About

Robert Riggs is an Assistant Professor, Academic General Faculty, on the Teaching Track at the University of Virginia School of Engineering and Applied Science. He serves as the Director of Undergraduate Programs in Systems Engineering and the Director of the Systems Mechanical Engineering (ME) and Virginia Engineering Outreach (VEO) Programs. His research interests include integer programming and combinatorial optimization, healthcare systems engineering, applying lean enterprise principles to healthcare and manufacturing applications, optimization of disassembly and remanufacturing systems, and game theory. Riggs holds a PhD in Industrial and Operations Engineering from the University of Michigan, earned in 2015, a Master of Science in Engineering in Industrial and Operations Engineering from the same university in 2011, and a Bachelor of Science in Industrial and Manufacturing Engineering from Kettering University in 2008.

Research topics

• Medicine
• Physical therapy
• Machine Learning
• Computer Science
• Psychiatry
• Artificial Intelligence
• Internal medicine
• Demography
• Psychology
• Clinical psychology
• Engineering
• Psychotherapist

Selected publications

• Check-In Check-Up: Analyzing and Improving Pre-Appointment Engagement in a Primary Care Clinic at UVA Health System

  2025-05-02

  articleSenior author

  The UVA Health System utilizes a cloud-based electronic medical record (EMR) platform, featuring a secure patient portal that helps patients manage their health. This portal empowers users by enabling them to complete key health tasks, such as communicating with their care team, accessing health records, and coordinating appointments. This project focuses on increasing pre-appointment engagement through the patient portal to enhance the patient experience at a primary care clinic (PCC) within UVA Health.Patients can be assigned multiple types of pre-appointment tasks via the portal. One task, referred to as eCheck-In, is available to all patients prior to their appointment. This allows patients to complete necessary paperwork before their appointments, which streamlines visits. However, eCheck-Ins were completed for only 35.3% of patient encounters in 2024 at the PCC. To gather insights into this issue, an online survey was distributed to PCC patients to assess portal usage and the existing eCheck-In experience. Major themes emerged including issues of redundancy, poor technological literacy, and underutilization of the patient portal mobile app. Success in prior studies and support from the clinic’s providers suggest piloting a volunteer-led education program for eCheck-In and general patient portal support.Another type of pre-appointment task is specific to Medicare Annual Wellness Visits (AWV) where patients complete a comprehensive pre-visit questionnaire to streamline appointments and update providers on their annual health. By completing this questionnaire in advance of their appointments, patients and providers can collaborate more effectively to develop or update a personalized prevention plan, ensuring a more meaningful and efficient visit. For the previous two years at the PCC, the completion rate for pre-visit AWV questionnaires was just 50.1%.In 2023, one PCC provider implemented a new strategy to try and personally increase their patients’ completion rates of the AWV questionnaire. This strategy included personalized direct messages sent from the provider to their patients. Our analysis of this new strategy found that the provider significantly improved the completion rate among their patients by 18.6%. This strategy optimized their workflow, enhanced patient engagement, and improved overall visit quality.This strategy, and other findings in this paper, could be broadened to other providers, clinics, and medical centers to ensure a more efficient, effective, and equitable healthcare experience for all.

  PublisherDOI

• Refining a Novel AI Restaurant Recommender Application: A Systems Approach to Increasing User Engagement and Retention

  2025-05-02

  articleSenior author

  Deciding which restaurant to eat at often poses an inconvenience for many individuals. The decision-making process is riddled with a variety of factors such as personal preferences, social dynamics, and an overwhelming number of options. Our study addresses this issue by partnering with a startup, dinemait, that utilizes an artificial intelligence (AI) recommendation model to provide curated restaurant suggestions to its mobile application users. The goal of this work is centered around improving dinemait’s application to retain and grow its active user base. Our team used a systems-based approach to increase user engagement by: (1) evaluating the existing application, and (2) improving outreach features and techniques. After an internal review of the application, a study was conducted to gain user-centric data to further assess it. It consisted of focus group discussions and surveys to gain insight into necessary improvements and valuation of the application. Next, by researching specific marketing strategies and ideating push notifications to encourage interaction with the application, we provided suggestions for dinemait to deploy in order to gain exposure. Our results will provide us with insights into the viability of the current application and outreach tactics, to then guide our recommendations and implementation.

