Matthew Grennan is an Assistant Professor of Health Care Management at The Wharton School of the University of Pennsylvania where he currently teaches an undergraduate course in health care entrepreneurship and MBA courses on management & strategy in the medical device sector and health care data & analytics.
Professor Grennan holds a PhD in Strategy/Economics from New York University’s Stern School of Business. His research in industrial organization economics addresses important public policy and competitive strategy issues in healthcare markets, with a focus on negotiated pricing, innovation, regulation, and adoption of new technologies. His research has been funded by the National Science Foundation and published in leading journals such as the American Economic Review, the Journal of Political Economy, and Management Science. He is a member of the Academy of Management, American Economics Association, and Econometric Society; and an affiliate of the National Bureau of Economic Research, Leonard Davis Institute for Health Economic Research, and Strategy Research Forum.
Professor Grennan’s research explores questions in economic policy and firm strategy, with empirical applications in the health care context. His focus is on developing new, better data sources—and when necessary new empirical and theoretical models—with two major streams: (1) the interaction between negotiated prices and competition in business-to-business markets and (2) how regulatory and competitive forces shape innovation and market outcomes. Much of his recent work looks more closely at how information (or lack thereof) available to market participants affects these policy and strategy decisions.
Matthew Grennan and Robert J. Town (2019), Regulating Innovation with Uncertain Quality: Information, Risk, and Access in Medical Devices,.
Abstract: We study the impact of regulating product entry and quality information requirements on an oligopoly equilibrium and consumer welfare. Requiring product testing can reduce consumer uncertainty, but it also increases fixed costs of entry and time to market. Using variation between EU and US medical device regulations, we document patterns consistent with valuable learning from more stringent US requirements. To derive welfare implications, we pair the data with a model of supply, demand, and testing regulation. US policy is indistinguishable from the policy that maximizes total surplus in our estimated model, while the EU could benefit from more pre-market testing. "Post-market surveillance'' could further increase surplus.
Matthew Grennan, Kyle Myers, Ashley Swanson, Aaron Chatterji (Working), Physician-industry Interactions: Persuasion and Welfare.
Abstract: In markets where consumers seek expert advice regarding purchases, firms seek to influence experts, raising concerns about biased advice. Assessing firm-expert interactions requires identifying their causal impact on demand, amidst frictions like market power. We study pharmaceutical firms' payments to physicians, leveraging instrumental variables based on regional spillovers from hospitals' conflict-of-interest policies and market shocks due to patent expiration. We find that the average payment increases prescribing of the focal drug by 73 percent. Our structural model estimates indicate that payments decrease total surplus, unless payments are sufficiently correlated with information (vs. persuasion) or clinical gains not captured in demand.
Matthew Grennan and Ashley Swanson (Forthcoming), Transparency and Negotiated Prices: The Value of Information in Hospital-Supplier Bargaining.
Abstract: Using a detailed dataset of hospitals’ purchase orders, we find that information on purchasing by peer hospitals leads to reductions in the prices hospitals negotiate for supplies. Identification is based on staggered access to information across hospitals over time. Within coronary stents, reductions are concentrated among hospitals previously paying relatively high prices and for brands purchased in large volumes, and are consistent with resolving asymmetric information problems. Estimates across a large number of other important product categories indicate that the effects of information are largest in both absolute and relative terms for physician preference items (PPIs). Among PPIs, high-price, high-quantity hospital-brand combinations average 3.9 percent savings, versus 1.6 percent for commodities.
Stuart Craig, Matthew Grennan, Ashley Swanson (Under Review), Mergers and Marginal Costs: New Evidence on Hospital Buyer Power.
Abstract: We estimate the effects of horizontal mergers on marginal cost efficiencies – a ubiquitous merger justification – using data containing supply purchase orders from a large sample of US hospitals 2009-2015. The data provide a level of detail that has been difficult to observe previously, and a variety of product categories that allows us to examine economic mechanisms underlying “buyer power.” We find that merger target hospitals save on average $176 thousand (or 1.5 percent) annually, driven by geographically local efficiencies in price negotiations for high-tech “physician preference items.” We find only mixed evidence on savings by acquirers.
