Education

Research Description

Modeling Marketing and Operations Interface, Analysis of Supply Chain Models, Application of Stochastic Optimization in Production and Service Systems, Analysis of Queuing Systems.

Publications

Simulating triage of patients into an internal medicine department to validate the use of an optimization-based workload score

Agor, J., McKenzie, K., Mayorga, M. E., Ozaltin, O., Parikh, R. S., & Huddleston, J. (2017), In 2017 winter simulation conference (wsc). (Winter Simulation Conference Proceedings, ) (pp. 2881-2892).

The optimal assignment of spontaneous volunteers

Mayorga, M. E., Lodree, E. J., & Wolczynski, J. (2017), Journal of the Operational Research Society, 68(9), 1106-1116.

Changes in cigarette smoking initiation, cessation, and relapse among US adults: A comparison of two longitudinal samples

Yi, Z. N., Mayorga, M. E., Lich, K. H., & Pearson, J. L. (2017), Tobacco Induced Diseases, 15.

A maximum expected covering problem for district design

Ansari, S., Mclay, L. A., & Mayorga, M. E. (2017), Transportation Science, 51(1), 376-390.

Cost-effectiveness analysis of four simulated colorectal cancer screening interventions, North Carolina

Lich, K. H., Cornejo, D. A., Mayorga, M. E., Pignone, M., Tangka, F. K. L., Richardson, L. C., Kuo, T. M., Meyer, A. M., Hall, I. J., Smith, J. L., Durham, T. A., Chall, S. A., Crutchfield, T. M., & Wheeler, S. B. (2017), Preventing Chronic Disease, 14.

The numbers of new waitlist registrants and removals are outpacing the number of liver transplants: National trends and regional variation from 2003 through 2014

Yi, Z. A., Mayorga, M., Wheeler, S. B., Hayashi, P. H., Barritt, A. S., & Orman, E. S. (2016), Hepatology, 64, 701A-701A.

Simulation of triaging patients into an internal medicine department to validate the use of an optimization based workload score

Agor, J., McKenzie, K., Ozaltin, O., Mayorga, M., Parikh, R. S., & Huddleston, J. (2016), In 2016 winter simulation conference (wsc). (Winter Simulation Conference Proceedings, ) (pp. 3708-3709).

A Discrete event simulation model to estimate population level health and economic impacts of smoking cessation interventions

Mayorga, M. E., Reifsnider, O. S., Wheeler, S. B., & Kohler, R. E. (2016), In 2016 10th european conference on antennas and propagation (eucap). (Proceedings of the European Conference on Antennas and Propagation, ) (pp. 1257-1268).

Creating common patients and evaluating indiviual results: issues in indivual simulation for health policy analysis

Cornejo, D., Mayorga, M. E., & Lich, K. H. (2016), In 2016 10th european conference on antennas and propagation (eucap). (Proceedings of the European Conference on Antennas and Propagation, ) (pp. 1387-1398).

A Framework for modeling the complex interaction between breast cancer and diabetes

Goodarzi, S. H., McKenzie, K., Nataraj, N., Ivy, J. S., Mayorga, M., Mason, J., & Tejada, J. (2016), In 2016 10th european conference on antennas and propagation (eucap). (Proceedings of the European Conference on Antennas and Propagation, ) (pp. 1245-1256).

View all publications via NC State Libraries

Grants

Center for Health Promotion and Disease Prevention

Centers for Disease Control & Prevention(9/30/17 - 9/29/18)

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Center for Health Promotion and Disease Prevention

Sponsor: Centers for Disease Control & Prevention

Start Date: 9/30/17

End Date: 9/29/18

Abstract

The Carolina Cancer Screening Initiative (CCSI) is a newly initiated effort, sponsored by the Lineberger Comprehensive Cancer Center, with the mission to develop, test, and implement evidence-based interventions (EBIs) to improve cancer screening in practice. The initiative’s first major effort is focused on implementing an intervention to address CRC screening in an area of 11 counties within northeastern North Carolina. The area was recently identified as one of three distinct “hotspots” in the US due to excessive colorectal cancer deaths. This is an opportune time to use our previous CDC-funded work to develop a state-based individual simulation model of colorectal cancer (CRC). The model includes progression, screening, and treatment for CRC, and CRC interventions. We propose to expand on the model to understand which evidence-based interventions will have the most potential for success and inform CCSI’s planning and implementation efforts in this part of the state. This goal will be achieved by: Aim 1: Repurpose existing individual-based simulation model to understand how local implementation of specific interventions and policies would be expected to affect local CRC screening rates and outcomes Aim 2: Use the decision model to mobilize stakeholder support for EBI implementation. Title: UNC Center for Health Promotion and Disease Prevention- Year 4

