About

Noah Gans is the Anheuser-Busch Professor of Management Science at Wharton and a Professor of Operations, Information and Decisions. His research primarily focuses on service operations, stochastic processes, and the management of queueing systems, with a particular interest in telephone call centers. Gans has held significant editorial roles, including Department Editor of Stochastic Models and Simulation at Management Science, and has served as President of the Manufacturing and Service Operations Management Society (MSOM). At Wharton, he coordinates the PhD Program within the OID Department and teaches core courses on Business Analytics for MBA students, along with elective courses on Analytics for Services and Revenue Management.

Research topics

• Computer Science
• Economics
• Mathematics
• Artificial Intelligence
• Business
• Machine Learning
• Actuarial science
• Statistics
• Medicine
• Nursing

Selected publications

• Conditional Approval and Value-Based Pricing for New Health Technologies

  Management Science · 2024 · 4 citations

  Senior authorCorresponding
  • Computer Science
  • Economics
  • Business

  Health technology assessments often inform decisions made by public payers, such as the UK’s National Health Service, as they negotiate the pricing of companies’ new health technologies. A common assessment mechanism compares the incremental cost-effectiveness ratio (ICER) of the new health technology, relative to a standard of care, to a maximum threshold on the cost per quality-adjusted life year. In much research and practice, these assessments may not distinguish between cost-per-patient and negotiated price, effectively ignoring the value-based-pricing principle that better health outcomes merit higher prices. Other research makes this distinction, but it does not account for uncertainty in the ICER associated with clinical trial data that are limited in size and scope. This paper models the strategic behavior of a payer and a company as they price a new health technology, and it considers the use of conditional approval (CA) schemes whose post-marketing trials reduce ICER uncertainty before final pricing decisions are made. Analytical results suggest a very different view of the value-based pricing negotiations underlying these schemes: interim prices used during CA post-marketing trials should reflect cost-sharing for the CA scheme, not just cost-effectiveness goals for a treatment. Moreover, the types of caps on interim prices used by entities such as the UK Cancer Drugs Fund may hinder the development of new technologies and lead to suboptimal CA designs. We propose a new risk-sharing mechanism to remedy this. Numerical results, calibrated to approval data of an oncology drug, illustrate the issues in a practical setting. This paper was accepted by Stefan Scholtes, healthcare management. Funding: Financial support from the Mack Institute for Innovation Management at the Wharton School to the authors and the support of Dr. Simba Gill and Sabi Dau to the INSEAD Healthcare Management Initiative are gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.03628 .

  PublisherDOI

• Conditional Approval and Value-Based Pricing for New Health Technologies

  SSRN Electronic Journal · 2022

  Senior authorCorresponding
  • Business
  • Actuarial science
  • Economics
  PublisherDOI

• Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions

  Management Science · 2021 · 29 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  We propose and analyze the first model for clinical trial design that integrates each of three important trends intending to improve the effectiveness of clinical trials that inform health-technology adoption decisions: adaptive design, which dynamically adjusts the sample size and allocation of interventions to different patients; multiarm trial design, which compares multiple interventions simultaneously; and value-based design, which focuses on cost-benefit improvements of health interventions over a current standard of care. Example applications are to seamless phase II/III dose-finding trials and to trials that test multiple combinations of therapies. Our objective is to maximize the expected population health-economic benefit of health-technology adoption decisions less clinical trial costs. We show that unifying the adaptive, multiarm, and value-based approaches to trial design can reduce the cost and duration of multiarm trials with efficient adaptive look ahead policies that focus on value to patients and account for correlated rewards across arms. Features that differentiate our approach from much other work on stochastic optimization include stopping times that balance sampling costs and the expected value of information of those samples, performance guarantees offered by new asymptotic convergence proofs, and the modeling of arms’ potentially different sampling costs. Our proposed solution can be computed feasibly and can randomize patients. The class of trials for the base model assumes that health-economic data are collected and observed quickly. Related work from Bayesian optimization can enable the further inclusion of trials with intermediate duration delays between the time of treatment initiation and observation of outcomes. This paper was accepted by Stefan Scholtes, healthcare management.

