Paper Versus Electronic Medical Records: The Effects of Access on Physicians' Decisions to Use Complex Information Technologies*

This study examines physicians' responses to complex information technologies (IT) in the health care supply chain. We extend individual‐level IT adoption models by incorporating a new construct: system accessibility. The main premise of the study is, when faced with a decision between alternate IT systems, individual users tend to select and make use of the technology or system that is most readily accessible. We discuss both physical and logical dimensions of accessibility as they relate to adoption of electronic medical records (EMR). Physical accessibility refers to the availability of computers that can be used to access EMR, while logical accessibility refers to the ease or difficulty of logging into the system. Using data from a survey of 199 physicians practicing in a large U.S. hospital, we show that, when deciding between the paper chart and EMR, accessibility is an important consideration in a physician's decision to use the system. Both dimensions of accessibility act as barriers to EMR use intentions through their indirect effect on physicians' perceptions of EMR usefulness and ease of use. Logical access also has a direct effect on EMR use intentions. We conclude that accessibility is an important factor that limits acceptance of complex IT such as EMR.     

Deterministic Approximation of Stochastic Evolution in Games

This paper provides deterministic approximation results for stochastic processes that arise when finite populations recurrently play finite games. The processes are Markov chains, and the approximation is defined in continuous time as a system of ordinary differential equations of the type studied in evolutionary game theory. We establish precise connections between the long‐run behavior of the discrete stochastic process, for large populations, and its deterministic flow approximation. In particular, we provide probabilistic bounds on exit times from and visitation rates to neighborhoods of attractors to the deterministic flow. We sharpen these results in the special case of ergodic processes.     

The Dynamics of Inequality

The past forty years have seen a rapid rise in top income inequality in the United States. While there is a large number of existing theories of the Pareto tail of the long‐run income distributions, almost none of these address the fast rise in top inequality observed in the data. We show that standard theories, which build on a random growth mechanism, generate transition dynamics that are too slow relative to those observed in the data. We then suggest two parsimonious deviations from the canonical model that can explain such changes: “scale dependence” that may arise from changes in skill prices, and “type dependence,” that is, the presence of some “high‐growth types.” These deviations are consistent with theories in which the increase in top income inequality is driven by the rise of “superstar” entrepreneurs or managers.