VTechWorks Repository :: Browsing by Author "Hosseinichimeh, Niyousha"

Browsing by Author "Hosseinichimeh, Niyousha"

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Advancing Emergency Department Efficiency, Infectious Disease Management at Mass Gatherings, and Self-Efficacy Through Data Science and Dynamic Modeling
Ba-Aoum, Mohammed Hassan (Virginia Tech, 2024-04-09)
This dissertation employs management systems engineering principles, data science, and industrial systems engineering techniques to address pressing challenges in emergency department (ED) efficiency, infectious disease management at mass gatherings, and student self-efficacy. It is structured into three essays, each contributing to a distinct domain of research, and utilizes industrial and systems engineering approaches to provide data-driven insights and recommend solutions. The first essay used data analytics and regression analysis to understand how patient length of stay (LOS) in EDs could be influenced by multi-level variables integrating patient, service, and organizational factors. The findings suggested that specific demographic variables, the complexity of service provided, and staff-related variables significantly impacted LOS, offering guidance for operational improvements and better resource allocation. The second essay utilized system dynamics simulations to develop a modified SEIR model for modeling infectious diseases during mass gatherings and assessing the effectiveness of commonly implemented policies. The results demonstrated the significant collective impact of interventions such as visitor limits, vaccination mandates, and mask wearing, emphasizing their role in preventing health crises. The third essay applied machine learning methods to predict student self-efficacy in Muslim societies, revealing the importance of socio-emotional traits, cognitive abilities, and regulatory competencies. It provided a basis for identifying students with varying levels of self-efficacy and developing tailored strategies to enhance their academic and personal success. Collectively, these essays underscore the value of data-driven and evidence-based decision- making. The dissertation's broader impact lies in its contribution to optimizing healthcare operations, informing public health policy, and shaping educational strategies to be more culturally sensitive and psychologically informed. It provides a roadmap for future research and practical applications across the healthcare, public health, and education sectors, fostering advancements that could significantly benefit society.
Agency to Change: A Narrative Inquiry of White Men Faculty in Engineering Engaged in Broadening Participation Work
Hampton, Cynthia (Virginia Tech, 2021-01-29)
Transformational change for Broadening Participation in Engineering (BPE) of racial, ethnic, and gender groups has not occurred, despite continuing efforts for over four decades. BPE can be represented through particular activities to increase underrepresented students' participation at the undergraduate and graduate levels (herein referred to as BPE Work). One approach to investigating the complexity of change through BPE is through the analysis of a sub-group of faculty who engage in BPE Work within the system of engineering education. In the case of BPE, investigation of faculty engagement is limited. Further, limited exploration of the majority group's experiences (i.e., white men) exists concerning their agency and this type of work. This study investigates the experiences of engineering faculty who identify as white men and have been engaged in BPE Work using faculty agency and narrative. These narratives reveal insights into the current system that may drive, sustain, or prohibit BPE change. Using the narrative experiences of eight engineering faculty involved in BPE Work who identify as white men, this research explores the following questions: (1) What activities do white men faculty describe in their personal narratives of engaging in BPE Work; (2) How do white men faculty describe their trajectory into and through engaging in BPE Work; (3) What factors influence the actions and perspectives of white men faculty engaged in BPE Work; and (4) How do white men faculty describe the outcomes to their professional and personal lives when using their agency for BPE Work? Application of data analysis to research questions to elicit findings found in chapter 4 consisted of an accountability cycle, BPE Work activities, factors that impact (constraining or enabling) BPE Work, and outcomes to the participants' lives from engagement in BPE Work. The participants of this study shared experiences in which they expressed perspectives on BPE, reflecting on their backgrounds. Archer (2003) describes the ability to take a stance regarding society as invoking an "active agent," but that this stance is not a one-and-done situation (p. 343). This study resulted in findings for Deans and Provosts on the vital need for a normalized climate for BPE Work, the hidden essential functions of Engineering Student Support Centers, value-focused needs for tenure/promotion/merit processes for BPE Work, the trajectory of faculty development in BPE Work, the experiences that permeate into faculty life in undergraduate student development, and the need for future work in interrogating power dynamics in engineering education The need for all faculty to be involved in change alludes to a necessary understanding. The number of faculty of color and women faculty is not robust enough or supported to carry the system's burden. A need is present to take a realistic look at how white men experience BPE Work. This look is vital for policy and the identification of system constraints that need to be evaluated and used to drive BPE forward.
