Joel Goh - Faculty & Research - Harvard Business School
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Joel Goh

Visiting Scholar

Technology and Operations Management

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
  1. Evidence of Upcoding in Pay-for-Performance Programs

    Hamsa Bastani, Joel Goh and Mohsen Bayati

    Recent Medicare legislation seeks to improve patient care quality by financially penalizing providers for hospital-acquired infections (HAIs). However, Medicare cannot directly monitor HAI rates and instead relies on providers accurately self-reporting HAIs in claims to correctly assess penalties. Consequently, the incentives for providers to improve service quality may disappear if providers upcode, i.e., misreport HAIs (possibly unintentionally) in a manner that increases reimbursement or avoids financial penalties. Identifying upcoding in claims data is challenging due to unobservable confounders (e.g., patient risk). We leverage state-level variations in adverse event reporting regulations and instrumental variables to discover contradictions in HAI and present-on-admission (POA) infection reporting rates that are strongly suggestive of upcoding. We conservatively estimate that 10,000 out of 60,000 annual reimbursed claims for POA infections (18.5%) were upcoded HAIs, costing Medicare $200 million. Our findings suggest that self-reported quality metrics are unreliable and thus, recent legislation may result in unintended consequences.

    Keywords: Medical Coding; Health Policy; healthcare-acquired conditions; Medicare; Health Care and Treatment; Policy; Performance Improvement; Quality; Measurement and Metrics; Government Legislation;


    Bastani, Hamsa, Joel Goh, and Mohsen Bayati. "Evidence of Upcoding in Pay-for-Performance Programs." Management Science (forthcoming). (2015 INFORMS Health Applications Society best student (H. Bastani) paper award.)  View Details
  2. The Business Case for Investing in Physician Well-Being

    Tait D. Shanafelt, Joel Goh and Christine A. Sinsky

    Importance: Widespread burnout among physicians has been recognized for more than two decades. Extensive evidence indicates that physician burnout has important personal and professional consequences.
    Observations: A lack of awareness regarding the economic costs of physician burnout and uncertainty regarding what organizations can do to address the problem have been barriers to many organizations taking action. Although there is a strong moral and ethical case for organizations to address physician burnout, financial principles (e.g., return on investment) can also be applied to determine the economic cost of burnout and guide appropriate investment to address the problem. The business case to address physician burnout is multifaceted and includes costs associated with turnover, lost revenue associated with decreased productivity, as well as financial risk and threats to the organization’s long-term viability due to the relationship between burnout and lower quality of care, decreased patient satisfaction, and problems with patient safety. Nearly all U.S. health care organizations have used similar evidence to justify their investments in safety and quality. Herein, we provide conservative formulas based on readily available organizational characteristics to determine the financial return on organizational investments to reduce physician burnout. A model outlining the steps of the typical organization’s journey to address this issue is presented. Critical ingredients to making progress include prioritization by leadership, physician involvement, organizational science/learning, metrics, structured interventions, open communication, and promoting culture change at the work unit, leader, and organization level.
    Conclusions and Relevance: Understanding the business case to reduce burnout and promote engagement as well as overcoming the misperception that nothing meaningful can be done are key steps for organizations to begin to take action. Evidence suggests that improvement is possible, investment is justified, and return on investment measurable. Addressing this issue is not only the organization’s ethical responsibility, it is also the fiscally responsible one.

    Keywords: Physicians; well-being; ROI; Health; Welfare or Wellbeing; Ethics; Investment Return; Health Industry;


    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
  3. Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations

    Joel Goh, Mohsen Bayati, Stefanos A. Zenios, Sundeep Singh and David Moore

    Cost-effectiveness studies of medical innovations often suffer from data inadequacy. When Markov chains are used as a modeling framework for such studies, this data inadequacy can manifest itself as imprecision in the elements of the transition matrix. In this paper, we study how to compute maximal and minimal values for the discounted value of the chain (with respect to a vector of state-wise costs or rewards) as these uncertain transition parameters jointly vary within a given uncertainty set. We show that these problems are computationally tractable if the uncertainty set has a row-wise structure. Conversely, we prove that the row-wise structure is necessary for tractability. Without it, the problems become computationally intractable (strongly NP-hard). We apply our model to assess the cost effectiveness of fecal immunochemical testing (FIT), a new screening method for colorectal cancer. Our results show that despite the large uncertainty in FIT's performance, it could be cost-effective relative to the prevailing screening method of colonoscopy.