Matthew Grennan and Ashley Swanson (Working), Diagnosing Price Dispersion: Demand, Bargaining, and Search in Hospital-Supplier Contracting.
Abstract: In a wide range of product markets in which prices are negotiated, price dispersion across buyers for similar (and even identical) products can be driven by heterogeneity in brand preferences, search/contracting costs, and bargaining abilities. We develop a model that allows for each of these mechanisms and estimate it using data on purchases of medical devices/supplies in a broad variety of product categories purchased by over 20 percent of U.S. hospitals 2009-15. While nearly all categories exhibit substantial price dispersion, the drivers vary. Among physician preference items, brand preferences are important drivers of price heterogeneity; among more commodity-like products, bargaining heterogeneity plays a dominant role. Search/contracting costs keep choice sets small, affecting welfare via access to more and better suppliers, and also slightly increasing negotiated markups. We relate these results to expected heterogeneity in the welfare impacts of the growth of conglomerate device suppliers and the entry of information into the marketplace that could facilitate search.
Matthew Grennan (2014), Bargaining Ability and Competitive Advantage: Empirical Evidence from Medical Devices, Management Science, 60, pp. 3011-3025.
Abstract: In markets where buyers and suppliers negotiate, supplier costs, buyer willingness to pay, and competition determine only a range of potential prices, leaving the final price dependent on other factors (e.g., negotiating skill), which I call bargaining ability. I use a model of buyer demand and buyer–supplier bargaining, combined with detailed data on prices and quantities at the buyer–supplier relationship level, to estimate firm bargaining abilities in the context of the coronary stent industry where different hospitals (buyers) pay different prices for the exact same product from the same supplier. I estimate that (1) variation in bargaining abilities explains 79% of this price variation, (2) bargaining ability has a large firm-specific component, and (3) changes in the distribution of bargaining abilities over time suggest learning as an important channel influencing bargaining ability.
Matthew Grennan and Mara Lederman (Work In Progress), The Multiproduct Firm Advantage: Evidence From Regulatory Delay in Medical Devices.
Abstract: Theory offers many explanations for why multiproduct firms dominate a variety of industries, but empirically measuring these forces is complicated by the fact that the scale and scope of the firm are typically endogenous decisions. This paper exploits exogenous variation in firm “multiproductness” induced by differing regulatory regimes in the US and EU to empirically measure the size and sources of multiproduct firm advantages in a medical device market. We find that the average multiproduct device firm enjoys spillovers across its product portfolio that increase the value of a new product introduction by 20.1 percent relative to a new single product firm. The available data suggest that at least part of this effect can be explained by: (1) increased information transmission and (2) greater physical design complementarities among products by multiproduct firms. We discuss these findings in the context of recent questions surrounding innovation, barriers to entry, and conflicts of interest in medical devices.
Matthew Grennan (2013), Price Discrimination and Bargaining: Empirical Evidence from Medical Devices, American Economic Review, 103.
Abstract: Many important issues in business-to-business markets involve price discrimination and negotiated prices, situations where theoretical predictions are ambiguous. This paper uses new panel data on buyer- supplier transfers and a structural model to empirically analyze bargaining and price discrimination in a medical device market. While many phenomena that restrict different prices to different buyers are suggested as ways to decrease hospital costs (e.g., mergers, group purchasing organizations, and transparency), I find that: (i) more uniform pricing works against hospitals by softening competition; and (ii) results depend ultimately on a previously unexplored bargaining effect.
