Professor Goh’s primary research interest is applying mathematical models to real-world problems in health care in order to inform, improve, and enhance medical decision making and health policy. His recent work in this domain focuses on developing new methods for active postmarketing drug surveillance, assessing the human and economic costs of workplace stressors, and comparing the cost-effectiveness of screening strategies for colorectal cancer.
His secondary interest is developing theory for optimal decision making under uncertainty. It supports his primary research agenda by supplying a fresh source of tools and ideas that enriches his models for health care applications. Within this area, he has investigated inventory control problems in supply chains and the theory and applications of distributionally robust optimization.
Joel Goh
Visiting Scholar
Visiting Scholar
Joel Goh is a visiting scholar in the Technology & Operations Management Unit.
Professor Goh develops mathematical models to provide insights into medical decision making and recommendations for health policy in areas including drug safety, workplace stress, and cost-effectiveness of new medical technology. He has also made methodological contributions in the field of operations research, specifically in robust optimization and supply chain management. Professor Goh is the co-creator of ROME (Robust Optimization Made Easy), a freely distributed software package for modeling robust optimization problems. His research has been published in Management Science and Operations Research.
Joel Goh is a visiting scholar in the Technology & Operations Management Unit.
Professor Goh develops mathematical models to provide insights into medical decision making and recommendations for health policy in areas including drug safety, workplace stress, and cost-effectiveness of new medical technology. He has also made methodological contributions in the field of operations research, specifically in robust optimization and supply chain management. Professor Goh is the co-creator of ROME (Robust Optimization Made Easy), a freely distributed software package for modeling robust optimization problems. His research has been published in Management Science and Operations Research.
Professor Goh holds a Ph.D. in Operations, Information, and Technology from the Stanford University Graduate School of Business. He also earned M.S. and B.S. degrees from Stanford in Electrical Engineering.
- Journal Articles
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- Ferreira, Kris J., and Joel Goh. "Assortment Rotation and the Value of Concealment." Management Science 67, no. 3 (March 2021): 1489–1507. View Details
- Han, Shasha, Tait D. Shanafelt, Christine A. Sinsky, Karim M. Awad, Liselotte N. Dyrbye, Lynne C. Fiscus, Mickey Trockel, and Joel Goh. "Estimating the Attributable Cost of Physician Burnout in the United States." Annals of Internal Medicine 170, no. 11 (June 4, 2019): 784–790. View Details
- Bastani, Hamsa, Joel Goh, and Mohsen Bayati. "Evidence of Upcoding in Pay-for-Performance Programs." Management Science 65, no. 3 (March 2019): 1042–1060. (2015 INFORMS Health Applications Society best student (H. Bastani) paper award.) View Details
- Shanafelt, Tait D., Joel Goh, and Christine A. Sinsky. "The Business Case for Investing in Physician Well-Being." JAMA Internal Medicine 177, no. 12 (December 2017): 1826–1832. (doi:10.1001/jamainternmed.2017.4340.) View Details
- Goh, Joel, Mohsen Bayati, Stefanos A. Zenios, Sundeep Singh, and David Moore. "Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations." Operations Research 66, no. 3 (May–June 2018): 697–715. (Winner, 2014 INFORMS Health Applications Society Pierskalla Award & Finalist, 2014 INFORMS George E. Nicholson student paper competition.) View Details
- Goh, Joel, and Evan L. Porteus. "Multi-Echelon Inventory Management Under Short-Term Take-or-Pay Contracts." Production and Operations Management 25, no. 8 (August 2016): 1415–1429. (Finalist for 2014 POMS College of Supply Chain Management Student Paper Award.) View Details
- Mankad, Shawn, Hyunjeong "Spring" Han, Joel Goh, and Srinagesh Gavirneni. "Understanding Online Hotel Reviews Through Automated Text Analysis." Service Science 8, no. 2 (June 2016): 124–138. View Details
- Goh, Joel, Jeffrey Pfeffer, and Stefanos A. Zenios. "The Relationship Between Workplace Stressors and Mortality and Health Costs in the United States." Management Science 62, no. 2 (February 2016): 608–628. View Details
- Goh, Joel, Jeffrey Pfeffer, and Stefanos A. Zenios. "Exposure to Harmful Workplace Practices Could Account for Inequality in Life Spans Across Different Demographic Groups." Health Affairs 34, no. 10 (October 2015): 1761–1768. View Details
- Goh, Joel, Jeffrey Pfeffer, and Stefanos A. Zenios. "Workplace Stressors & Health Outcomes: Health Policy for the Workplace." Behavioral Science & Policy 1, no. 1 (Spring 2015): 43–52. View Details
- Goh, Joel, Margrét V. Bjarnadóttir, Mohsen Bayati, and Stefanos A. Zenios. "Active Postmarketing Drug Surveillance for Multiple Adverse Events." Operations Research 63, no. 6 (November–December 2015): 1528–1546. (Finalist, 2012 INFORMS Health Applications Society Pierskalla Award.) View Details
- Goh, Joel, and Nicholas G. Hall. "Total Cost Control in Project Management via Satisficing." Management Science 59, no. 6 (June 2013): 1354–1372. View Details
- Goh, Joel, Kian Guan Lim, Melvyn Sim, and Weina Zhang. "Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns." European Journal of Operational Research 221, no. 2 (September 1, 2012): 397–406. View Details
- Goh, Joel, and Melvyn Sim. "Robust Optimization Made Easy with ROME." Operations Research 59, no. 4 (July–August 2011): 973–985. View Details
- Goh, Joel, and Melvyn Sim. "Distributionally Robust Optimization and Its Tractable Approximations." Operations Research 58, no. 4 (pt.1) (July–August 2010): 902–917. View Details
- Goh, Joel, Ilya Fushman, Dirk Englund, and Jelena Vuckovic. "Genetic Optimization of Photonic Bandgap Structures." Optics Express 15, no. 13 (June 25, 2007): 8218–8230. View Details
- Book Chapters
