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

Colorectal cancer screening in newly insured Medicaid members: a review of concurrent federal and state policies

Meghan C. O'Leary, Lich, K. H., Gu, Y., Wheeler, S. B., Coronado, G. D., Bartelmann, S. E., … Davis, M. M. (2019), BMC HEALTH SERVICES RESEARCH. https://doi.org/10.1186/s12913-019-4113-2

Identifying trade-offs in equity and efficiency for simultaneously optimizing location and multipriority dispatch of ambulances

Enayati, S., Mayorga, M. E., Toro-Diaz, H., & Albert, L. A. (2019), INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH, 26(2), 415–438. https://doi.org/10.1111/itor.12590

Ambulance redeployment and dispatching under uncertainty with personnel workload limitations

Enayati, S., Ozaltin, O. Y., Mayorga, M. E., & Saydam, C. (2018), IISE Transactions, 50(9), 777–788. https://doi.org/10.1080/24725854.2018.1446105

Introduction to the Special Issue on Advancing Health Services

Maillart, L. M., & Mayorga, M. E. (2018), SERVICE SCIENCE, 10(3), V-. https://doi.org/10.1287/serv.2018.0225

Real-time ambulance redeployment approach to improve service coverage with fair and restricted workload for EMS providers

Enayati, S., Mayorga, M. E., Rajagopalan, H. K., & Saydam, C. (2018), OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 79, 67–80. https://doi.org/10.1016/j.omega.2017.08.001

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. https://doi.org/10.1186/s12971-017-0121-3

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., … Wheeler, S. B. (2017), Preventing Chronic Disease, 14. https://doi.org/10.5888/pcd14.160158

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) (pp. 2881–2892). https://doi.org/10.1109/wsc.2017.8248011

A maximum expected covering problem for district design

Ansari, S., Mclay, L. A., & Mayorga, M. E. (2017), Transportation Science, 51(1), 376–390. https://doi.org/10.1287/trsc.2015.0610

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. https://doi.org/10.1057/s41274-017-0219-2

View all publications via NC State Libraries

Grants

RAPID: Collaborative Research: Defining a Performance Measurement Framework for Spontaneous Volunteer Management Systems in Post-Disaster Relief

National Science Foundation (NSF)(11/01/18 - 10/31/19)

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RAPID: Collaborative Research: Defining a Performance Measurement Framework for Spontaneous Volunteer Management Systems in Post-Disaster Relief

Sponsor: National Science Foundation (NSF)

Start Date: 11/01/18

End Date: 10/31/19

Abstract

This study is concerned with volunteer management systems (VMSs) that seek to incorporate spontaneous volunteers into organized relief efforts. We use the term spontaneous volunteer management system to describe the process an organization follows to acquire spontaneous volunteers and assign them to duties. This definition inlcudes (i) Points of Distribution (PODs), which can be thought of as the retailers of the humanitarian supply chain where relief supplies are distributed directly to beneficiaries; (ii) physical or virtual volunteer reception centers (VRCs) that focus on volunteer registration, training, and deployment; or (iii) the volunteer supply chain network which includes interactions among multiple PODs and/or VRCs. What constitutes an effective VMS depends on whose perspectives is taken. We propose a comprehensive analysis of performance measurements for volunteer management systems that considers the perspective of all stakeholders: the organizations (or volunteer supply chain), the volunteers, and the beneficiaries. To do so, we plan to (i) interview stakeholders to determine perceptions of what the key performance indicators should be; (ii) collect real-time data we can later analyze to estimate performance based on VMSs currently in place; and (iii) determine the key VMS decisions that influence the performance indicators of interest.

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Improving Patient Throughput in the Emergency Department at Southeastern Regional Medical Center

Southeastern Regional Medical Center (SRMC)(6/01/18 - 5/31/19)

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Improving Patient Throughput in the Emergency Department at Southeastern Regional Medical Center

Sponsor: Southeastern Regional Medical Center (SRMC)

Start Date: 6/01/18

End Date: 5/31/19

Abstract

The Southeastern Regional Medical Center (SRMC) in Lumberton, NC, would like to improve throughput of patients within the Emergency Department (ED). The goal is to have 60% of patients exit the ED within 180 minutes; the current performance is that 40% of patients do so. Ultimately, measures such as this one drive patient satisfaction with the hospital, and thus the measure is a priority of the hospital leadership. Based on benchmarking and discussions with other hospitals, SRMC has asked for analytics and systems engineering approaches to assist in meeting their performance goal. The ISE team proposes engineering analytics and modeling to assist in understanding what actions will improve patient throughput in the ED to meet the performance goal of the hospital.