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RAPID: Collaborative Research: Field Study of Volunteer Convergence in Post-Disaster Relief

National Science Foundation (NSF)(10/01/17 - 9/30/18)

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RAPID: Collaborative Research: Field Study of Volunteer Convergence in Post-Disaster Relief

Sponsor: National Science Foundation (NSF)

Start Date: 10/01/17

End Date: 9/30/18

Abstract

Volunteer convergence refers to the mass influx into affected areas by individuals who feel compelled to assist with relief efforts following large-scale disaster events. These “spontaneous” volunteers are often not affiliated with mainstream relief agencies and typically lack emergency response training or experience. Although volunteer convergence represents a valuable source of labor that can potentially augment emergency response capacity to perform time-sensitive relief activities, they also pose significant logistical challenges for relief agencies that can be addressed using OR/MS expertise. Unlike classical labor assignment problems, the management of post-disaster volunteers is characterized by uncertainty regarding the size, availability, and commitment of the labor pool. Consequently, the unique characteristics of volunteer convergence logistics inspired us to develop, in recent work, a novel OR/MS modeling framework, namely queuing control with not only random customer arrivals and departures, but also random server arrivals and abandonments. However, the current state of knowledge with regard to the logistics of volunteer convergence is limited. As such, this project intends to investigate volunteer convergence for the purpose of identifying key characteristics and processes that should be considered when developing OR/MS models for post-disaster volunteer management.

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LAS DO7 Mayorga - 3.2 Small Conflict Economies

Laboratory for Analytic Sciences(1/01/17 - 12/31/17)

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LAS DO7 Mayorga - 3.2 Small Conflict Economies

Sponsor: Laboratory for Analytic Sciences

Start Date: 1/01/17

End Date: 12/31/17

Abstract

DO7 Small Conflict Economies

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SCH: INT: Collaborative Research: S.E.P.S.I.S.: Sepsis Early Prediction Support Implementation System: CCHS Summer

National Science Foundation (NSF)(10/01/15 - 9/30/18)

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SCH: INT: Collaborative Research: S.E.P.S.I.S.: Sepsis Early Prediction Support Implementation System: CCHS Summer

Sponsor: National Science Foundation (NSF)

Start Date: 10/01/15

End Date: 9/30/18

Abstract

Every year approximately 700,000 people die in US hospitals. In 16% of them, the first diagnosis at death was septicemia – one of the most common delayed diagnoses associated with inpatient death. Sepsis is one of the ten leading causes of death. While it is difficult to estimate how many of these deaths could have been averted or postponed if a better system of care was in place, it is widely recognized that sepsis patients have better outcomes if treated earlier. We propose to take a date-driven evidence-based approach that integrates computer science and industrial engineering to develop personalized sepsis diagnosis and treatment plans. The goal of this research is to integrate electronic health records (EHR) and clinical expertise to provide an evidence-based framework for diagnosing sepsis patients, accurately risk-stratifying patients within the sepsis spectrum, and developing intervention policies that inform sepsis treatment decisions. We will achieve this research goal through accomplishing three specific aims based on a longitudinal data set of EHRs of Mayo Clinic Rochester hospital patients discharged and Christiana Care Health System hospital patients. The project will provide graduate students and health services researchers with cross-disciplinary educational experience. This is formalized via the rotational internships of engineering and computer science students that immerse them into the healthcare systems.

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LAS DO6 Mayorga - User Experience

Laboratory for Analytic Sciences(1/01/16 - 12/31/16)

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LAS DO6 Mayorga - User Experience

Sponsor: Laboratory for Analytic Sciences

Start Date: 1/01/16

End Date: 12/31/16

Abstract

DO6 User Experience

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SIP 14-010 Cancer Prevention & Control Research Network Coordinating Center

Centers for Disease Control & Prevention(9/30/15 - 9/29/17)

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SIP 14-010 Cancer Prevention & Control Research Network Coordinating Center

Sponsor: Centers for Disease Control & Prevention

Start Date: 9/30/15

End Date: 9/29/17

Abstract

The main objective of this contract is to leverage existing resources, including statewide multi-payer insurance claims data and a microsimulation model, to answer research questions at the state level related to optimizing CRC screening, particularly in medically vulnerable populations.

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LAS DO5 Mayorga Task 5.2

Laboratory for Analytic Sciences(3/24/15 - 12/31/15)

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LAS DO5 Mayorga Task 5.2

Sponsor: Laboratory for Analytic Sciences

Start Date: 3/24/15

End Date: 12/31/15

Abstract

This work answers the following research questions: What concepts and techniques will provide a foundation for specifying workflows along with the quality of experience (QoE) expectations and achieving effective resource allocations? Can we provide a framework for dynamically reallocating resources among users/agents according to user-defined performance objectives (i.e., QoS metrics: time-to-solution, accuracy, energy utilization) at multiple levels (e.g., component-level and end-to-end AW-level) based on the current system status. Can we adapt the optimization framework to deal with competing metrics and/or multiple-stake holders?

