182 incentives for “meaningful use” of electronic medical records [1]. However, cost, difficulty of implementation, and other barriers impede adoption of such systems, and studies have documented low rates of technology acquisi- tion and implementation in emergency department (ED) and other settings [2]. A study of residency-affiliated EDs in 2000 found low rates of adoption, with only 7% reporting fully imple- mented technology for medication error checking, 18% for computerized medication order entry, and 21% for clinical documentation. A 2006 survey of health information technology in all Massachusetts EDs found similar results, with 11% of respondents reporting fully implemented technology for medication error checking [2]. Another national study showed that, during 2001–2003, only 31% of US EDs used electronic medical records in any form [3]. Our hypothesis is that fewer than half of US EDs had adopted electronic health records systems and related decision-support tools by 2005–2006. Methods We report on data collected as part of the National ED Safety Study (NEDSS). Details of the study design and data collection have been published previously [4]. NEDSS was coordinated by the Emergency Medicine Network (EMNet, www.emnet-usa.org). We recruited EDs mainly by inviting network members. Since most members are affiliated with an emergency medicine residency program (i.e., are academic EDs), we also recruited non-academic and other EDs not affiliated with EMNet through postings on emergency medicine list servers, by contacting sites directly, and through presentations at emergency medicine meetings. We excluded federal hospitals, children’s hospitals, and hospitals in US territories. For the parent National ED Safety Study, each site had a Site Responsible Investigator. For the present study, that person provided information by filling out a survey. The survey asked about ED attributes and adoption of the health information technology applications shown in Table 1. These applications were chosen by the authors as represen- tative of the diverse applications available today, within the constraints of a larger survey with other goals. We performed all calculations with SAS 9.12 (SAS Institute, Cary, NC). The Institutional Review Board of each participating hospital approved the study. Our main outcome measure is descriptive: how many EDs have adopted which applications? Our secondary objective was to determine whether ED characteristics predicted adoption of health information technology. We created two methods for addressing this question. Int J Emerg Med (2010) 3:181–185 Table 1 Characteristics of 68 emergency departments surveyeda Characteristic N (%) Emergency medicine residency affiliation Yes 51 (75%) No 17 (25%) Annual census (visit per year) <10,000 0 10,000–19,999 0 20,000–29,999 1 (1%) 30,000–39,999 12 (18%) 40,000–49,999 12 (18%) ≥50,000 43 (63%) US region Northeast 30 (44%) Midwest 15 (22%) South 10 (15%) West 13 (19%) 9 8 5 8.5 7.5 9 8 9 9 8 Median computerization scoreb a We surveyed key informants at 69 US EDs, as part of the National Emergency Department Safety Study [4], with 68 (99%) of EDs surveyed responding b The Computerization Score was simply a way for us to count the number of information technologies that had been acquired. We calculated the score by assigning one point for each acquired application listed in Table 2 Firstly, we selected one application that seemed to represent the potential of information technology to improve health care. There is substantial debate about what constitutes “meaningful use” of information tech- nology, and we felt that selecting one crucial application for analysis would be the best way to compare across EDs. For the purpose of this analysis, we selected computerized medication ordering as the application. We chose this application because medication ordering represents a crucial site of potential intervention to improve adherence to recommended care and avoid error. We studied this as an outcome variable by creating a logistic regression model with computerized medication order entry as the dependent variable. We then used the model to determine whether ED characteristics predicted its adoption. We categorized EDs according to emergen- cy medicine residency affiliation (binary), annual census (continuous), and region (categorical: Northeast, Mid- west, South, and West). We used multiple logistic regression to model these characteristics as predictors of adoption of computerized medication ordering. We used the Hosmer-Lemeshow goodness of fit test to evaluate for non-linearity. Secondly, we did not wish to limit our investigation to any one application or combination of applications, as was done in the analysis described above. Thus, we also compared EDs by the raw number of applications they