HCMG 391 Health Care Entrepreneurship
HCMG 853 Management and Strategy of Medical Devices and Technology
HCMG 857 Health Care Data Analytics
In healthcare or anywhere else across science, or business, or sports, the importance of data and analytics is virtually unquestioned. That, however, does not mean that it needs no elucidation. In this course, we begin with a fundamental understanding of the state of data and analytics in healthcare and then move onto examples of its use in converting from business questions to implemented solutions. We "sidestep" into the world of algorithms/machine-learning/AI and causal inference, but our focus is on business applications of these tools to the available data in the healthcare industry. As we discuss examples, we always seek to show how human creativity needs to be at the heart of the questions being probed. We highlight today's data universe in healthcare, the level of integration we have achieved, and the immensity of the remaining task, all with an eye to the business opportunities that exist now. We end with a showcasing of the art of the possible - in 2020 - and with (hopefully) a clear look ahead at what remains to be achieved. At the end of this course, students will: 1. Know the health care data landscape; 2. Understand the "loop" that drives modern evidence-based businesses; 3. Dive into real health care data analytics problems, developing a first-hand familiarity with basic tools and concepts; 4. Anticipate the business opportunities evolving in health care data and analytics. Other experience in data science can serve as a substitute for the prerequisites. Knowledge of basic statistics is a must. Coding experience is a plus. Experience coding to solve data/statistics problems is ideal. Further training in data science is a plus, and we welcome those with more advanced preparation.
Delivering basic health care advances worldwide and continuing to increase lifespan and quality (in an affordable manner) represent some of the major societal challenges of our time. Addressing these challenges will require innovation in both medical technology and the ways in which health services are delivered. Through readings, cases, guest lectures, and your own entrepreneurial work outside of class, we will examine the environment facing prospective health care entrepreneurs: (1) sources of health care innovation; (2) the many "customers" in health care: patients, doctors, hospitals, insurers, and regulators; (3) the powerful established firms with developed clinical and sales expertise; (4) the investing community. Along the way we will develop a framework for thinking about what is different (and what is not) about the challenges of health care entrepreneurship.
Successful medical devices are an amalgamation of creative and innovative thinking, clinical expertise, and engineering know-how that endures intense regulatory and reimbursement scrutiny. This course will provide a foundation for understanding the nuances of the medical device industry. It will cover topics ranging from device design and discovery, regulatory issues, marketing, reimbursement, management, and strategy. Classroom activities will be supplemented with optional tours of hospitals, research and manufacturing facilities, and hands-on demonstrations of devices. Though the course is intended primarily for MBA students, it will be open to medical and engineering students as well as to hospital house staff.
In healthcare or anywhere else across science, or business, or sports, the importance of data and analytics is virtually unquestioned. That, however, does not mean that it needs no elucidation. In this course, we begin with a fundamental understanding of the state of data and analytics in healthcare and then move onto examples of its use in converting from business questions to implemented solutions. We "sidestep" into the world of algorithms/machine-learning/AI and causal inference, but our focus is on business applications of these tools to the available data in the healthcare industry. As we discuss examples, we always seek to show how human creativity needs to be at the heart of the questions being probed. We highlight today's data universe in healthcare, the level of integration we have achieved, and the immensity of the remaining task, all with an eye to the business opportunities that exist now. We end with a showcasing of the art of the possible - in 2020 - and with (hopefully) a clear look ahead at what remains to be achieved. At the end of this course, students will: 1. Know the health care data landscape; 2. Understand the "loop" that drives modern evidence-based businesses; 3. Dive into real health care data analytics problems, developing a first-hand familiarity with basic tools and concepts; 4. Anticipate the business opportunities evolving in health care dataand analytics. Experience in data science if prerequisite not met. Other experience in data science can serve as a substitute for the prerequisites. Knowledge of basic statistics is a must. Coding experience is a plus. Experience coding to solve data/statistics problems is ideal. Further training in data science is a plus, and we welcome those with more advanced preparation.
Arranged with members of the Faculty of the Health Care Systems Department. For further information contact the Department office, Room 204, Colonial Penn Center, 3641 Locust Walk, 898-6861.
So far, attempts at transparency in health care fees have not shifted consumer behavior – and too much pricing information could be counterproductive, experts say.Knowledge @ Wharton - 2019/07/2