    Keywords: Markov Chains; cost effectiveness; medical innovations; Colorectal Cancer; Health Care and Treatment; Cost vs Benefits; Innovation and Invention; Mathematical Methods; Health Industry;


    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
  4. Multi-Echelon Inventory Management Under Short-Term Take-or-Pay Contracts

    Joel Goh and Evan L. Porteus

    We extend the Clark–Scarf serial multi-echelon inventory model to include procuring production inputs under short-term take-or-pay contracts at one or more stages. In each period, each such stage has the option to order/process at two different cost rates; the cheaper rate applies to units up to the contract quantity selected in the previous period. We prove that in each period and at each such stage, there are three base-stock levels that characterize an optimal policy, two for the inventory policy and one for the contract quantity selection policy. The optimal cost function is additively separable in its state variables, leading to conquering the curse of dimensionality and the opportunity to manage the supply chain using independently acting managers. We develop conditions under which myopic policies are optimal and illustrate the results using numerical examples. We establish and use a generic one-period result, which generalizes an important such result in the literature. Extensions to cover variants of take-or-pay contracts are included. Limitations are discussed.

    Keywords: Inventory management; multi-echelon inventory theory; Karush Lemma; Clark-Scarf model; convex ordering cost; Advance Commitments; Supply Chain;


    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
  5. Understanding Online Hotel Reviews Through Automated Text Analysis

    Shawn Mankad, Hyunjeong "Spring" Han, Joel Goh and Srinagesh Gavirneni

    Customer reviews submitted at Internet travel portals are an important yet underexplored new resource in obtaining feedback on customer experience for the hospitality industry. These data are often voluminous and unstructured, presenting analytical challenges for traditional tools that were designed for well-structured, quantitative data. We adapt methods from natural language processing and machine learning to illustrate how the hotel industry can leverage this new data source by performing automated evaluation of the quality of writing, sentiment estimation, and topic extraction. By analyzing 5,830 reviews from 57 hotels in Moscow, Russia, we find that (i) negative reviews tend to focus on a small number of topics, whereas positive reviews tend to touch on a greater number of topics; (ii) negative sentiment inherent in a review has a larger downward impact than corresponding positive sentiment; and (iii) negative reviews contain a larger variation in sentiment on average than positive reviews. These insights can be instrumental in helping hotels achieve their strategic, financial, and operational objectives.

    Keywords: hotel reviews; natural language processing; service operations; Information Technology; Service Operations; Accommodations Industry; Moscow;


    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
  6. The Relationship Between Workplace Stressors and Mortality and Health Costs in the United States

    Joel Goh, Jeffrey Pfeffer and Stefanos A. Zenios

    Even though epidemiological evidence links specific workplace stressors to health outcomes, the aggregate contribution of these factors to overall mortality and health spending in the United States is not known. In this paper, we build a model to estimate the excess mortality and incremental health expenditures associated with exposure to the following 10 workplace stressors: unemployment, lack of health insurance, exposure to shift work, long work hours, job insecurity, work–family conflict, low job control, high job demands, low social support at work, and low organizational justice. Our model uses input parameters obtained from publicly accessible data sources. We estimated health spending from the Medical Expenditure Panel Survey and joint probabilities of workplace exposures from the General Social Survey, and we conducted a meta-analysis of the epidemiological literature to estimate the relative risks of poor health outcomes associated with exposure to these stressors. The model was designed to overcome limitations with using inputs from multiple data sources. Specifically, the model separately derives optimistic and conservative estimates of the effect of multiple workplace exposures on health and uses optimization to calculate upper and lower bounds around each estimate, which accounts for the correlation between exposures. We find that more than 120,000 deaths per year and approximately 5%–8% of annual healthcare costs are associated with and may be attributable to how U.S. companies manage their work forces. Our results suggest that more attention should be paid to management practices as important contributors to health outcomes and costs in the United States.

