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PubMed2004

[Rediscovery in morphology; information processing of pathology].

20 years ago, bacilli of Helicobacter pylori were discovered, although these bacilli had been recognized before under microscope. This fact suggests that many findings are still buried in the pathological materials. Now computer technology is making remarkable progress; in addition to database software, voice recognition and virtual microscopy are available. These information technology will make possible to detect buried findings, and act as a guide to new rediscovery.

Databases, Factual↗

PubMed2004

Use of voice recognition software in an outpatient pediatric specialty practice.

BACKGROUND: Voice recognition software (VRS), with specialized medical vocabulary, is being promoted to enhance physician efficiency, decrease costs, and improve patient safety. This study reports the experience of a pediatric subspecialist (pediatric gastroenterology) physician with the use of Dragon Naturally Speaking (version 6; ScanSoft Inc, Peabody, MA), incorporated for use with a proprietary electronic medical record, in a large university medical center ambulatory care service. METHODS: After 2 hours of group orientation and 2 hours of individual VRS instruction, the physician trained the software for 1 month (30 letters) during a hospital slowdown. Set-up, dictation, and correction times for the physician and medical transcriptionist were recorded for these training sessions, as well as for 42 subsequently dictated letters. Figures were extrapolated to the yearly clinic volume for the physician, to estimate costs (physician: 110 dollars per hour; transcriptionist: 11 dollars per hour, US dollars). RESULTS: The use of VRS required an additional 200% of physician dictation and correction time (9 minutes vs 3 minutes), compared with the use of electronic signatures for letters typed by an experienced transcriptionist and imported into the electronic medical record. When the cost of the license agreement and the costs of physician and transcriptionist time were included, the use of the software cost 100% more, for the amount of dictation performed annually by the physician. CONCLUSIONS: VRS is an intriguing technology. It holds the possibility of streamlining medical practice. However, the learning curve and accuracy of the tested version of the software limit broad physician acceptance at this time.

Attitude of Health Personnel↗

PubMed2005

Overcoming obstacles to work-changing technology such as PACS and voice recognition.

OBJECTIVE: When implementing picture archival communication systems (PACS) and voice recognition (VR) technology in radiology, management attention generally has focused on justifying the system financially, investigating potential vendors, developing timelines for hardware delivery, installing the software, developing interfaces, and tracking and measuring results. These are critical components in implementing PACS and VR technology, but not sufficient for success. CONCLUSION: This article argues that the ability to view organizational issues related to changing how people do their work from multiple organizational perspectives is equally important in determining the success of PACS and VR projects.

Adaptation, Psychological↗

PubMedDate not supplied

The effect of automatic speech recognition systems on speaking workload and task efficiency.

PURPOSE: This investigation explored the speech-production behaviors associated with the use of automatic speech recognition (ASR) software for dictation of spontaneous and scripted material by individuals with and without spinal cord injury (SCI). The variables of interest in determining speaking workload and efficiency included syllables per breath group, frequency of breath groups, frequency of apnea, time needed for dictation and number of words spoken during a dictation task. METHOD: Twelve individuals participated, six with SCI and six able-bodied cohorts matched for age, sex and height. Subjects dictated with continuous-speech ASR, discrete-word ASR and no ASR in a spontaneous-speaking situation, as well as in a scripted speaking situation. RESULTS: For all variables, differences amongst dictation conditions were significant. No significant differences were found between speaker groups. Dictation with both discrete-word and continuous-speech ASR resulted in a decrease in the number of syllables produced per breath group, increases in the frequency of breath groups and apnea, with differences from normal being greater for dictation with discrete-word ASR. In addition, when participants dictated with either type of ASR, the amount of time and number of words produced were significantly greater than that associated with production of the same message without ASR, requiring 'more work' on the part of the speaker and ultimately reducing the efficiency with which a message was produced. CONCLUSIONS: From a human factors perspective, these results suggest that ASR software, especially discrete-word ASR, has the potential to increase energy expenditure during dictation over a prolonged period of time, thereby increasing speech workloads and the potential for overuse of the laryngeal system.

Adult↗

PubMed2005

Higher order statistical analysis of /x/ in male speech.

This paper presents a study of kurtosis analysis for the sound /x/ in male speech, /x/ is the sound of the 'o' at the end of words such as 'ago'. The sound analysed for this paper came from the Australian National Database of Spoken Language, more specifically the male speaker 17. The /x/ was isolated and extracted from the database by the author in a quiet booth using standard multimedia software. A 5 millisecond window was used for the analysis as it was shown previously by the author to be the most appropriate size for speech phoneme analysis. The significance of the research presented here is shown in the results where a majority of coefficients had a platykurtic (kurtosis between 0 and 3) value as opposed to the previously held leptokurtic (kurtosis > 3) belief.

Algorithms↗

PubMed2006

Initial clinical experience with a partly autonomous robotic surgical instrument server.

