We demonstrate the importance of explicit definitions of electronic health record (EHR) data completeness and how different conceptualizations of completeness may impact findings from EHR-derived datasets. This study has important repercussions for researchers and clinicians engaged in the secondary use of EHR data. We describe four prototypical definitions of EHR completeness: documentation, breadth, density, and predictive completeness. Each definition dictates a different approach to the measurement of completeness. These measures were applied to representative data from NewYork-Presbyterian Hospital’s clinical data warehouse. We found that according to any definition, the number of complete records in our clinical database is far lower than the nominal total. The proportion that meets criteria for completeness is heavily dependent on the definition of completeness used, and the different definitions generate different subsets of records. We conclude that the concept of completeness in EHR is contextual. We urge data consumers to be explicit in how they define a complete record and transparent about the limitations of their data.

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This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/manc.12091This paper examines the relationship between the bundling decision of a large firm and the impact of spillovers from smaller firms drawing on stylized facts from the software industry. We find that bundling occurs only in the presence of significant spillovers from the two smaller firms. The large firm does not bundle when there are spillovers for only one product. Finally, we show that welfare is higher under bundlingauthorsversionPeer reviewe