Application of Systems Engineering Analysis Methods to Examine Engineering Transfer Student Persistence
Smith, Natasha Leigh (Virginia Tech, 2020-01-20)
The demand for engineering graduates in the United States continues to grow, yet the number of students entering post-secondary education is declining, and graduation rates have seen little to no change over the last several decades. Engineering transfer students are a growing population and can help meet the nation's needs, however, there is little research on the persistence of this population after they transfer to the receiving institution. Student persistence is dependent on a complex set of interactions over time. Management systems engineering provides a framework for working with complex systems through system analysis and design, with a focus on the interactions of the system components. This research includes multiple management systems engineering analysis methods used to define and develop a systems view of engineering transfer student persistence. This work includes a comprehensive literature review to identify factors affecting engineering transfer student persistence, an empirical analysis of an institutional dataset, and development of a simulation model to demonstrate the throughput of engineering transfer student. Findings include 34 factors identified in the literature as affecting engineering student persistence. A review of the literature also highlighted two important gaps in the literature, including a focus on post-transfer success almost exclusively in the first post-transfer year and a significant interest in vertical transfer students, with little consideration given to lateral transfer students. The empirical analysis addressed the gaps found in the literature. Vertical and lateral engineering transfer students were found to experience different levels of transfer shock which also impacts their 4-year graduation rates. The analysis also found transfer shock was not unique to the first post-transfer term, it was also present in the second and third post-transfer terms, and reframed as transfer adjustment. The simulation model uncovers leaving patterns of engineering transfer students which include the students leaving engineering in the second year, as well as those graduating with an engineering degree in the third year. Overall this research identifies explicit factors that affect engineering transfer student persistence and suggests a new systems engineering approach for understanding student persistence and how institutions can affect change.
Comparing Self-Report Assessments and Scenario-Based Assessments of Systems Thinking Competence
Davis, Kirsten A.; Grote, Dustin; Mahmoudi, Hesam; Perry, Logan; Ghaffarzadegan, Navid; Grohs, Jacob; Hosseinichimeh, Niyousha; Knight, David B.; Triantis, Konstantinos (Springer, 2023-03)
Self-report assessments are used frequently in higher education to assess a variety of constructs, including attitudes, opinions, knowledge, and competence. Systems thinking is an example of one competence often measured using self-report assessments where individuals answer several questions about their perceptions of their own skills, habits, or daily decisions. In this study, we define systems thinking as the ability to see the world as a complex interconnected system where different parts can influence each other, and the interrelationships determine system outcomes. An alternative, less-common, assessment approach is to measure skills directly by providing a scenario about an unstructured problem and evaluating respondents' judgment or analysis of the scenario (scenario-based assessment). This study explored the relationships between engineering students' performance on self-report assessments and scenario-based assessments of systems thinking, finding that there were no significant relationships between the two assessment techniques. These results suggest that there may be limitations to using self-report assessments as a method to assess systems thinking and other competencies in educational research and evaluation, which could be addressed by incorporating alternative formats for assessing competence. Future work should explore these findings further and support the development of alternative assessment approaches.