    Keywords: occupational health; health costs; mortality; applied optimization; Health;


    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
  7. Exposure to Harmful Workplace Practices Could Account for Inequality in Life Spans Across Different Demographic Groups

    Joel Goh, Jeffrey Pfeffer and Stefanos A. Zenios

    The existence of important socioeconomic disparities in health and mortality is a well-established fact. Many pathways have been adduced to explain inequality in life spans. In this article we examine one factor that has been somewhat neglected: people with different levels of education get sorted into jobs with different degrees of exposure to workplace attributes that contribute to poor health. We used General Social Survey data to estimate differential exposures to workplace conditions, results from a meta-analysis that estimated the effect of workplace conditions on mortality, and a model that permitted us to estimate the overall effects of workplace practices on health. We conclude that 10%–38% of the difference in life expectancy across demographic groups can be explained by the different job conditions their members experience.

    Keywords: occupational health; inequality; life expectancy; socioeconomic issues; Health;


    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
  8. Workplace Stressors & Health Outcomes: Health Policy for the Workplace

    Joel Goh, Jeffrey Pfeffer and Stefanos A. Zenios

    Extensive research focuses on the causes of workplace-induced stress. However, policy efforts to tackle the ever-increasing health costs and poor health outcomes in the United States have largely ignored the health effects of psychosocial workplace stressors such as high job demands, economic insecurity, and long work hours. Using meta-analysis, we summarize 228 studies assessing the effects of ten workplace stressors on four health outcomes. We find that job insecurity increases the odds of reporting poor health by about 50%, high job demands raise the odds of having a physician-diagnosed illness by 35%, and long work hours increase mortality by almost 20%. Therefore, policies designed to reduce health costs and improve health outcomes should account for the health effects of the workplace environment.

    Keywords: occupational health; mortality; stress; Meta-analysis; Health;


    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
  9. Active Postmarketing Drug Surveillance for Multiple Adverse Events

    Joel Goh, Margrét V. Bjarnadóttir, Mohsen Bayati and Stefanos A. Zenios

    Postmarketing drug surveillance is the process of monitoring the adverse events of pharmaceutical or medical devices after they are approved by the appropriate regulatory authorities. Historically, such surveillance was based on voluntary reports by medical practitioners, but with the widespread adoption of electronic medical records and comprehensive patient databases, surveillance systems that utilize such data are of considerable interest. Unfortunately, existing methods for analyzing the data in such systems ignore the open-ended exploratory nature of such systems that requires the assessment of multiple possible adverse events. In this article, we propose a method, SEQMEDS, that assesses the effect of a single drug on multiple adverse events by analyzing data that accumulate sequentially and explicitly captures interdependencies among the multiple events. The method continuously monitors a vector-valued test-statistic derived from the cumulative number of adverse events. It flags a potential adverse event once the test-statistic crosses a stopping boundary. We employ asymptotic analysis that assumes a large number of observations in a given window of time to show how to compute the stopping boundary by solving a convex optimization problem that achieves a desired Type I error and minimizes the expected time to detection under a pre-specified alternative hypothesis. We apply our method to a model in which the interdependency among the multiple adverse events is captured by a Cox proportional hazards model with time-dependent covariates and demonstrate that it provides an approximation of a fully sequential test for the maximum hazard ratio of the drug over multiple adverse events. A numerical study verifies that our method delivers Type I /II errors that are below pre-specified levels and is robust to distributional assumptions and parameter values.

    Keywords: drug surveillance; health care; stochastic models; queueing; diffusion approximation; Brownian motion; Health Care and Treatment; Data and Data Sets; Analysis;


    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
  10. Total Cost Control in Project Management via Satisficing

    Joel Goh and Nicholas G. Hall

    We consider projects with uncertain activity times and the possibility of expediting, or crashing, them. Activity times come from a partially specified distribution within a family of distributions. This family is described by one or more of the following details about the uncertainties: support, mean, and covariance. We allow correlation between past and future activity time performance across activities. Our objective considers total completion time penalty plus crashing and overhead costs. We develop a robust optimization model that uses a conditional value-at-risk satisficing measure. We develop linear and piecewise-linear decision rules for activity start time and crashing decisions. These rules are designed to perform robustly against all possible scenarios of activity time uncertainty, when implemented in either static or rolling horizon mode. We compare our procedures against the previously available Program Evaluation and Review Technique and Monte Carlo simulation procedures. Our computational studies show that, relative to previous approaches, our crashing policies provide both a higher level of performance, i.e., higher success rates and lower budget overruns, and substantial robustness to activity time distributions. The relative advantages and information requirements of the static and rolling horizon implementations are discussed.