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- Goh, Joel. "Competing Interests." Chap. 4 in Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, edited by Tinglong Dai and Sridhar Tayur, 51–78. John Wiley & Sons, 2018. View Details
- Working Papers
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- Ferreira, Kris J., Joel Goh, and Ehsan Valavi. "Intermediation in the Supply of Agricultural Products in Developing Economies." Harvard Business School Working Paper, No. 18-033, October 2017. View Details
- Cases and Teaching Materials
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- Ferreira, Kris, Joel Goh, and Dawn H. Lau. "GHN and AhaMove: Last-Mile Delivery in Vietnam." Harvard Business School Teaching Note 622-010, September 2021. View Details
- Ferreira, Kris, Joel Goh, Dawn Lau, and Tuan Phan. "GHN and AhaMove: Last-Mile Delivery in Vietnam." Harvard Business School Case 619-051, June 2019. (Revised September 2021.) View Details
- Goh, Joel, Robert S. Huckman, and Nikhil Sahni. "University Hospitals Cleveland Medical Center: Managing Capacity in Neurology." Harvard Business School Case 618-062, March 2018. View Details
- Goh, Joel, and Ananth Raman. "Coca-Cola in Vietnam." Harvard Business School Case 616-063, June 2016. View Details
- Research Summary
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Active postmarketing drug surveillance. There is substantial interest within the U.S. health community and among health policymakers in developing a surveillance system that scans public health databases in order to proactively detect potential drug safety issues. Existing methods for such surveillance have limitations. Namely, they do not account for the dynamic relationships between multiple adverse events. Professor Goh has developed a new surveillance method that circumvents this limitation by capturing the dependence of adverse events. This method has shown promising results in preliminary tests on simulated data and is being validated using patient data from Medicare.
Assessing the human and economic costs of workplace stressors. While there is widespread consensus that a variety of workplace stressors have negative health consequences, studies to date have focused on individual stressors because of the technical and logistical challenges in investigating the aggregate health effects of workplace stress. To address this problem, Professor Goh has developed a model that relates 10 commonly studied stressors (including long hours, lack of control, and work-family conflict) to four commonly measured health outcomes. The analysis suggests that workplace stressors have a substantial impact on mortality and health care spending in the United States.
Comparing the cost-effectiveness of screening strategies for colorectal cancer. Colorectal cancer is a leading cause of cancer-related deaths in the United States, but it is largely preventable through early screening and intervention. Colonoscopy, the prevalent screening method, is highly effective but also expensive. A new method, fecal immunochemical testing (FIT), is cheaper but less accurate. Moreover, because FIT has not yet been widely adopted and tested, evidence of its efficacy is limited and exhibits large variability. Because existing methods for cost-effectiveness analyses do not fully account for the effect of uncertainty, policymakers are unable to rigorously assess the cost-effectiveness of FIT. In response to this issue, Professor Goh has developed a model for cost-effectiveness analysis that fully accounts for the inherent uncertainty of the analyses. The analysis rigorously identifies worst-case scenarios, and it shows that even in these cases, outcomes using FIT would be comparable to those using colonoscopy, but at a much lower cost.Inventory control problems in supply chains. In this stream of theoretical research, Professor Goh has investigated how inventory should be optimally managed in supply chains. Specifically, he has studied how supply chains can make decisions to operate optimally when they experience random demand over multiple time periods, and when supply chain members have added options to make short-term order commitments in advance of their actual orders.
Theory and applications of distributionally robust optimization. Distributionally robust optimization is a modeling paradigm for decision making under uncertainty where optimal decisions or decision rules (for sequential problems) are sought for the worst-case distribution of uncertainty within a family. This modeling paradigm has the advantage that it often entails only modest data and computational requirements, even for complex problems. Professor Goh has studied how flexible nonlinear decision rules can be used for decision making within this paradigm, and has applied these ideas to the management of projects and financial portfolios. He has also created a freely distributed software package, ROME (Robust Optimization Made Easy), for modeling and solving such problems.
- Teaching
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This course enables students to develop the skills and concepts needed to ensure the ongoing contribution of a firm's operations to its competitive position. It helps them to understand the complex processes underlying the development and manufacture of products as well as the creation and delivery of services.
Topics encompass:
- Process analysis
- Cross-functional and cross-firm integration
- Product development
- Information technology
- Technology and operations strategy
- Awards & Honors
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Finalist for the 2014 POMS College of Supply Chain Management Student Paper Award for "Multi-Echelon Inventory Management under Short-Term Take-or-Pay Contracts" with Evan L. Porteus (forthcoming in Production and Operations Management).Finalist for 2012 Pierskalla Award from INFORMS Health Applications Society for "Active Postmarketing Drug Surveillance for Multiple Adverse Events" with Margrét V. Bjarnadóttir, Mohsen Bayati, and Stefanos A. Zenios (Operations Research, November–December 2015).
- Areas of Interest
- In The News
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