Empirical Investigation of Lean Management and Lean Six Sigma Success in Local Government Organizations
Al rezq, Mohammed Shjea (Virginia Tech, 2024-05-29)
Lean Management and Lean Six Sigma (LM/LSS) are improvement methodologies that have been utilized to achieve better performance outcomes at organizational and operational levels. Although there has been evidence of breakthrough improvement across diverse organizational settings, LM/LSS remains an early-stage improvement methodology in public sector organizations, specifically within local government organizations (LGOs). Some LGOs have benefited from LM/LSS and reported significant improvements, such as reducing process time by up to 90% and increasing financial savings by up to 57%. While the success of LM/LSS can lead to satisfactory outcomes, the risk of failure can also result in a tremendous waste of financial and non-financial resources. Evidence from the literature indicates that the failure to achieve the expected outcomes is likely due to the lack of attention paid to critical success factors (CSFs) that are crucial for LM/LSS success. Furthermore, research in this research area regarding characterizing and statistically examining the CSFs associated with LM/LSS in such organizational settings has been limited. Hence, the aim of this research is to provide a comprehensive investigation of the success factors for LM/LSS in LGOs. The initial stage of this dissertation involved analyzing the scientific literature to identify and characterize the CSFs associated with LM/LSS in LGOs through a systematic literature review (SLR). This effort identified a total of 47 unique factors, which were grouped into 5 categories, including organization, process, workforce knowledge, communications, task design, and team design. The next stage of this investigation focused on identifying a more focused set of CSFs. This involved evaluating the strength of the effect (or importance) of the factors using two integrated approaches: meta-synthesis and expert assessment. This process concluded with a total of 29 factors being selected for the empirical field study. The final stage included designing and implementing an online survey questionnaire to solicit LGOs' experience on the presence of factors during the development and/or implementation of LM/LSS and their impact on social-technical system outcomes. Once the survey was concluded, an exploratory factor analysis (EFA) was conducted to identify the underlying latent variables, followed by using a partial least square-structural equation model (PLS-SEM) to determine the significance of the factors on outcomes. The EFA identified three endogenous and five exogenous latent variables. The results of the PLS-SEM model identified four significant positive relationships. Based on the results from the structural paths, the antecedent Improvement Readiness (IR) and Change Awareness (CA) were significant and had a positive influence on Transformation Success (TS). For the outcome Deployment Success (DS), Sustainable Improvement Infrastructure (SII) was the only significant exogenous variable and had the highest positive impact among all significant predictor constructs. Furthermore, Measurement-Based Improvement (MBI) was significant and positively influenced Improvement Project Success (IPS). Findings from this dissertation could serve as a foundation for researchers looking to further advance the maturity of this research area based on the evidence presented in this work. Additionally, this work could be used as guidelines for practitioners in developing implementation processes by considering the essential factors to maximize the success of LM/LSS implementation. Given the diversity of functional areas and processes within LGO contexts, it is also possible that other public sector organizations could benefit from these findings.
An Empirical Investigation on the Critical Success Factors for Kaizen Events in Hospitals
Harry, Kimberly D.M. (Virginia Tech, 2023-09-06)
A Kaizen event (KE) may be defined as a structured improvement project that uses a cross-functional team and specific improvement goals to improve a targeted work area or process in an accelerated time frame. KEs, also known as Rapid Improvement Events (RIEs), have been utilized within hospitals to achieve beneficial operations, stakeholder (i.e., social), financial, and clinical outcomes. Due to their potential to achieve positive results in a rapid timeframe, understanding the determinants of KE success within a hospital environment is a valuable research undertaking. To date there has been limited rigorous empirical quantitative research focused on identifying success factors (SFs) influencing socio-technical outcomes of hospital-based KEs. Hence, this empirical research study seeks to determine the critical success factors (CSFs) for KEs in hospitals. For the first phase of this research work, a comprehensive systematic literature review (SLR) was conducted to identify the success factors (SFs) for KEs in hospitals as reported in the literature. This SLR resulted in the identification of 54 unique success factors mapping to four broad success factor categories, KE Task Design, KE Team Design, Organization, and KE Process. Thereafter, the second phase, which involved the variable reduction process, was performed to determine the strength of effect, or importance, of the SFs in order to determine a feasible number of SFs to include in further empirical work. Two robust methods were applied; a Meta-synthesis Evaluation and an Expert Survey, to query the SFs and to determine high priority factors for the empirical study. As a result, a total of 30 factors were finalized for empirical study. Next, the last phase, the empirical study to investigate and determine the CSFs for KEs in hospitals, was executed using a retrospective field study survey research design. Specifically, a survey questionnaire was designed to elicit feedback on perceptual measures from targeted hospital KE facilitators/leaders on the criticality of SFs on socio-technical outcomes for KEs in hospitals. Sixty usable responses were obtained, which were subjected to Exploratory Factor Analysis (EFA) and Partial Least Squares-Structural Equation Modeling (PLS-SEM), which were used to identify latent factor constructs and to determine the significance of the SFs, respectively. The results of this study identified seven significant direct relationships. Kaizen Event Design Characteristics (KEDC) and Target Area Buy-in (TABI) were found to have significant direct effects with both dependent variables, Performance Impact (PI) and Growth in Kaizen Capabilities (KCG). In addition, KEDC also had a significant direct relationship with Performance Culture (PC) and Team Dynamics (TD), respectively. Also, PC has a significant direct relationship with TD. Furthermore, Logistic Regression was utilized to test the SFs impact on the one objective technical outcome measure in the study, Goal Attainment (GOALATT). This analysis revealed one significant negative relationship occurring between TD and GOALATT. Overall, the study's findings provide evidence-based results for informing hospital managers, leaders, and continuous improvement practitioners on the key factors or value-added practices that can be adopted in their hospital KE initiatives to achieve beneficial socio-technical outcomes, as well as overall hospital KE success. Furthermore, this research can enable academia/researchers to strategize more confirmatory analysis approaches for theory validation and generalizability.
