Group Model Building Techniques for Rapid Elicitation of Parameter Values, Effect Sizes, and Data Sources

Hosseinichimeh, Niyousha; MacDonald, Rod; Hyder, Ayaz; Ebrahimvandi, Alireza; Porter, Lauren; Reno, Rebecca; Maurer, James K.; Andersen, Deborah Lines; Richardson, George P.; Hawley, Jana M.; Andersen, David F.

doi:10.1002/sdr.1575

Cited by 18 publications

(16 citation statements)

References 13 publications

(17 reference statements)

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“…At the core of the system there is a system dynamics model. System dynamics has been previously applied in various industry projects (application examples are reported in Rouwette and Ghaffarzadegan, ) and have proven to be an effective method for communication and data elicitation (Black and Andersen, ; Black, ; Hosseinichimeh et al, ) as well as modeling of innovation diffusion (Milling, , ). Our model is a framework for policy analysis and testing the effects of different alternatives on market penetration, costs, and revenue.…”

Section: Methodsmentioning

confidence: 99%

Dell's SupportAssist customer adoption model: enhancing the next generation of data‐intensive support services

Ghaffarzadegan

Rad

et al. 2017

System Dynamics Review

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We developed a decision support system to model, analyze, and improve market adoption of Dell's SupportAssist program. SupportAssist is a proactive and preventive support service capability that can monitor system operations data from all connected Dell devices around the world and predict impending failures in those devices. Performance of such data-intensive services is highly interconnected with market adoption: service performance depends on the richness of the customer database, which is influenced by customer adoption that in turn depends on customer satisfaction and service performance-a reinforcing feedback loop. We developed the SupportAssist adoption model (SAAM). SAAM utilizes various data sources and modeling techniques, particularly system dynamics, to analyze market response under different strategies. Dell anticipates improving market adoption of SupportAssist and revenue from support services, as results of using this analytical tool.

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Section: Methodsmentioning

confidence: 99%

Dell's SupportAssist customer adoption model: enhancing the next generation of data‐intensive support services

Ghaffarzadegan

Rad

et al. 2017

System Dynamics Review

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“…The full descriptions of the scripts used in the GMB session are provided in Hosseinichimeh et al . (, ). The follow‐up session was conducted to present the model results and get feedback from the participants.…”

Section: Stakeholder Involvement and Modelling Process Of The Ohio Inmentioning

confidence: 99%

“…The first GMB session was conducted to collaboratively develop the backbone structure of an SD model of infant mortality and identify interventions that could potentially reduce the state's IMR. The full descriptions of the scripts used in the GMB session are provided in Hosseinichimeh et al (2017aHosseinichimeh et al ( , 2017b. The followup session was conducted to present the model results and get feedback from the participants.…”

Section: Phase 1: Identifying Stakeholder Behaviormentioning

confidence: 99%

“…We were able to use panels of stakeholder experts to fill gaps in available data to get a formal model 'up and running'. Also, we used their guidance to help the modelling team quickly and accurately identify publicly available sources of relevant data useful in model calibration (Hosseinichimeh et al, 2017a).…”

Section: Diverse Stakeholders Can Be a Source Of Expert Judgement Formentioning

confidence: 99%

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Using a Stakeholder Analysis to Improve Systems Modelling of Health Issues: The Impact of Progesterone Therapy on Infant Mortality in Ohio

et al. 2018

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Systems modelling of health issues enriches analysis of health care policies by exploring interdependencies among system components. Considering a wide range of stakeholder behavior is a requisite for developing a useful model. Our paper shows how to systematically identify stakeholder behavior, formulate them in a model and test their impact on health outcomes. Stakeholder behavior are explained in the context of a simulation‐based analysis of infant mortality in Ohio. Using group model building techniques, we identify three levels of stakeholder behavior—individual, organizational and policy behaviors—and discuss how these behaviors are reflected in the model formulation and drive the key health outcomes. Specifically, we show how individual and policy behaviors are formulated to examine their influence on the impact of progesterone therapy—one of the interventions identified in the group model building session—on infant mortality. We conclude with recommendations for incorporating diverse stakeholder perspectives in systems modelling of health issues. © 2018 John Wiley & Sons, Ltd.

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“…Many studies have focused on one state or a limited number of modifiable risk factors across states to investigate determinants of preterm births. [5][6][7][8][9][10][11][12][13] Understanding drivers of IMR reduction in states that have achieved substantial rate reductions may help improve high IMRs. In this study, we explore state variations of the IMR and categorize them as either successful-with an IMR reduction above the national average-or unsuccessful.…”

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confidence: 99%

Understanding State-Level Variations in the US Infant Mortality: 2000 to 2015

2018

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Objective To exploit state variations in infant mortality, identify diagnoses that contributed to reduction of the infant mortality rate (IMR), and examine factors associated with preterm-related mortality rate (PMR). Study Design Using linked birth-infant deaths files, we examined patterns in the leading causes of IMR. We compared these rates at both national and state levels to find reduction trends. Creating a cross-sectional time series of states' PMR and some explanatory variables, we implemented a fixed-effect regression model to examine factors associated with PMR at the state level. Results We found substantial state-level variations in changes of the IMR (range = − 2.87–2.08) and PMR (−1.77–0.67). Twenty-one states in which the IMR declined more than the national average of 0.99 (6.89–5.90) were labeled as successful. In the successful states, we found reduction in the PMR accounted for the largest decline in the IMR—0.90 fewer deaths. Changes in the other subgroups of leading causes did not differ significantly in successful and unsuccessful states. Conclusion Trends in the causes of mortality are heterogeneous across states. Although its impact is not large, reducing the percentage of pregnant women with inadequate care is one of the mechanisms through which the PMR decrease.

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Group Model Building Techniques for Rapid Elicitation of Parameter Values, Effect Sizes, and Data Sources

Cited by 18 publications

References 13 publications

Dell's SupportAssist customer adoption model: enhancing the next generation of data‐intensive support services

Dell's SupportAssist customer adoption model: enhancing the next generation of data‐intensive support services

Using a Stakeholder Analysis to Improve Systems Modelling of Health Issues: The Impact of Progesterone Therapy on Infant Mortality in Ohio

Understanding State-Level Variations in the US Infant Mortality: 2000 to 2015

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