184 Qualitatively, it seems reasonable to conclude from these three studies that most US EDs do not have electronic medication ordering, or its companion technology, medica- tion error checking. However, this conclusion must be caveated by the fact that large academic EDs were over- represented in these samples. Studies of health information technology adoption in physicians’ offices and hospitals throughout the country also revealed slow uptake by 2008 [8, 9]. These studies found that only 7.6% of US hospitals have a basic electronic records system, and 1.5% a comprehensive one. (These studies defined “basic” as including only demo- graphic information, CPOE, laboratory and imaging results, and “comprehensive” as including the above, plus clinical notes, plus electronic prescribing, radiographic image display, and decision support.) Computerized provider order entry for medications has been implemented in only 17% of hospitals. Only 4% of physicians’ offices have a fully functional electronic-records system, and 13% have a basic system. ED-based studies also show that some health informa- tion technology applications are widespread, including patient tracking; ordering tests; and displaying prior visit notes, ECGs, and laboratory and radiology results [2, 3, 5]. An analysis of the types of health information technology may improve our understanding of these results. One type of application is computationally simple, merely entering and extracting information from a database, and displaying it. The other type of application requires all of the above processes, plus algorithmic processing that seeks to supplement human cognition with computation. For lack of better terms, we might refer to these two types of application as “passive” and “active” applications, respec- tively. From a workflow standpoint, passive applications simply convert traditional paper processes into an electronic format. Such technologies include patient tracking; ordering tests; and displaying prior visit notes, ECGs, laboratory and radiology results. Active applications add functions designed to improve outcomes by reducing error or increasing adherence to the standard of care—they seek to change human behavior. These applications often embody fundamental changes in clinical processes, and implemen- tation is intertwined with changes in workflow [6, 10]. Examples include computerized provider order entry and related error-checking functions, and decision support systems. In light of this dichotomization, we can see that the present and prior studies reveal a high rate of adoption of passive applications in US EDs and a low rate of adoption of active applications [2, 3]. (We included laboratory test ordering in the “passive” category for historical reasons. Such systems have been present in most EDs for many years [5] and were not originally subject to behavior-modification technology.) Int J Emerg Med (2010) 3:181–185 An optimistic interpretation of our data would be to celebrate the fact that most US EDs did not invest in expensive, cumbersome, and even potentially risky systems prematurely. Now that a large number of commercial and home-grown systems are available, we should take the time to study them and reach consensus on which would be best for particular environments. We should pay particular attention to the interaction of information systems with human workflows [6, 10]. The Certification Commission for Healthcare Information Technology formed an ED Work Group in 2007, which is in the process of certifying various health information technology platforms for ED use, in concert with the Health Level Seven project [11]. This type of certification process may be an important first step in improving the types of systems that are available, or at least our understanding of what is available. Our study is limited by the convenience sample of 68 EDs across the US. Participating sites were generally large, urban, academic EDs. Because the underlying patient safety study focused on adult illnesses, the study excluded children’s hospitals. We did assess for variabil- ity in technology adoption by ED characteristics, and found no significant variation. This improves our confidence in the generalizability of our data to other EDs in the US, though, again, large academic EDs were over-represented in the sample. Perhaps more saliently, the present results are similar to those of prior studies, from academic EDs throughout the US, and all EDs in Massachusetts [2]. It remains true that we have little data on health information technology adoption in the 1/3 of US EDs with an annual visit volume <10,000. From the perspective of the individual patient, small EDs have the same clinical goals as large EDs. But from a systems perspective, the marginal cost of health information technology in low-volume settings will be high relative to benefit, both financially and in terms of workflow modification and associated risks [8]. Another limitation is the fact that our assessment was part of a larger survey, and thus, we could not conduct as detailed an inquiry as would have been desirable. In particular, we did not ascertain adoption of health informa- tion technology for medication order checking or decision support. However, the low rate of adoption of medication order entry technology allows us to conclude with confidence that these technologies were not available in the majority of respondent EDs. Active applications involve intertwining of technology and human workflows, and are intended to change behavior. Passive applications involve simple data pro- cessing and display. Together with the findings of other studies, our results demonstrate that most US EDs have adopted passive applications but not active applications that have been recommended forcefully [2, 3]. We view