PhD Candidate
Associate
Operations and Technology Management
BSc (Universität Jena), MSc (LSE), MRes (University of Cambridge)
Year of entry: 2018
My doctoral training has centred around empirical healthcare operations, with an emphasis on econometric methods and their applications to public health. Health inequalities are a focus of my research, and I study data-driven interventions that aim at addressing them. My research is shaped by the insights I have gained from being a part of the NHS East of England public health team for the past four years. I am also a Research Fellow at London Business School, and teach their Business Analytics course to masters students.
Publications and papers
Selected publications and papers
- Pape, T., Savva, N., Scholtes, S., Kastner, C. and Horder, K. (2024) “Waiting for surgery: accounting for the social determinants of health in service rationing processes.” Working paper (submitted to Management Science)
- Pape, T., Kavadias, S. and Sommer, S. (2024) “Decision bias in project selection: experimental evidence from the knapsack problem.” Working paper (re-submitted for second-round review to Management Science)
- Betcheva, L., Erhun, F., Feylessoufi, A., Fryers, P., Goncalves, P., Jiang, H., Kattuman, P., Pape, T., Pari, A., Scholtes, S. and Tyrrell, C. (2024) “An adaptive research approach to COVID-19 forecasting for regional health systems in England.” INFORMS Journal on Applied Analytics (DOI: 10.1287/inte.2023.0009) (published online Apr 2024)
- Pape, T. (2017) “Value of agreement in decision analysis: concept, measures and application.” Computers and Operations Research, 80: 82-93 (DOI: 10.1016/j.cor.2016.11.018)
- Pape, T. (2016) “Prioritising data items for business analytics: framework and application to human resources.” European Journal of Operational Research, 252(2): 687-698 (DOI: 10.1016/j.ejor.2016.01.052)
- Pape, T. (2015) “Heuristics and lower bounds for the simple assembly line balancing problem type 1: overview, computational tests and improvements.” European Journal of Operational Research, 240(1): 32-42 (DOI: 10.1016/j.ejor.2014.06.023)
Journal articles
- Betcheva, L., Erhun, F., Feylessoufi, A., Fryers, P., Goncalves, P., Jiang, H., Kattuman, P., Pape, T., Pari, A., Scholtes, S. and Tyrrell, C. (2024) “An adaptive research approach to COVID-19 forecasting for regional health systems in England.” INFORMS Journal on Applied Analytics (DOI: 10.1287/inte.2023.0009) (published online Apr 2024)
- Vindrola-Padros, C., Pape, T., Utley, M. and Fulop, N.J. (2017) “The role of embedded research in quality improvement: a narrative review.” BMJ Quality and Safety, 26(1): 70-80 (DOI: 10.1136/bmjqs-2015-004877)
- Pape, T. (2017) “Value of agreement in decision analysis: concept, measures and application.” Computers and Operations Research, 80: 82-93 (DOI: 10.1016/j.cor.2016.11.018)
- Pape, T. (2016) “Prioritising data items for business analytics: framework and application to human resources.” European Journal of Operational Research, 252(2): 687-698 (DOI: 10.1016/j.ejor.2016.01.052)
- Pape, T. (2015) “Heuristics and lower bounds for the simple assembly line balancing problem type 1: overview, computational tests and improvements.” European Journal of Operational Research, 240(1): 32-42 (DOI: 10.1016/j.ejor.2014.06.023)
Working papers
- Pape, T., Savva, N., Scholtes, S., Kastner, C. and Horder, K. (2024) “Waiting for surgery: accounting for the social determinants of health in service rationing processes.” Working paper (submitted to Management Science)
- Pape, T., Kavadias, S. and Sommer, S. (2024) “Decision bias in project selection: experimental evidence from the knapsack problem.” Working paper (re-submitted for second-round review to Management Science)
- Pape, T., Rixson, L. and Pari, A. (2024) “Inequity in treatment access for child mental health services in England: an analysis of administrative national data for 2021/22.” Working paper (re-submitted for second-round review to British Journal of Psychiatry Bulletin)
Research interests
Tom’s current projects seek to identify operations management solutions to reduce healthcare inequities (with NHS East of England) and try to understand what is a good size for a primary care practice to be operationally efficient (with the Health Foundation). To answer these topical questions, Tom applies causal statistical methods to routinely collected patient-level data.
Pathway
Operations and Technology Management