Essays on Mathematical Modeling and Empirical Investigations of Organizational Learning in Cancer Research
Mahmoudi, Hesam (Virginia Tech, 2023-09-01)
After numerous renewals and reignitions since the initiation of the "War on Cancer" more than five decades ago, the recent reignition of "Moonshot to Cure Cancer" points to the systemic persistence of cancer as a major cause of loss of life and livelihood. Literature points to the diminishing returns of cancer research through time, as well as heterogeneities in cancer research centers' innovation strategies. This dissertation focuses on the strategic decision by cancer research centers to invest their resources in conducting early phases of clinical trials on new candidate drugs/treatments (resembling exploration) or late phases of clinical trials that push established candidates towards acquiring FDA approvals (resembling exploitation). The extensive clinical trials data suggests that cancer research centers are not only different in their emphasis on exploratory trials, but also in how their emphasis is changing over time. This research studies the dynamics of this heterogeneity in cancer research centers' innovation strategies, how experiential learning and capability development interact to cause dynamics of divergence among learning agents, and how the heterogeneity among cancer research centers' innovation strategies is affected by the dynamics of learning from experience and capability development. The findings of this dissertation shows that endogenous heterogeneities can arise from the process of learning from experience and accumulation of capabilities. It is also shown that depending on the sensitivity of the outcome of decisions to the accumulated capabilities, such endogenous heterogeneities can be value-creating and thus, justified. Empirical analysis of cancer clinical trials data shows that cancer research centers learn from success and failure of their previous trials to adopt more/less explorative tendencies. It also demonstrates that cancer research centers with a history of preferring exploratory or FDA trials have the tendency to increase their preference and become more specialized in one specific type (endogenous specialization). These behavioral aspects of the cancer research centers' innovation strategies provide some of the tools necessary to model the behavior of the cancer research efforts from a holistic viewpoint.
Exploring personalized psychotherapy for depression: A system dynamics approach
Wittenborn, Andrea K.; Hosseinichimeh, Niyousha (Public Library of Science, 2022-10)
Depressive disorders are the leading contributor to medical disability, yet only 22% of depressed patients receive adequate treatment in a given year. Response to treatment varies widely among individuals with depression, and poor response to one treatment does not signal poor response to others. In fact, half of patients who do not recover from a first-line psychotherapy will recover from a second option. Attempts to personalize psychotherapy to patient characteristics have produced better outcomes than usual care, but research on personalized psychotherapy is still in its infancy. The present study explores a new method for personalizing psychotherapy for depression through simulation modeling. In this study, we developed a system dynamics simulation model of depression based on one of the major mechanisms of depression in the literature and investigated the trend of depressive symptoms under different conditions and treatments. Our simulation outputs show the importance of individualized services with appropriate timing, and reveal a new method for personalizing psychotherapy to heterogeneous individuals. Future research is needed to expand the model to include additional mechanisms of depression.
Generative Agent-Based Modeling: Unveiling Social System Dynamics through Coupling Mechanistic Models with Generative Artificial Intelligence
Ghaffarzadegan, Navid; Majumdar, Aritra; Williams, Ross; Hosseinichimeh, Niyousha (2024-01)
We discuss the emerging new opportunity for building feedback-rich computational models of social systems using generative artificial intelligence. Referred to as Generative Agent-Based Models (GABMs), such individual-level models utilize large language models to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful dynamic models of various social systems that include realistic human reasoning and decision-making.