    Keywords: project management; time and cost control; robust optimization; satisficing; linear decision rule; PERT; Management; Cost Management; Projects;


    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
  11. Portfolio Value-at-Risk Optimization for Asymmetrically Distributed Asset Returns

    Joel Goh, Kian Guan Lim, Melvyn Sim and Weina Zhang

    We propose a new approach to portfolio optimization by separating asset return distributions into positive and negative half-spaces. The approach minimizes a newly-defined Partitioned Value-at-Risk (PVaR) risk measure by using half-space statistical information. Using simulated data, the PVaR approach always generates better risk-return tradeoffs in the optimal portfolios when compared to traditional Markowitz mean–variance approach. When using real financial data, our approach also outperforms the Markowitz approach in the risk-return tradeoff. Given that the PVaR measure is also a robust risk measure, our new approach can be very useful for optimal portfolio allocations when asset return distributions are asymmetrical.

    Keywords: robust optimization; portfolio management; value-at-risk; Mathematical Methods; Finance;


    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
  12. Robust Optimization Made Easy with ROME

    Joel Goh and Melvyn Sim

    We introduce ROME, an algebraic modeling toolbox for a class of robust optimization problems. ROME serves as an intermediate layer between the modeler and optimization solver engines, allowing modelers to express robust optimization problems in a mathematically meaningful way. In this paper, we discuss how ROME can be used to model (1) a service-constrained robust inventory management problem, (2) a project-crashing problem, and (3) a robust portfolio optimization problem. Through these modeling examples, we highlight the key features of ROME that allow it to expedite the modeling and subsequent numerical analysis of robust optimization problems. ROME is freely distributed for academic use at

    Keywords: robust optimization; algebraic modeling toolbox; MATLAB; stochastic programming; decision rules; Inventory control; PERT; project management; portfolio optimization; Technology; Mathematical Methods; Operations;


    Goh, Joel, and Melvyn Sim. "Robust Optimization Made Easy with ROME." Operations Research 59, no. 4 (July–August 2011): 973–985.  View Details
  13. Distributionally Robust Optimization and Its Tractable Approximations

    Joel Goh and Melvyn Sim

    In this paper we focus on a linear optimization problem with uncertainties, having expectations in the objective and in the set of constraints. We present a modular framework to obtain an approximate solution to the problem that is distributionally robust and more flexible than the standard technique of using linear rules. Our framework begins by first affinely extending the set of primitive uncertainties to generate new linear decision rules of larger dimensions and is therefore more flexible. Next, we develop new piecewise-linear decision rules that allow a more flexible reformulation of the original problem. The reformulated problem will generally contain terms with expectations on the positive parts of the recourse variables. Finally, we convert the uncertain linear program into a deterministic convex program by constructing distributionally robust bounds on these expectations. These bounds are constructed by first using different pieces of information on the distribution of the underlying uncertainties to develop separate bounds and next integrating them into a combined bound that is better than each of the individual bounds.

    Keywords: Technology; Mathematical Methods; Operations;


    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
Working Papers
  1. Intermediation in the Supply of Agricultural Products in Developing Economies

