Touchless computer interfaces in hospitals: A review <s> Radiologists 7 users <s> INTRODUCTION ::: Speech recognition is currently being deployed in medical and anaesthesia applications. This article is part of a project to investigate and further develop a prototype of a speech-input interface in Danish for an electronic anaesthesia patient record, to be used in real time during operations. ::: ::: ::: OBJECTIVE ::: The aim of the experiment is to evaluate the relative impact of several factors affecting speech recognition when used in operating rooms, such as the type or loudness of background noises, type of microphone, type of recognition mode (free speech versus command mode), and type of training. ::: ::: ::: METHODS ::: Eight volunteers read aloud a total of about 3600 typical short anaesthesia comments to be transcribed by a continuous speech recognition system. Background noises were collected in an operating room and reproduced. A regression analysis and descriptive statistics were done to evaluate the relative effect of various factors. ::: ::: ::: RESULTS ::: Some factors have a major impact, such as the words to be recognised, the type of recognition and participants. The type of microphone is especially significant when combined with the type of noise. While loud noises in the operating room can have a predominant effect, recognition rates for common noises (e.g. ventilation, alarms) are only slightly below rates obtained in a quiet environment. Finally, a redundant architecture succeeds in improving the reliability of the recognitions. ::: ::: ::: CONCLUSION ::: This study removes some uncertainties regarding the feasibility of introducing speech recognition for anaesthesia records during operations, and provides an overview of the interaction of several parameters that are traditionally studied separately. <s> BIB001 </s> Touchless computer interfaces in hospitals: A review <s> Radiologists 7 users <s> Abstract Introduction This article describes the evaluation of a prototype speech-input interface to an anaesthesia patient record, conducted in a full-scale anaesthesia simulator involving six doctor–nurse anaesthetist teams. Objective The aims of the experiment were, first, to assess the potential advantages and disadvantages of a vocal interface compared to the traditional touch-screen and keyboard interface to an electronic anaesthesia record during crisis situations; second, to assess the usability in a realistic work environment of some speech input strategies (hands-free vocal interface activated by a keyword; combination of command and free text modes); finally, to quantify some of the gains that could be provided by the speech input modality. Methods Six anaesthesia teams composed of one doctor and one nurse were each confronted with two crisis scenarios in a full-scale anaesthesia simulator. Each team would fill in the anaesthesia record, in one session using only the traditional touch-screen and keyboard interface while in the other session they could also use the speech input interface. Audio-video recordings of the sessions were subsequently analysed and additional subjective data were gathered from a questionnaire. Analysis of data was made by a method inspired by queuing theory in order to compare the delays associated to the two interfaces and to quantify the workload inherent to the memorisation of items to be entered into the anaesthesia record. Results The experiment showed on the one hand that the traditional touch-screen and keyboard interface imposes a steadily increasing mental workload in terms of items to keep in memory until there is time to update the anaesthesia record, and on the other hand that the speech input interface will allow anaesthetists to enter medications and observations almost simultaneously when they are given or made. The tested speech input strategies were successful, even with the ambient noise. Speaking to the system while working appeared feasible, although improvements in speech recognition rates are needed. Conclusion A vocal interface leads to shorter time between the events to be registered and the actual registration in the electronic anaesthesia record; therefore, this type of interface would likely lead to greater accuracy of items recorded and a reduction of mental workload associated with memorisation of events to be registered, especially during time constrained situations. At the same time, current speech recognition technology and speech interfaces require user training and user dedication if a speech interface is to be used successfully. <s> BIB002 </s> Touchless computer interfaces in hospitals: A review <s> Radiologists 7 users <s> PURPOSE ::: Despite the frequent introduction of voice recognition (VR) into radiology departments, little evidence still exists about its impact on workflow, error rates and costs. We designed a study to compare typographical errors, turnaround times (TAT) from reported to verified and productivity for VR-generated reports versus transcriptionist-generated reports in MRI. ::: ::: ::: METHODS ::: Fifty MRI reports generated by VR and 50 finalized MRI reports generated by the transcriptionist, of two radiologists, were sampled retrospectively. Two hundred reports were scrutinised for typographical errors and the average TAT from dictated to final approval. To assess productivity, the average MRI reports per hour for one of the radiologists was calculated using data from extra weekend reporting sessions. ::: ::: ::: RESULTS ::: Forty-two % and 30% of the finalized VR reports for each of the radiologists investigated contained errors. Only 6% and 8% of the transcriptionist-generated reports contained errors. The average TAT for VR was 0 h, and for the transcriptionist reports TAT was 89 and 38.9 h. Productivity was calculated at 8.6 MRI reports per hour using VR and 13.3 MRI reports using the transcriptionist, representing a 55% increase in productivity. ::: ::: ::: CONCLUSION ::: Our results demonstrate that VR is not an effective method of generating reports for MRI. Ideally, we would have the report error rate and productivity of a transcriptionist and the TAT of VR. <s> BIB003
Reports generated using voice recognition were approximately 24% shorter in length and took 50% longer to dictate than those transcribed conventionally. The reports generated using voice dictation also contained more errors (5.1 errors/report vs 0.12 errors/report). Creating and editing radiology reports using voice control BIB003 Radiologists 2 users Productivity for one radiologist was calculated at 8.6 MRI reports per hour using voice recognition and 13.3 MRI reports per hour using a transcriptionist. Voice recognition of sentences BIB001 Users with no medical background 8 users The system performed at a higher rate of classification in command mode than free speech mode (81.6% vs 77.1%). Training the system without background noise improves recognition rate (85.5% vs 77.8%). Updating anaesthesia record using speech recognition BIB002 