Group Model Building Techniques for Rapid Elicitation of Parameter Values and Effect-Size-Driven Formulations
Hosseinichimeh, Niyousha; MacDonald, Rod; Hyder, Ayaz; Ebrahimvandi, Alireza; Porter, Lauren; Reno, Becky; Maurer, Julie A.; Andersen, Deborah; Richardson, George; Hawley, Josh; Andersen, David F. (2017-02)
Three parts to this presentation: Group Model Building and the Ohio Infant Mortality Project More Details on Rapid Elicitation of Parameters and Size Effects Advance Look at the Resultant Model as of Feb. 2017
Identifying the Early Signs of Preterm Birth from U.S. Birth Records Using Machine Learning Techniques
Ebrahimvandi, Alireza; Hosseinichimeh, Niyousha; Kong, Zhenyu James (MDPI, 2022-06-25)
Preterm birth (PTB) is the leading cause of infant mortality in the U.S. and globally. The goal of this study is to increase understanding of PTB risk factors that are present early in pregnancy by leveraging statistical and machine learning (ML) techniques on big data. The 2016 U.S. birth records were obtained and combined with two other area-level datasets, the Area Health Resources File and the County Health Ranking. Then, we applied logistic regression with elastic net regularization, random forest, and gradient boosting machines to study a cohort of 3.6 million singleton deliveries to identify generalizable PTB risk factors. The response variable is preterm birth, which includes spontaneous and indicated PTB, and we performed a binary classification. Our results show that the most important predictors of preterm birth are gestational and chronic hypertension, interval since last live birth, and history of a previous preterm birth, which explains 10.92, 5.98, and 5.63% of the predictive power, respectively. Parents’ education is one of the influential variables in predicting PTB, explaining 7.89% of the predictive power. The relative importance of race declines when parents are more educated or have received adequate prenatal care. The gradient boosting machines outperformed with an AUC of 0.75 (sensitivity: 0.64, specificity: 0.73) for the validation dataset. In this study, we compare our results with seminal and most related studies to demonstrate the superiority of our results. The application of ML techniques improved the performance measures in the prediction of preterm birth. The results emphasize the importance of socioeconomic factors such as parental education as one of the most important indicators of preterm birth. More research is needed on these mechanisms through which socioeconomic factors affect biological responses.
Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients
Darabi, Negar; Hosseinichimeh, Niyousha; Noto, Anthony; Zand, Ramin; Abedi, Vida (Frontiers, 2021-03-31)
Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting–XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model’s AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64–0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems’ resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.
Methods and Results from Parameter Estimation Exercises Used in 2-Day Group Modeling Session for Ohio Infant Mortality Study
Hosseinichimeh, Niyousha; MacDonald, Rod; Hyder, Ayaz; Ebrahimvandi, Alireza; Porter, Lauren; Reno, Becky; Maurer, Julie A.; Andersen, Deborah; Richardson, George; Hawley, Josh; Andersen, David F. (2017-10)
Much of the existing group model-building literature focuses on approaches to defining model boundary and conceptualizing overall model structure including the creation of a backbone stock-and-flow structure as well as mapping key feedback structures into the model. Detailed formulation and parameter estimates are often left to back room techniques. This paper reports on the use of three scripts designed to engage groups in tasks related to model parameterization. The paper describes the context of the study—a group model building session hosted by the Ohio State University and focusing on infant mortality in the state of Ohio—and then proceeds to lay out the agenda for the two-day group modeling project. We discuss in detail how three scripts were used and also present a summary of the data that was collected to parameterize the final model. The paper sketches how that data has been used to support the rapid prototyping of the first phase of a running simulation model.
