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• Case | HBS Case Collection | September 2018 (Revised December 2019)

  Zebra Medical Vision

  Shane Greenstein and Sarah Gulick

  An Israeli startup founded in 2014, Zebra Medical Vision developed algorithms that produced diagnoses from X-rays, mammograms, and CT-scans. The algorithms used deep learning and digitized radiology scans to create software that could assist doctors in making diagnoses. By July 2018, Zebra had developed seven algorithms to analyze scans for emphysema, liver density, compression fractures, bone density, brain bleeds, breast cancer, and a calcium score used to detect calcified plaque in coronary arteries. For each scan analyzed, Zebra charged hospitals $1. By 2018 Zebra found itself in a race with its competitors to perfect these algorithms, create software tools, distribute the tools to physician partners, and create a market. Zebra already had several partners in the U.S. and Europe who gave feedback on its development. Management had to answer the following questions: What should they do next at Zebra? Should they work on the accuracy of the already developed algorithms or continue to develop many new tools? If they chose to develop new products, which applications should they address? And how should they go to market?

  Keywords: radiology; Machine learning; X-ray; CT scan; medical technology; probability; FDA 510(k); diagnosis; Business Startups; Health Care and Treatment; Technology; Software; Competitive Strategy; Product Development; Commercialization; Decision Choices and Conditions; Health Industry; Medical Devices and Supplies Industry; Technology Industry; Israel;

  Citation:

  Greenstein, Shane, and Sarah Gulick. "Zebra Medical Vision." Harvard Business School Case 619-014, September 2018. (Revised December 2019.)  View Details

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• Case | HBS Case Collection | November 2019

  DeepMap: Charting the Road Ahead For Autonomous Vehicles

  Shane Greenstein and Nicole Tempest Keller

  Founded in 2016, DeepMap developed high definition (HD) mapping software and localization services for Level 4+ autonomous vehicles. Traditional navigational maps were accurate to a few meters, which was sufficient for drivers, but not for machine-driven vehicles which required centimeter level accuracy. Autonomous vehicles required a new form of map that was highly precise, produced a 3D representation of the surrounding area, enabled vehicles to locate themselves within the map, provided information for how to navigate safely using the correct rules of the road, and was updated continuously as road conditions changed. DeepMap was not selling a static mapping database, but rather licensing its software under a software-as-a-service model. As a startup with limited resources navigating a nascent market, DeepMap faced uncertainty across several dimensions: the timing of overall AV market adoption, which countries would adopt fastest, which AV segments would move most rapidly, which sensor technologies would become standard, and what impact regulation would have. They also faced the challenge of serving customers on a global scale. As DeepMap looked ahead, it had to decide how and where to focus and allocate its funding in order to achieve its short and long-term objectives. 

  Keywords: mapping software; autonomous vehicles; Business Startups; Software; Technological Innovation; Technology Adoption; Service Delivery; Global Range; Resource Allocation; Strategic Planning; Technology Industry; Auto Industry;

  Citation:

  Greenstein, Shane, and Nicole Tempest Keller. "DeepMap: Charting the Road Ahead For Autonomous Vehicles." Harvard Business School Case 620-047, November 2019.  View Details

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• Article | Telecommunications Policy | September 2019

  The Persistence of Broadband User Behavior: Implications for Universal Service and Competition Policy

  Andre Boik, Shane Greenstein and Jeffrey Prince

  In several markets, firms compete not for consumer expenditure but consumer attention. We examine user priorities over the allocation of their time, and interpret that behavior in light of salient tensions in policy discussions over universal service, data caps, and related policy topics, such as merger analysis. Specifically, we use extensive microdata on user online choice to characterize the demand for the services offered online, which drives a household’s supply of attention. Our data cover a period of time that saw the introduction of many new and notable sites and new devices on which to access them. In our analysis, we assess “how” households supply their attention along various dimensions, such as their concentration of attention across the universe of sites and the amount of attention expenditure per domain visit. Remarkably, we find no change in “how” households allocated their attention despite drastically changing where they allocated it. Moreover, conditional on total attention expenditure, demographics entirely fail to predict our key measures of attention allocation decisions. We highlight several important implications, for policy and beyond, stemming from the persistence and demographic orthogonality of our novel attention measures.

  Keywords: broadband service; attention allocation; Consumer Behavior; Household; Internet; Competition; Policy;

  Citation:

  Boik, Andre, Shane Greenstein, and Jeffrey Prince. "The Persistence of Broadband User Behavior: Implications for Universal Service and Competition Policy." Telecommunications Policy 43, no. 8 (September 2019).  View Details

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