Extracting a conceptual model for a complex integrated system in health care | Health facility operations simulation | DES | ° Individualization ° Interaction ° Interference |
‘What if’; ‘How to’ scenarios | 5 months |
| Laurence et al. 2016 [53] | Improving the planning of the GP workforce in Australia: a simulation model incorporating work transitions, health needs, and service usage. | Planning Health force | Markov | ° Interaction | ‘What if’; ‘How to’ scenarios | 10 years |
| Lay-Yee et al. 2015 [54] | Determinants and disparities: a simulation approach to the case of child health care. | Performance evaluation of community health | Micro | ° Individualization ° Influence of historical occurrence ° Dynamism ° Interaction |
‘What if’ scenarios | 10 years |
| Lebcir et al. 2017 [55] | A discrete event simulation model to evaluate the use of community services in the treatment of patients with Parkinson’s disease in the United Kingdom. | Performance evaluation of community health | DES | ° Individualization ° Influence of historical occurrence ° Interference ° Interaction ° Dynamism ° Simultaneity of events |
‘What if’; ‘How to’ scenarios | 3 years |
| Levy et al. 2016 [56] | Estimating the Potential Impact of Tobacco Control Policies on Adverse Maternal and Child Health Outcomes in the United States Using the SimSmoke Tobacco Control Policy Simulation Model. | Tobacco harm policies. Market Control and Interventions | Markov | ° Interaction ° Influence of historical events |
‘What if’ scenarios | 50 years |
| Loyo et al. 2013 [57] | From model to action: using a system dynamics model of chronic disease risks to align community action. | Evaluating care management and interventions of chronic conditions. | SD | ° Dynamism ° Soft variables ° Interaction |
‘What if’ scenarios | 30 years |
| Matta et al. 2007 [58] | Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center | Performance measures evaluation | DES | ° Individualization ° Interference ° Interaction ° Dynamism |
‘What if’; ‘How to’ scenarios | 1 working day |
| Milstein et al. 2010 [59] | Analyzing national health reform strategies with a dynamic simulation model. | National Health Reform Evaluation | SD | ° Dynamism ° Soft variables ° Interaction |
‘What if’ scenarios | 25 years |
| Nianogo et al. 2018 [60] | Impact of Public Health Interventions on Obesity and Type 2 Diabetes Prevention: A Simulation Study. | Evaluation of Public health intervention | ABM | ° Individualization ° Simultaneity of events ° Influence of historical occurrence ° Interaction ° Emergence ° Intelligent Adaptation |
‘What if’ scenarios | Adult life |
| Norouzzadeh et al. 2015 [61] | Simulation Modeling to Optimize Health Care Delivery in an Outpatient Clinic | Health facility operations simulation | DES | ° Individualization ° Interference ° Interaction ° Dynamism |
‘What if’; ‘How to’ scenarios | 2 years |
| Oh et al. 2016 [62] | Use of a simulation-based decision support tool to improve emergency department throughput | Health facility operations simulation | DES | ° Individualization ° Interference ° Interaction ° Dynamism |
‘What if’; ‘How to’ scenarios | 2.5 years |
| Rashwan et al. 2015 [63] | Modeling behavior of nurses in a clinical medical unit in a university hospital: Burnout implications | Planning Health force | SD | ° Dynamism ° Soft variables ° Interaction |
‘What if’; ‘How to’ scenarios | 1 working day |
| Rejeb et al. 2018 [64] | Performance and cost evaluation of health information systems using micro-costing and discrete-event simulation. | Evaluation of Information System | DES | ° Individualization ° Influence of historical occurrence ° Interference ° Interaction ° Dynamism ° Simultaneity of events |
‘What if’; ‘How to’ scenarios | 1 to 5 years |
| Sugiyama et al. 2017 [65] | Construction of a simulation model and evaluation of the effect of potential interventions on the incidence of diabetes and initiation of dialysis due to diabetic nephropathy in Japan. | Evaluating care management and interventions of chronic conditions. | SD | ° Dynamism ° Influence of historic occurrences ° Interaction |
‘What if’ scenarios | 35 years |
| Vataire et al. 2014 [66] | Core discrete event simulation model for the evaluation of health care technologies in major depressive disorder. | Evaluating care management and interventions of chronic conditions. | DES | ° Individualization ° Influence of historical occurrence ° Interaction |
‘What if’ scenarios | 1 to 5 years |
* The model has been used in several projects and the time provided corresponds the one used most recently by Vidyanti et al. 2015 [67]