Model-based Analysis of Diversity in Higher Education
Andalib, Maryam Alsadat (Virginia Tech, 2018-07-03)
U.S. higher education is an example of a large multi-organizational system within the service sector. Its performance regarding workforce development can be analyzed through the lens of industrial and systems engineering. In this three-essay dissertation, we seek the answer to the following question: How can the U.S. higher education system achieve an equal representation of female and minority members in its student and faculty populations? In essay 1, we model the education pipeline with a focus on the system's gender composition from k-12 to graduate school. We use a system dynamics approach to present a systems view of the mechanisms that affect the dynamics of higher education, replicate historical enrollment data, and forecast future trends of higher education's gender composition. Our results indicate that, in the next two decades, women will be the majority of advanced degree holders. In essay 2, we look at the support mechanisms for new-parent, tenure-track faculty in universities with a specific focus on tenure-clock extension policies. We construct a unique data set to answer questions around the effectiveness of removing the stigma connected with automatic tenure-clock policies. Our results show that such policies are successful in removing the stigma and that, overall, faculty members that have newborns and are employed by universities that adopt auto-TCE policies stay one year longer in their positions than other faculty members. In addition, although faculty employed at universities that adopt such policies are generally more satisfied with their jobs, there is no statistically significant effect of auto TCE policies on the chances of obtaining tenure. In essay 3, we focus on the effectiveness of training underrepresented minorities (e.g., African Americans and Hispanics) in U.S. higher education institutions using a Data Envelopment Analysis approach. Our results indicate that graduation rates, average GPAs, and post-graduate salaries of minority students are higher in selective universities and those located in more diverse towns/cities. Furthermore, the graduation rate of minority students in private universities and those with affirmative action programs is higher than in other institutions. Overall, this dissertation provides new insights into improving diversity within the science workforce at different organizational levels by using industrial and systems engineering and management sciences methods.
Modeling and estimating the feedback mechanisms among depression, rumination, and stressors in adolescents
Hosseinichimeh, Niyousha; Wittenborn, Andrea K.; Rick, Jennifer; Jalali, Mohammad S.; Rahmandad, Hazhir (PLOS, 2018-09-27)
The systemic interactions among depressive symptoms, rumination, and stress are important to understanding depression but have not yet been quantified. In this article, we present a system dynamics simulation model of depression that captures the reciprocal relationships among stressors, rumination, and depression. Building on the response styles theory, this model formalizes three interdependent mechanisms: 1) Rumination contributes to `keeping stressors alive'; 2) Rumination has a direct impact on depressive symptoms; and 3) Both `stressors kept alive' and current depressive symptoms contribute to rumination. The strength of these mechanisms is estimated using data from 661 adolescents (353 girls and 308 boys) from two middle schools (grades 6–8). These estimates indicate that rumination contributes to depression by keeping stressors `alive' — and the individual activated — even after the stressor has ended. This mechanism is stronger among girls than boys, increasing their vulnerability to a rumination reinforcing loop. Different profiles of depression emerge over time depending on initial levels of depressive symptoms, rumination, and stressors as well as the occurrence rate for stressors; levels of rumination and occurrence of stressors are stronger contributors to long-term depression. Our systems model is a steppingstone towards a more comprehensive understanding of depression in which reinforcing feedback mechanisms play a significant role. Future research is needed to expand this simulation model to incorporate other drivers of depression and provide a more holistic tool for studying depression.
Modeling of drinking and driving behaviors among adolescents and young adults in the United States: Complexities and Intervention outcomes
Hosseinichimeh, Niyousha; MacDonald, Rod; Li, Kaigang; Fell, James C.; Haynie, Denise L.; Simons-Morton, Bruce; Banz, Barbara C.; Camenga, Deepa R.; Iannotti, Ronald J.; Curry, Leslie A.; Dziura, James; Andersen, David F.; Vaca, Federico E. (Elsevier, 2024-07-22)
Alcohol-impaired driving is a formidable public health problem in the United States, claiming the lives of 37 individuals daily in alcohol-related crashes. Alcohol-impaired driving is affected by a multitude of interconnected factors, coupled with long delays between stakeholders’ actions and their impacts, which not only complicate policy-making but also increase the likelihood of unintended consequences. We developed a system dynamics simulation model of drinking and driving behaviors among adolescents and young adults. This was achieved through group model building sessions with a team of multidisciplinary subject matter experts, and a focused literature review. The model was calibrated with data series from multiple sources and replicated the historical trends for male and female individuals aged 15 to 24 from 1982 to 2020. We simulated the model under different scenarios to examine the impact of a wide range of interventions on alcohol-related crash fatalities. We found that interventions vary in terms of their effectiveness in reducing alcohol-related crash fatalities. In addition, although some interventions reduce alcohol-related crash fatalities, some may increase the number of drinkers who drive after drinking. Based on insights from simulation experiments, we combined three interventions and found that the combined strategy may reduce alcohol-related crash fatalities significantly without increasing the number of alcohol-impaired drivers on US roads. Nevertheless, related fatalities plateau over time despite the combined interventions, underscoring the need for new interventions for a sustained decline in alcohol-related crash deaths beyond a few decades. Finally, through model calibration we estimated time delays between actions and their consequences in the system which provide insights for policymakers and activists when designing strategies to reduce alcohol-related crash fatalities.