  PublisherDOI

• Sequential, Value-Based Designs for Certain Clinical Trials with Multiple Arms Having Correlated Rewards

  2019-12-01 · 2 citations

  article

  The time and money required to run clinical trials, as well as the cost effectiveness of technologies emerging from these trials are receiving increasing scrutiny. This paper explores the use of techniques inspired from fully sequential simulation optimization, based on Bayesian expected value of sampling information arguments, in the context of highly-sequential multi-arm trials. New allocation rules are shown to be useful for selecting which technology to assign a patient in a trial. They are based on clinical cost-benefit tradeoffs and the size of the population who benefits from the technology adoption decision.

  PublisherDOI

• Overbooking with Endogenous Demand

  SSRN Electronic Journal · 2019-01-01 · 6 citations

  articleOpen access
  PublisherDOI

• Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions

  SSRN Electronic Journal · 2018-01-01 · 15 citations

  articleOpen access
  PublisherDOI

• Proposal for fully sequential multiarm trials with correlated arms

  Winter Simulation Conference · 2016-12-11 · 2 citations

  article

  We focus on the design of multiarm multistage (MAMS) clinical trials, using ideas from simulation optimization, biostatistics, and health economics. From a trial design perspective, we build on the trend of comparing multiple treatments with a single control by allowing for more than two arms in a trial, and we allow for arbitrarily many stages of sampling by using a diffusion approximation that allows for adaptive stopping rules. From a simulation perspective, our techniques extend the correlated knowledge-gradient concept, which has been used in one-stage lookahead (knowledge gradient) procedures, to Bayesian fully sequential selection procedures.

  PublisherDOI

• Proposal for fully sequential multiarm trials with correlated arms

  2016 Winter Simulation Conference (WSC) · 2016-12-01

  article

  We focus on the design of multiarm multistage (MAMS) clinical trials, using ideas from simulation optimization, biostatistics, and health economics. From a trial design perspective, we build on the trend of comparing multiple treatments with a single control by allowing for more than two arms in a trial, and we allow for arbitrarily many stages of sampling by using a diffusion approximation that allows for adaptive stopping rules. From a simulation perspective, our techniques extend the correlated knowledge-gradient concept, which has been used in one-stage lookahead (knowledge gradient) procedures, to Bayesian fully sequential selection procedures.

  PublisherDOI

• Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

  Manufacturing & Service Operations Management · 2015-07-17 · 63 citations

  article1st authorCorresponding

  We develop and test an integrated forecasting and stochastic programming approach to workforce management in call centers. We first demonstrate that parametric forecasts, discretized using Gaussian quadrature, can be used to drive stochastic programs whose results are stable with relatively small numbers of scenarios. We then extend our approach to include forecast updates and two-stage stochastic programs with recourse and provide a general modeling framework for which recent, related models are special cases. In our formulations, the inclusion of multiple arrival-rate scenarios allows call centers to meet long-run average quality-of-service targets, and the use of recourse actions helps them to lower long-run average costs. Experiments with two large sets of call-center data highlight the complementary nature of these elements.

  PublisherDOI

• Introduction to the Special Issue on Business Analytics

  Management Science · 2014-06-01 · 13 citations

  article
  PublisherDOI

Frequent coauthors

• Stephen E. Chick

  INSEAD

  15 shared

• Özge Yapar

  Health Decision Technologies (United States)

  8 shared

• Yong‐Pin Zhou

  University of Washington

  7 shared

• Haipeng Shen

  5 shared

• Morris A. Cohen

  4 shared

• Garrett van Ryzin

  Amazon (United States)

  4 shared

• Alessandro Arlotto

  Duke University

  4 shared

• Nitin Bakshi

  4 shared

Awards & honors

• Manufacturing and Service Operations Management Society (MSO…