  PublisherDOI

• Redesign of the University of Virginia’s Emergency Department Waiting Room Layout to Optimize Patient Flow and Increase Satisfaction

  2025-05-02

  article

  Increasing demands on emergency departments (EDs) due to rising patient volumes and operational inefficiencies necessitate innovative solutions to enhance patient flow and satisfaction. Data from UVA Health reveals substantial ED crowding, with a 12% increase in ED visits between 2022 and 2023, and a 25% increase from 2021 to 2023. To address these challenges, a simulated redesign of the waiting room at the University of Virginia (UVA) ED was completed to improve space utilization and streamline patient movement. Current designs, characterized by repeated patient returns to the waiting area, create congestion and hinder the perception of progress in care. This redesign aims to expand available space and create “progression areas” where patients can be effectively managed post-triage, reducing returns to the main lobby and thereby minimizing congestion. Utilizing FlexSim HC simulation software, both the current and proposed layouts are modeled to forecast key operational metrics. Validation was conducted by comparing simulation outcomes with UVA Health data on patient wait times, bed utilization, and throughput, ensuring the reliability of the proposed improvements. While the new waiting room is farther from the Patient in Triage (PIT) area, thus increasing patient travel time and the average total triage time by $2 \%$, the variances of the arrival-to-roomed, arrival-to-triage, and triage-to-roomed times decreased by $57 \%, 83 \%$, and $67 \%$ respectively. Additionally, the time taken to bring trauma patients from registration to a trauma bay decreased by $30 \%$. Reducing the variance of wait times will increase patient satisfaction by eliminating the tail of unexpectedly long wait times. Reducing the variance enhances the reliability of wait time estimates, making actual experiences more aligned with expectations. Further research will focus on implementation challenges, including staff adaptation and continuous real-time assessment of operational performance.

  PublisherDOI

• A novel approach for multi-objective truck scheduling problems in a cross-docking center

  International Journal of Systems Assurance Engineering and Management · 2024-10-17 · 4 citations

  articleSenior author
  PublisherDOI

• Expanding VIAble Employment for Adults with Autism: A Systems Approach to Increase Nonprofit Sales

  2024-05-03

  article

  Across industries, people with disabilities face barriers obtaining and maintaining employment. Our project addresses this issue through a partnership with VIAble Ventures, a microbusiness run by the VIA Centers for Neurodevelopment. VIAble Ventures sells spa products like candles and bath salts, all of which are made by artisans with autism. The program provides on-site job training and a source of income for adults with intellectual and developmental disorders. The goal of this work was centered on increasing VIAble Venture sales to expand employment opportunities for autistic adults in the local Charlottesville area. Currently, sales depend heavily on availability and seasonality of in-person sales and limited online sales. Our team used a systems approach to increase online sales on VIAble Venture’s website by: (1) analyzing past transactions, and (2) redesigning the website. Using data analytics, we projected top selling scents seasonally and identified high margin products that VIAble Ventures could prioritize to increase profits. Additionally, our team conducted focus group testing on the navigation of their original website to identify pain points, notably the salience of navigational tools and clarity of the company mission. These findings guided the final redesign of the new Square website. By increasing production during currently lower sale months and streamlining the online user experience, VIAble Ventures could increase sales and thus increase the number of adults with autism employed.

  PublisherDOI

• Exploring Discrepancies: Analyzing Electronic Medical Records Data Against Direct Observations

  2024-05-03 · 1 citations

  articleSenior author

  An Electronic Medical Record, or EMR, is a digital way to keep track of patient information. EMRs are used by healthcare providers for diagnosis, treatment, and clinic decisions. There are several factors that impact the accuracy of EMRs, including data entry accuracy, backend configuration, and the time of documentation. This project aims to improve the patient and worker experience in primary care clinics, focusing on the influence of EMR. Through observations at the University of Virginia’s University Physicians Primary Care Clinic, appointment milestones were recorded to create a dataset for comparison with the EMR dataset. Discrepancies between in-person observations and EMR data were noted. Metrics were applied to analyze decision implications based on each dataset. This paper underscores the importance of accommodating discrepancies for reliable healthcare information and decision-making.