A Multi-Level Analysis of Major Health Challenges in the United States Using Data Analytics Approaches
Darabi, Negar (Virginia Tech, 2020-09-04)
The U.S. healthcare system is facing many public health challenges that affect population health, societal well-being, and quality of healthcare. Infant mortality, opioid overdose death, and hospital readmission after stroke are some of these important public health concerns that can impact the effectiveness and outcomes of the healthcare system. We analyze these problems through the industrial engineering and data analytics lens. The major goal of this dissertation is to enhance understanding of these three challenges and related interventions using different levels of analysis to improve the health outcomes. To attain this objective, I introduced three stand-alone papers to answer the related research questions. In essay 1, we focused on the performance of the state's healthcare systems in reducing unfavorable birth outcomes such as infant mortality, preterm birth, and low birthweight using Data Envelopment Approach. We constructed a unique state-level dataset to answer this main research question: what does make a healthcare system more successful in improving the birth outcomes? Our results indicated that socioeconomic and demographic factors may facilitate or obstruct health systems in improving their outcomes. We realized that states with a lower rate of poverty and African-American women were more successful in effectively reduce unfavorable birth outcomes. In the second essay, we looked into the trends of the opioid overdose mortalities in each state from 2008 to 2017. We investigated the effect of four state laws and programs that have been established to curb the epidemic (i.e., dose and duration limitations on the initial prescription, pain management clinic laws, mandated use of prescription drug monitoring programs, and medical cannabis laws) in short and long-term, while we controlled for several protentional risk factors. The results of fixed-effect regression and significant tests indicated that state policies and laws were unlikely to result in an immediate reduction in overdose mortalities and comprehensive interventions were needed to restrain the epidemic. The third essay investigated the risk factors of 30-day readmission in patients with ischemic stroke at an individual level. We aimed to identify the main risk factors of stroke readmissions and prioritized them using machine learning techniques and logistic regression. We also introduced the most effective predictive model based on different performance metrics. We used the electronic health records of stroke patients extracted from two stroke centers within the Geisinger Health System from 2015 to 2018. This data set included a comprehensive list of clinical features, patients' comorbidities, demographical characteristics, discharge status, and type of health insurance. One of the major findings of this study was that stroke severity, insert an indwelling urinary catheter, and hypercoagulable state were more important than generally known diagnoses such as diabetes and hypertension in the prediction of stroke 30-day readmission. Furthermore, machine learning-based models can be designed to provide a better predictive model. Overall, this dissertation provided new insights to better understand the three major challenges of the U.S. healthcare system and improve its outcomes.