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UNC Center for Health Promotion and Disease Prevention Year 5

Centers for Disease Control & Prevention(9/30/18 - 9/29/19)

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UNC Center for Health Promotion and Disease Prevention Year 5

Sponsor: Centers for Disease Control & Prevention

Start Date: 9/30/18

End Date: 9/29/19

Abstract

Over the past 5 years, with funding support from CDC and the University Cancer Research Fund (UCRF), a multi-disciplinary team has completed claims data-based analyses to confirm and update the models used to characterize the colorectal cancer (CRC) “hotspot” in the northeastern part of North Carolina (NC), to identify gaps in CRC screening delivery in NC and Oregon (OR), and to build a novel microsimulation model to understand the reach, comparative effectiveness and cost effectiveness of various health policies and evidence-based interventions (EBIs) on CRC screening and outcomes in NC and OR. In this work, we will leverage previous work to expand the model to understand which EBIs are expected to have the most potential for implementation success in specific sub-populations within northeastern NC and OR and to work with local stakeholders to virtually test/compare those EBIs, select EBIs for local implementation, and evaluate implementations strategies and outcomes in those sub-populations. This goal will be achieved by the following activities undertaken by the full research team: 1) Conduct stakeholder engagement to refine project goals for northeastern counties of NC and OR 2) Audit and edit our existing simulation model to facilitate future adaptations to new contexts 3) Repurpose our existing simulation model to understand how local implementation of specific interventions and policies in specific sub-populations would be expected to affect costs and local CRC screening rates and outcomes, and share model outputs to mobilize stakeholder support for EBI implementation. 4) Develop population health capacity for CRC screening in northeastern NC and explore models to enhance follow-up endoscopy access after abnormal CRC screening in underserved NC communities

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Analytics-Based Platform for Diabetic Retinopathy Care Management

National Institutes of Health (NIH)(6/01/19 - 4/30/20)

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Analytics-Based Platform for Diabetic Retinopathy Care Management

Sponsor: National Institutes of Health (NIH)

Start Date: 6/01/19

End Date: 4/30/20

Abstract

Analytics-Based Platform for Diabetic Retinopathy Care Management This proposal will demonstrate the feasibility and necessity of integrating data analytics and systems science into a cost-effective, end-to-end solution (Retinal Care DRâ„¢) for diabetic retinopathy care. Over 30 million people in the US have diabetes. Of these, 25% will develop diabetic retinopathy (DR) and 4% will develop vision-threatening diabetic retinopathy (VTDR). Ninety percent of all blindness from DR is preventable. However, prevention requires timely VTDR diagnosis and patient compliance with treatment recommendations. Currently, only 50% of patients receive an annual dilated eye exam, leaving half of those at risk for vision loss undiagnosed and untreated. Further, of those diagnosed with VTDR, only 30% undergo timely treatment. These two barriers have established DR as the leading cause of blindness in working-age Americans and highlight the need for a comprehensive solution that integrates the entire care path, from accurate, accessible diagnosis to effective care coordination and evidence-based treatment. Currently, Retinal Care Inc. can identify 65% of a population of patients with diabetes as not having VTDR with 99% negative predictive value using a handheld electroretinography (ERG) and pupillography device (RETEval, LKC Technologies) in a primary care setting as part of the Retinal Care DR program. This 2-minute test reduces the proportion of patients requiring the care coordination component of the service to 35% of the initial population. However, according to the published literature, only 4% of these patients should have VTDR, while the remaining 31% will undergo costly, time-consuming, and ultimately unnecessary care coordination to ensure follow-up with an eye care provider. Given the critical role of care coordination in reducing blindness from DR, minimizing the cost and maximizing the effectiveness of this effort is required to optimize the patient experience, their outcomes, and the commercial viability of the company. The following specific aims are designed to achieve these goals: Aim 1: Improve the ability to accurately identify patients at increased risk by including patient health and demographic attributes in a predictive algorithm for VTDR diagnosis. The accuracy of the RETeval device in identifying VTDR was established in a 468-patient study using the universally accepted ETDRS gold standard as truth. However, additional patient variables that are potentially correlated to DR severity are available but not yet incorporated into the prediction algorithm, including gender, medication, type of diabetes, diagnosis date, and additional features of the ERG and pupillary waveforms. Improving diagnostic accuracy will be achieved through machine learning algorithms for patient classification techniques, including multiple logistic regression and Generalized Linear Models. All patient variables will be candidates for independent variables. Variables for final inclusion in the model will be selected based on model fit while accounting for variable interactions. Varying cutoff values will be chosen to maximize accuracy (positive and negative predictive values). As classification techniques, principal component (PCA) and variable cluster analyses will be used to identify features of patients with similar attributes, potentially resulting in subpopulation-specific models. Aim 2: Optimize care coordination, from diagnosis to treatment, for patients at increased risk for VTDR. An ongoing, multi-year study of a diverse set of 2,000 patients with diabetes conducted and self-funded by Retinal Care (ClinicalTrials.gov: NCT03094819) has reinforced the critical role of care coordination in effective DR care, and the importance of minimizing the cost associated with care coordination efforts. Information collected from 500 currently-enrolled patients includes diagnostic results, primary care visits, care coordination (e.g. phone calls, text messages), follow-up and treatment compliance, sociodemographic information, and other pote