  PublisherDOI

• Improving Patient Flow in a Healthcare Clinic Post COVID-19: A Data Validation and Exploratory Analysis Approach

  2023-04-27 · 3 citations

  articleSenior author

  Since the beginning of the COVID-19 pandemic, healthcare clinics have faced increased inefficiencies due to an influx of patients returning to clinical care. The strain on nursing resources leads to long patient waiting times, which can lead to provider burnout and more stressful patient care. Here we compare the electronic medical record (EMR) timestamp data with observational data to understand better the current patient flow at the University Physicians of Charlottesville (UPC) clinic, a primary care clinic within the UVA Health System. Our overarching goal for this study is to propose data-driven solutions to improve clinic efficiency and reduce stress for providers, nurses, and staff. We implemented a two-phased analysis approach. The first phase involved cross-checking the EMR timestamp data with observed data to validate the consistency and reliability of the EMR timestamp data and thus allow us to confidently identify areas of improvement within the clinic, such as peak waiting periods. In the second phase, we used the validated data to analyze the distribution of delays during different appointment stages. Using a discrete event simulation, we recommend solutions that could improve the patient experience and reduce stress on medical personnel. The findings are further supported by graphical analyses of the delays in patient rooming depending on the time of day, length of the appointment, and provider. Overall, the two-phased approach will provide the clinic with a holistic understanding of the causes behind delays in patient care.

  PublisherDOI

• ChatGPT: Applications, Opportunities, and Threats

  2023 · 277 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.

  PublisherDOI

• A Systems Approach to Improving the Spectator Experience at Collegiate Football Games

  2023-04-27 · 1 citations

  articleSenior author

  As ticket sales and student attendance for University of Virginia (UVA) home football games decline, the university must find ways to engage fans with the football program. The following technical evaluation used a systems methodology to improve the customer experience for Scott Stadium spectators, with the additional hope of paralleling an improvement in the school’s football community. Taking a three-pronged approach, the analysis focused on traffic, in-game experience, and website design. A ride-along and interviews with the University Police Department (UPD) yielded observational data regarding game day pedestrian and vehicular traffic. The UVA Athletics Department provided ticketing data. Concessions numbers supplied by Aramark, a student survey, and the team’s observations from game days offered information regarding in-game experience. The research team’s examination of the department’s digital presence gave an analysis of the website design. The interview data and analysis of patron and vehicular traffic patterns indicated that a paucity of signage, GPS directions that only route drivers to prepaid parking, and a dated traffic plan contribute to pregame traffic backups. Investigating ticketing statistics showed that tardy students and inefficient distribution of stadium staff create sparsely attended kickoffs and entrance bottlenecks. An assessment of the game day website revealed a User Experience (UX) design that hinders fans from finding parking, concessions, and general information efficiently. Analysis of concessions data revealed that stadium staff fail to make student-preferred food items available in multiple convenient locations. Finally, the survey data revealed that many students leave before halftime, find the in-game entertainment in need of improvement, and attend games to fraternize with friends rather than watch football. Due to these results, the primary traffic recommendations involve increasing parking signage during game days and an updated traffic plan. To improve the in-game experience, suggestions include prioritizing student-preferred food items, rearranging event staff at entrance gates, incorporating incentives that encourage students and fans to arrive early and stay late at games, and updating in-game entertainment to shift student focus to on-field activities. Finally, recommendations to restructure the game day website include reducing text by utilizing images and bullet points, highlighting critical content through bolding and underlining, and grouping similar information with panels and icons.

  PublisherDOI

• ChatGPT: Applications, Opportunities, and Threats

  arXiv (Cornell University) · 2023-04-14 · 41 citations

  preprintOpen access

  Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations.

  PublisherOA PDFDOI

Frequent coauthors

• John Varga

  University of Michigan–Ann Arbor

  41 shared

• Luc Mouthon

  Assistance Publique – Hôpitaux de Paris

  30 shared

• Vanessa L. Malcarne

  San Diego State University

  26 shared

• Susan J. Bartlett

  McGill University Health Centre

  22 shared

• Joanne Manning

  21 shared

• Ghassan El‐Baalbaki

  20 shared

• Monique Hinchcliff

  Yale University

  20 shared

• Shervin Assassi

  The University of Texas Health Science Center at Houston

  20 shared