Headshot of Shannon Harris

Shannon Harris

Assistant Professor

Area: Supply Chain Management and Analytics

  • Snead Hall
  • 301 W. Main Street
  • Box 844000
  • Richmond, VA, 23284-4000
  • Office: B4133
  • Alternate Website: https://sites.google.com/vcu.edu/shannonlharris

Education

  • PhD, University of Pittsburgh, 2016.
  • BS, George Mason University, 2007.

Expertise

  • Healthcare Scheduling
  • Predictive Analytics
  • Social Justice Health Ops

Interests

Teaching
  • Data Analysis
    Healthcare Transformation and Analytics
    Data Mining
Bio

Shannon Harris earned a PhD in Business Analytics and Operations from the University of Pittsburgh in 2016. Her research interests include mathematical and empirical modeling with a focus on healthcare applications. Primarily, she analyzes the attendance behavior of patients to outpatient clinic appointments, and how that behavior affects a clinic’s scheduling practices. Additionally, she has projects researching racial bias in healthcare scheduling, and how people-centric operations affect patients’ transition of care from the hospital to home. Her work has been published in the European Journal of Operational Research, Manufacturing and Service Operations Management, Journal of Operations Management, Military Medicine, and the Journal of Multi-Criteria Decision Analysis.

Shannon worked as a management consultant at Deloitte Consulting and as a cost analyst at Technomics, Inc. She has served as a track chair for several INFORMS and CORS conference sessions, and has served on the board of the INFORMS Minority Issues Forum (MIF), INFORMS Diversity Committee, and the PhD Project student planning committee.

Research

Published Intellectual Contributions
Conference Proceeding
  • Samorani, M., Harris, S. (2019). Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling. Proceedings of the 2019 INFORMS Workshop on Data Mining and Decision Analytics (DMA 2019).
  • Samorani, M., Harris, S. (2019). The Impact of Probabilistic Classifiers on Appointment Scheduling with No-Shows. Fortieth International Conference on Information Systems, Munich.
  • Castaneda, E., Gonzalez, J., Harris, S., Kim, J. (2007). Optimized airport security infrastructure system (OASIS). (pp.1--6). Systems and Information Engineering Design Symposium, 2007. SIEDS 2007. IEEE.
Journal Article
  • Samorani, M., Harris, S., Goler Blount, L., Lu, H., Santoro, M. (in press). Overbooked and Overlooked: Machine Learning and Racial Bias in Medical Appointment Scheduling. Manufacturing and Service Operations Management . DOI: https://doi.org/10.1287/msom.2021.0999
  • Shanklin, R., Samorani, M., Harris, S., Santoro, M. (2022). Ethical Redress of Racial Inequities in Artificial Intelligence: Lessons from Decoupling Machine Learning from Optimization in Medical Appointment Scheduling. (4 ed., vol. 35, pp.19). Philosophy and Technology. DOI: 10.1007/s13347-022-00590-8
  • Chun, Y., Harris, S., Chandrasekaran, A. (2022). Improving Care Transitions with Standardized Peer Mentoring: Evidence from Intervention Based Research Using Randomized Control Trial. (2 ed., vol. 68, pp.185-214). Journal of Operations Management . DOI: https://doi.org/10.1002/joom.1170
  • Simons, A., McHugh, K., Appling, S., Harris, S., Burgoon, J. (2022). Instructional Approaches: Anatomy Education of Physical Therapists. (1 ed., vol. 15, pp.102-114). Anatomical Sciences Education. DOI: https://doi.org/10.1002/ase.2037
  • Harris, S., Samorani, M. (2021). On Selecting a Probabilistic Classifier for Appointment No-show Prediction. (vol. 124, pp.14). Decision Support Systems. DOI: https://doi.org/10.1016/j.dss.2020.113472
  • Harris, S., May, J. H., Vargas, L. G., Foster, K. M. (2020). The effect of cancelled appointments on outpatient clinic operations. (3 ed., vol. 284, pp.847-860). European Journal of Operational Research. DOI: https://doi.org/10.1016/j.ejor.2020.01.050
  • Goffman, R. M., Harris, S., May, J. H., Milicevic, A. S., Monte, R. J., Myaskovsky, L., Rodriguez, K. L., Tjader, Y. C., Vargas, D. L. (2017). Modeling patient no-show history and predicting future outpatient appointment behavior in the Veterans Health Administration. (5-6 ed., vol. 182, pp.e1708-e1714). Military medicine. DOI: https://doi.org/10.7205/MILMED-D-16-00345
  • Harris, S., May, J. H., Vargas., L. G. (2016). Predictive analytics model for healthcare planning and scheduling. (1 ed., vol. 253, pp.121-131). European Journal of Operational Research. DOI: http://dx.doi.org/10.1016/j.ejor.2016.02.017
  • Lin, C. S., Harris, S. (2013). A unified framework for the prioritization of organ transplant patients: analytic hierarchy process, sensitivity and multifactor robustness study. (3-4 ed., vol. 20, pp.157-172). Journal of Multi-Criteria Decision Analysis.
Media Contributions
Magazine
  • WIRED Magazine: A Health Care Algorithm Offered Less Care to Black Patients.
    A study shows the risks of making decisions using data that reflects inequities in American society.

    Link: https://www.wired.com/story/how-algorithm-favored-whites-over-blacks-health-care/. (October 24, 2019).
Internet
  • Healthcare IT News.
    Study: Scheduling systems lead to longer wait times for Black patients

    Link: https://www.healthcareitnews.com/news/study-scheduling-systems-lead-longer-wait-times-black-patients?mc_cid=3c707c61ca&mc_eid=799484f666. (August 27, 2021).
  • Forbes Online: Medical Scheduling Software Makes Black Patients Wait Longer In Waiting Rooms Than White Patients.

    Link: https://www.forbes.com/sites/traversmark/2019/12/03/medical-scheduling-software-makes-black-patients-wait-longer-in-waiting-rooms-than-white-patients/#52c28e4a559e. (December 3, 2019).