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Scaling Colorectal Cancer Screening Through Outreach, Referral, and Engagement (SCORE): A State-Level Program to Reduce Colorectal Cancer Burden in Vulnerable Populations

National Institutes of Health (NIH)(9/30/18 - 8/31/19)

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Scaling Colorectal Cancer Screening Through Outreach, Referral, and Engagement (SCORE): A State-Level Program to Reduce Colorectal Cancer Burden in Vulnerable Populations

Sponsor: National Institutes of Health (NIH)

Start Date: 9/30/18

End Date: 8/31/19

Abstract

1. SPECIFIC AIMS Despite strong evidence that colorectal cancer (CRC) screening reduces mortality, rates of CRC screening, follow-up, and referral-to-care (herein referred to collectively as “CRC screening”) remain suboptimal in the US. In North Carolina, underuse of CRC screening contributes to high CRC burden in vulnerable communities in NC. Underuse of screening CRC contributes to CRC mortality “hotspots” in specific rural areas in North Carolina. In such regions, CRC burden disproportionately affects African Americans, American Indians, Appalachians, Medicaid beneficiaries, and the uninsured. Community Health Centers (CHCs) and the NC Medicaid program play a critical role in providing preventive care for vulnerable populations. Unfortunately, CRC screening rates in CHCs and in the NC Medicaid population are well below national targets. Multiple patient, provider, and system-level factors contribute to these low rates, and hence multi-level interventions are needed to achieve national screening targets. Accumulating evidence suggests that strategies that employ outreach, especially outreach that expands the use fecal immunochemical testing (FIT), in-reach, and improved colonoscopy access can increase screening rates. However, evidence is lacking regarding how such strategies can be implemented in ways that are effective, efficient, and scalable. Thus, an urgent need exists to fill multiple gaps in our understanding about how to implement evidence-based strategies to increase CRC screening. Specifically, studies of multi-level interventions that simultaneously combine outreach and in-reach while reducing barriers along the screening–follow-up–referral continuum are needed. Further, there is a critical need to develop mechanisms for providing stakeholders with actionable information about intervention cost and impact to inform resource allocation decisions. Lastly, evaluation and dissemination of local innovations are also needed. Our long-term goal is to reduce CRC burden and disparities in our region and nationally. Our objective for this application is to leverage our expertise in CRC screening research, implementation science, and simulation modeling to fill critical evidence gaps regarding screening in vulnerable populations. Our central hypothesis is that combining a scalable outreach program that leverages both CHC and Medicaid data infrastructure, CHC visit-based approaches, and structured navigation to colonoscopy will increase CRC screening in a way that is acceptable, cost-effective, and scalable. We propose a pilot phase (aim 1) during which we will adapt and refine our multi-level intervention to optimize feasibility, acceptability, and impact potential. Then, in an implementation phase (aim 2) we will evaluate the intervention using experimental and observational methods to assess efficacy and implementation outcomes. During these phases, we will also refine approaches for simulation modeling to engage stakeholders in making resource allocation decisions (aim 3) and evaluate and disseminate local innovations to improve CRC screening in NC (aim 4). Specific Aim 1: Refine and pilot-test a stepped, multilevel intervention for CRC screening, follow-up and referral-to-care for vulnerable populations in three NC regions. Activities: Build on extensive, complementary work in NC, to engage partners in refining multi-level screening interventions and optimizing their feasibility, acceptability, and potential impact. Work with ACCSIS Coordinating Center to harmonize implementation and efficacy measures. Specific Aim 2: Using experimental and mixed methods approaches, evaluate a multi-level CRC screening intervention in vulnerable populations in North Carolina. Activities: Conduct an efficacy-implementation hybrid trial in rural FQHC networks (4 in North Carolina). Specific Aim 3: Refine and evaluate methods for using microsimulation modeling to engage local and regional stakeholders in making data-driven decisions about resource investment to improve CRC screening. Activities: Conduct

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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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