    Kris Johnson Ferreira, Joel Goh and Ehsan Valavi

    Problem Definition: Farmers face several challenges in agricultural supply chains in emerging economies that contribute to extreme levels of poverty. One common challenge is that farmers only have access to one channel, often an auction, for which to sell their crops. Recently, e-intermediaries have emerged as alternate, technology-driven posted-price channels. We aim to develop insights into the structural drivers of farmer and supply chain profitability in emerging markets and understand the impact of e-intermediaries.
    Academic / Practical Relevance: In practice, much attention has been given to e-intermediaries and they have often been touted as for-profit social enterprises that improve farmers' welfare. Yet, studies in the operations literature that systematically analyze the impact of e-intermediaries are lacking. Our work fills this gap and answers practical questions regarding the responsible operations of e-intermediaries.
    Methodology: We develop an analytical model of a supply chain that allows us to study several key features of intermediated supply chains. We complement the model's insights with observations from a numerical study.
    Results: In the absence of an e-intermediary, auctions cause farmers to either overproduce or underproduce compared to their ideal production levels in a vertically integrated chain. The presence of an e-intermediary with limited market share improves farmers' profits; however, if the e-intermediary grows too large, it negatively impacts both farmers' and supply chain profits. Finally, as the number of farmers increases, farmers' profits approach zero, irrespective of the e-intermediary's presence.
    Managerial Implications: Our results provide a balanced perspective on the value of e-intermediation, compared to the generally positive views advanced by case studies. For-profit e-intermediaries that also aim to improve farmers' livelihoods cannot blindly operate as pure profit-maximizers, assuming that market forces alone will ensure that farmers benefit. Even when e-intermediation benefits farmers, it is insufficient to mitigate the negative effects of supply fragmentation, suggesting that for farmers, market power is more important than market access.

    Keywords: developing countries; agricultural supply chain; Intermediation; multiple cahnels; Walrasian auction; Developing Countries and Economies; Supply Chain; Distribution Channels; Profit; Agriculture and Agribusiness Industry;


    Ferreira, Kris Johnson, 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
  2. Assortment Rotation and the Value of Concealment

    Kris Johnson Ferreira and Joel Goh

    Assortment rotation—the retailing practice of changing the assortment of products offered to customers—has recently been used as a competitive advantage for both brick-and-mortar and online retailers. Fast-fashion retailers have differentiated themselves by rotating their assortment multiple times throughout a standard selling season. Interestingly, the entire online flash sales industry was created using this idea as a cornerstone of its business strategy. In this paper, we identify and investigate a new reason why frequent assortment rotations can be valuable to a retailer, particularly for products where consumers typically purchase multiple products in a given category during a selling season. Namely, by distributing its seasonal catalog of products over multiple assortments rotated throughout the season—as opposed to selling all products in a single assortment—the retailer effectively conceals a portion of its full product catalog from consumers. This injects uncertainty into the consumer's relative product valuations since she is unable to observe the entire catalog of products that the retailer will sell that season. Rationally acting consumers may respond to this additional uncertainty by purchasing more products, thereby generating additional sales for the retailer. We refer to this phenomenon as the value of concealment. A negative value of concealment is possible and represents the event that rationally acting consumers respond to the additional uncertainty by purchasing fewer products. We develop a consumer choice model and finite-horizon stochastic dynamic program to study when the value of concealment is positive or negative. We show that when consumers are myopic, the value of concealment is always positive. In contrast, we show that when consumers are strategic, the value of concealment is context dependent; we present insights and discuss intuition regarding which product categories likely lead to positive vs. negative values of concealment.

    Keywords: assortment planning; strategic consumers; consumer choice; Strategy; Consumer Behavior; Operations; Sales; Retail Industry;


    Ferreira, Kris Johnson, and Joel Goh. "Assortment Rotation and the Value of Concealment." Harvard Business School Working Paper, No. 17-041, November 2016. (Revised January 2018.)  View Details
Cases and Teaching Materials
  1. University Hospitals Cleveland Medical Center: Managing Capacity in Neurology

    Joel Goh, Robert S. Huckman and Nikhil Sahni

    In December 2014, Dr. Anthony Furlan, chair of the Department of Neurology at University Hospitals Cleveland Medical Center (UH), faced a mandate from the hospital’s executive leadership team. Specifically, all UH departments were directed to take steps within six months to reduce the waiting time for outpatient appointments—measured as the time to first available outpatient appointment—to no more than 15 days. For Furlan and his colleagues in neurology, achieving this target was a significant challenge, as the department’s current time to first available appointment was 93 days. The case considers several alternatives for reducing waiting time in outpatient neurology without increasing the total clinical staff. The case allows students to evaluate opportunities for expanding the effective capacity of a complex service operation and to understand the tradeoffs between customer service and labor utilization.

    Keywords: health care; Hospitals; capacity planning; scheduling; Health Care and Treatment; Service Operations; Performance Capacity; Health Industry; North America; United States; Ohio; Cleveland;


    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