Systems Society, a very large U.S. healthcare IT industry trade group, observed in 2009 that EHR adoption rates "have been slower than expected in the United States, especially in comparison to other industry sectors and other developed countries. A key reason, aside from initial costs and lost productivity during EMR implementation, is lack of efficiency and usability of EMRs currently available." The U.S. National Institute of Standards and Technology of the Department of Commerce studied usability in 2011 and lists a number of specific issues that have been reported by health care workers. The U.S. military's EHR, AHLTA, was reported to have significant usability issues. Furthermore, studies such as the one conducted in BMC Medical Informatics and Decision Making, also showed that although the implementation of electronic medical records systems has been a great assistance to general practitioners there is still much room for revision in the overall framework and the amount of training provided. It was observed that the efforts to improve EHR usability should be placed in the context of physician-patient communication. However, physicians are embracing mobile technologies such as smartphones and tablets at a rapid pace. According to a 2012 survey by Physicians Practice, 62.6 percent of respondents (1,369 physicians, practice managers, and other healthcare providers) say they use mobile devices in the performance of their job. Mobile devices are increasingly able to sync up with electronic health record systems thus allowing physicians to access patient records from remote locations. Most devices are extensions of desk-top EHR systems, using a variety of software to communicate and access files remotely. The advantages of instant access to patient records at any time and any place are clear, but bring a host of security concerns. As mobile systems become more prevalent, practices will need comprehensive policies that govern security measures and patient privacy regulations. Other advanced computational techniques have allowed EHRs to be evaluated at a much quicker rate. Natural language processing is increasingly used to search EMRs, especially through searching and analyzing notes and text that would otherwise be inaccessible for study when seeking to improve care. One study found that several machine learning methods could be used to predict the rate of a patient's mortality with moderate success, with the most successful approach including using a combination of a convolutional neural network and a heterogenous graph model. Hardware and workflow considerations When a health facility has documented their