Production Pressure in Complex Socio-Technical Systems: Analysis, Measurement, and Prediction
Hashemian, Seyed Mohammad (Virginia Tech, 2024-06-17)
This dissertation brings together the areas of safety science and operations management through a mixed-methods approach to investigate the complex relationships between two, often conflicting, organizational goals - efficiency and safety, in sociotechnical systems (STSs). This research mainly focuses on production pressure (PrP) which is considered as one of the main negative outcomes of overprioritizing the efficiency aspect of STSs. This work seeks to introduce novel methodologies for assessing PrP in real time for the purpose of mitigating its risks and unwanted consequences, particularly in safety critical environments such as traffic control centers (TCCs). Essay 1 concentrates on the theoretical underpinnings of PrP by systematically reviewing the existing literature to clarify and unify the concept under the context of safety science. It identifies key factors contributing to PrP, its negative effects on safety performance in various industries, and potential mitigation strategies. By doing so, this essay contributes to the field through laying the groundwork for more effective management strategies to improve workplace safety. Essay 2 addresses a significant gap identified in Essay 1 by developing a methodology based on Data Envelopment Analysis (DEA) for the ongoing measurement and monitoring of PrP. This innovative approach introduces a quantitative mechanism that juxtaposes efficiency and safety related outcomes of hourly performance in safety critical environments. This proposed method allows for a detailed analysis of performance dynamics within STSs. The practical application of this model is demonstrated through its implementation in the infrastructure management system of INFRABEL, the Belgian National Railroad Company. Essay 3 advances the conversation by tackling the predictive limitations of the DEA model established in Essay 2. It integrates Machine Learning (ML) techniques with DEA to develop an innovative method for forecasting near-future PrP levels for proactive management of safety risks. The major contribution of Essay 3 is the novel interface between ML and DEA that can improve decision-making capabilities of managers in safety-critical STSs through real-time monitoring and predictive analytics. Together, these studies contribute to the theoretical discussions around PrP and present practical solutions to longstanding challenges in safety science and operational management.
Resource Allocation Decision-Making in Sequential Adaptive Clinical Trials
Rojas Cordova, Alba Claudia (Virginia Tech, 2017-06-19)
Adaptive clinical trials for new drugs or treatment options promise substantial benefits to both the pharmaceutical industry and the patients, but complicate resource allocation decisions. In this dissertation, we focus on sequential adaptive clinical trials with binary response, which allow for early termination of drug testing for benefit or futility at interim analysis points. The option to stop the trial early enables the trial sponsor to mitigate investment risks on ineffective drugs, and to shorten the development time line of effective drugs, hence reducing expenditures and expediting patient access to these new therapies. In this setting, decision makers need to determine a testing schedule, or the number of patients to recruit at each interim analysis point, and stopping criteria that inform their decision to continue or stop the trial, considering performance measures that include drug misclassification risk, time-to-market, and expected profit. In the first manuscript, we model current practices of sequential adaptive trials, so as to quantify the magnitude of drug misclassification risk. Towards this end, we build a simulation model to realistically represent the current decision-making process, including the utilization of the triangular test, a widely implemented sequential methodology. We find that current practices lead to a high risk of incorrectly terminating the development of an effective drug, thus, to unrecoverable expenses for the sponsor, and unfulfilled patient needs. In the second manuscript, we study the sequential resource allocation decision, in terms of a testing schedule and stopping criteria, so as to quantify the impact of interim analyses on the aforementioned performance measures. Towards this end, we build a stochastic dynamic programming model, integrated with a Bayesian learning framework for updating the drug’s estimated efficacy. The resource allocation decision is characterized by endogenous uncertainty, and a trade-off between the incentive to establish that the drug is effective early on (exploitation), due to a time-decreasing market revenue, and the benefit from collecting some information on the drug’s efficacy prior to committing a large budget (exploration). We derive important structural properties of an optimal resource allocation strategy and perform a numerical study based on realistic data, and show that sequential adaptive trials with interim analyses substantially outperform traditional trials. Finally, the third manuscript integrates the first two models, and studies the benefits of an optimal resource allocation decision over current practices. Our findings indicate that our optimal testing schedules outperform different types of fixed testing schedules under both perfect and imperfect information.
A Stakeholder Analysis of Infant Mortality in Ohio: Key Behaviors and Their Formulations
Hosseinichimeh, Niyousha; Kim, Hyunjung; Ebrahimvandi, Alireza; Iams, Jay; Andersen, David F. (2017-11-27)
This document reports stakeholder behaviors considered for modeling the impact of progesterone therapy on infant mortality in the state of Ohio and describes the formula used in the model. In part I, we present seven classes of stakeholder behaviors. For each stakeholder, first, we explain the behavior in Ohio. Then we provide evidence from the literature. Finally, we state how the behavior was captured in the system dynamics model. In part II, we show the structure of the model and the formula used to simulate the model.

Browsing by Author "Hosseinichimeh, Niyousha"

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