Objective
To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research.
Methods
The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches.
Results
The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%.
Conclusions
Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.
The U.S. residential real estate agency market presents a puzzle for economic theory: commissions on real estate transactions have remained constant and high for decades even though agent entry is frequent and agents’ costs of providing service are low. We model the real estate agency market, and other brokered markets, via repeated extensive form games; in our game, brokers first post prices for customers and then choose which agents on the other side of the market to work with. We show that prices appreciably higher than the competitive prices can be sustained (for a fixed discount factor) regardless of the number of brokers; this is done through strategies that condition willingness to transact with each broker on that broker’s initial posted prices. Our results can thus rationalize why brokered markets exhibit pricing high above marginal cost despite fierce competition for customers; moreover, our model can help explain why agents and platforms who have tried to reduce commissions have had trouble entering the market.
Citation:
Hatfield, John William, Scott Duke Kominers, and Richard Lowery. "Collusion in Brokered Markets." Harvard Business School Working Paper, No. 20-023, September 2019.
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We introduce a new model of school choice with reserves in which a social planner is constrained by a limited supply of reserve seats and tries to find an optimal matching according to a social welfare function. We construct the optimal distribution of reserves via a quartic-time dynamic programming algorithm. Due to the modular nature of the dynamic program, the mechanism is strategy-proof for reserve-eligible students.
This case is set in mid-2019, when Qualcomm was struggling with unwanted takeover battles, fights with Apple and the Chinese government, and internal dissension on the board of directors. Ten years earlier Qualcomm was hailed as a monopoly on CDMA technologies and its derivatives—it appeared to be positioned to become the next dominant technology company in the tradition of Microsoft and Intel. But as the industry changed, Qualcomm’s competitive position seriously weakened. The purpose of the case is to explore central questions in strategy formulation: 1. how to build a long-term sustainable position based on technology, 2. how to design an IP strategy to lock in customers for the long run, 3. how to build a global standard and appropriate returns on that standard, and 4. how to respond when your historical business model starts to crumble.