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SCH: INT: Collaborative Research: S.E.P.S.I.S.: Sepsis Early Prediction Support Implementation System

National Science Foundation (NSF)(10/01/15 - 9/30/19)

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SCH: INT: Collaborative Research: S.E.P.S.I.S.: Sepsis Early Prediction Support Implementation System

Sponsor: National Science Foundation (NSF)

Start Date: 10/01/15

End Date: 9/30/19

Abstract

Every year approximately 700,000 people die in US hospitals. In 16% of them, the first diagnosis at death was septicemia – one of the most common delayed diagnoses associated with inpatient death. Sepsis is one of the ten leading causes of death. While it is difficult to estimate how many of these deaths could have been averted or postponed if a better system of care was in place, it is widely recognized that sepsis patients have better outcomes if treated earlier. Sepsis is one of the most common of these diagnoses with delayed effective treatment interventions. As opposed to wrong diagnoses, delayed diagnoses have historically not been considered adverse events as there is no change in patient condition as a result of care delivered. However, patients with delayed diagnoses do have worse outcomes than those who receive timely treatment. These diagnostic and/or treatment delays associated with inpatient mortality and long term morbidity consequences represent a significant and modifiable patient safety issue. Awareness of sepsis is low; many septic patients are under-diagnosed at an early stage when aggressive treatment could still reverse the course of the infection. Early recognition and implementation of early goal directed therapy improves outcomes and decreases mortality. For every one hour delay in treatment of severe sepsis or severe shock with antibiotics, there is an incremental decrease in patient survival. For example, a delay in antibiotics of five hours decreases survival to 50%. We propose to take a date-driven evidence-based approach that integrates computer science and industrial engineering to develop personalized sepsis diagnosis and treatment plans. The goal of this research is to integrate electronic health records (EHR) and clinical expertise to provide an evidence-based framework for diagnosing sepsis patients, accurately risk-stratifying patients within the sepsis spectrum, and developing intervention policies that inform sepsis treatment decisions. We will achieve this research goal through accomplishing three specific aims based on a longitudinal data set of EHRs of Mayo Clinic Rochester hospital patients discharged and Christiana Care Health System hospital patients.

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LAS DO3 Task Order 2.3 Instrumentation

Laboratory for Analytic Sciences(3/31/14 - 5/15/15)

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LAS DO3 Task Order 2.3 Instrumentation

Sponsor: Laboratory for Analytic Sciences

Start Date: 3/31/14

End Date: 5/15/15

Abstract

Task Order 2.3 Instrumentation

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CAREER: Incorporating Patient Heterogeneity and Choice into Predictive Models of Health & Economic Outcomes

National Science Foundation (NSF)(9/01/13 - 5/31/17)

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CAREER: Incorporating Patient Heterogeneity and Choice into Predictive Models of Health & Economic Outcomes

Sponsor: National Science Foundation (NSF)

Start Date: 9/01/13

End Date: 5/31/17

Abstract

This Faculty Early Career Development (CAREER) Program award investigates how to obtain better predictions of the effectiveness of population-level health interventions. The research strategy is to create consumer choice models that incorporate patient heterogeneity into predictive models of health and economic outcomes. The patient plays a key role in conditions that are chronic and not immediately life threatening, but that in the long term can be harmful through associated co-morbidities. This research builds on collaborations with health sciences experts to take an interdisciplinary approach. Consumer choice models will be transformed into models for patient choice. This allows for individual patient preferences to be considered (e.g., time-preferences, willingness-to-pay for treatment) which may depend on socio-demographic patient attributes. These new patient choice models will be embedded into predictive models of health outcomes (smoking cessation and maternal drinking) and long term treatment effectiveness studies will be conducted at the population level. The educational plan includes advising and mentoring activities that work with discovery-oriented "creative inquiry" projects involving multi-level student teams. If successful, the results of this research will allow health science experts to move from efficacy to effectiveness characterization by explicitly considering individual patient preferences. These new models may also be used in predictive health models of other policy-relevant issues. Ultimately, the inclusion of patient choice will result in more accurate estimates of treatment effectiveness and more accurate predictions of health and economic outcomes. This allows policy makers to choose the most efficient intervention strategies, with a richer information set than can be provided by clinical trials alone. Middle school students studying in technology programs will be introduced to the field of industrial engineering and engage with undergraduate research teams. Graduate students will mentor participating undergraduates, resulting in a pipeline of students who will be prepared to undertake technical careers in the healthcare service industry and in academia.

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