BACKGROUND: The authors believe it would be useful to have surgical robots capable of some degree of autonomous action in cooperation with the human members of a surgical team. They believe that a starting point for such development would be a system for delivering and retrieving instruments during a surgical procedure. METHODS: The described robot delivers instruments to the surgeon and retrieves the instruments when they are no longer being used. Voice recognition software takes in requests from the surgeon. A mechanical arm with a gripper is used to handle the instruments. Machine-vision cameras locate the instruments after the surgeon puts them down. Artificial intelligence software makes decisions about the best response to the surgeon's requests. RESULTS: A robot was successfully used in surgery for the first time June 16, 2005. The operation involved excision of a benign lipoma. The procedure lasted 31 min, during which time the robot performed 16 instrument deliveries and 13 instrument returns with no significant errors. The average time between verbal request and delivery of an instrument was 12.4 s. CONCLUSIONS: The described robot is capable of delivering instruments to a surgeon at command and can retrieve them independently using machine vision. This robot, termed a "surgical instrument server," represents a new class of information-processing machines that will relieve the operating room team of repetitive tasks and allow the members to focus more attention on the patient.

Adult↗

PubMed2005

[Speech recognition: impact on workflow and report availability].

With ongoing technical refinements speech recognition systems (SRS) are becoming an increasingly attractive alternative to traditional methods of preparing and transcribing medical reports. The two main components of any SRS are the acoustic model and the language model. Features of modern SRS with continuous speech recognition are macros with individually definable texts and report templates as well as the option to navigate in a text or to control SRS or RIS functions by speech recognition. The best benefit from SRS can be obtained if it is integrated into a RIS/RIS-PACS installation. Report availability and time efficiency of the reporting process (related to recognition rate, time expenditure for editing and correcting a report) are the principal determinants of the clinical performance of any SRS. For practical purposes the recognition rate is estimated by the error rate (unit "word"). Error rates range from 4 to 28%. Roughly 20% of them are errors in the vocabulary which may result in clinically relevant misinterpretation. It is thus mandatory to thoroughly correct any transcribed text as well as to continuously train and adapt the SRS vocabulary. The implementation of SRS dramatically improves report availability. This is most pronounced for CT and CR. However, the individual time expenditure for (SRS-based) reporting increased by 20-25% (CR) and according to literature data there is an increase by 30% for CT and MRI. The extent to which the transcription staff profits from SRS depends largely on its qualification. Online dictation implies a workload shift from the transcription staff to the reporting radiologist.

Database Management Systems↗

PubMed2005

Speech recognition implementation in radiology.

Continuous speech recognition (SR) is an emerging technology that allows direct digital transcription of dictated radiology reports. The SR systems are being widely deployed in the radiology community. This is a review of technical and practical issues that should be considered when implementing an SR system.

Humans↗

PubMed2005

Intelligibility of laryngectomees' substitute speech: automatic speech recognition and subjective rating.

Substitute speech after laryngectomy is characterized by restricted aero-acoustic properties in comparison with laryngeal speech and has therefore lower intelligibility. Until now, an objective means to determine and quantify the intelligibility has not existed, although the intelligibility can serve as a global outcome parameter of voice restoration after laryngectomy. An automatic speech recognition system was applied on recordings of a standard text read by 18 German male laryngectomees with tracheoesophageal substitute speech. The system was trained with normal laryngeal speakers and not adapted to severely disturbed voices. Substitute speech was compared to laryngeal speech of a control group. Subjective evaluation of intelligibility was performed by a panel of five experts and compared to automatic speech evaluation. Substitute speech showed lower syllables/s and lower word accuracy than laryngeal speech. Automatic speech recognition for substitute speech yielded word accuracy between 10.0 and 50% (28.7+/-12.1%) with sufficient discrimination. It complied with experts' subjective evaluations of intelligibility. The multi-rater kappa of the experts alone did not differ from the multi-rater kappa of experts and the recognizer. Automatic speech recognition serves as a good means to objectify and quantify global speech outcome of laryngectomees. For clinical use, the speech recognition system will be adapted to disturbed voices and can also be applied in other languages.

Humans↗

PubMed2004

Speech recognition-based and automaticity programs to help students with severe reading and spelling problems.

This study was conducted to assess the effectiveness of two programs developed by the Frostig Center Research Department to improve the reading and spelling of students with learning disabilities (LD): a computer Speech Recognition-based Program (SRBP) and a computer and text-based Automaticity Program (AP). Twenty-eight LD students with reading and spelling difficulties (aged 8 to 18) received each program for 17 weeks and were compared with 16 students in a contrast group who did not receive either program. After adjusting for age and IQ, both the SRBP and AP groups showed significant differences over the contrast group in improving word recognition and reading comprehension. Neither program showed significant differences over contrasts in spelling. The SRBP also improved the performance of the target group when compared with the contrast group on phonological elision and nonword reading efficiency tasks. The AP showed significant differences in all process and reading efficiency measures.

Adolescent↗

PubMed2005

An evaluation of the quick inventory of depressive symptomatology and the hamilton rating scale for depression: a sequenced treatment alternatives to relieve depression trial report.

BACKGROUND: Nine DSM-IV-TR criterion symptom domains are evaluated to diagnose major depressive disorder (MDD). The Quick Inventory of Depressive Symptomatology (QIDS) provides an efficient assessment of these domains and is available as a clinician rating (QIDS-C16), a self-report (QIDS-SR16), and in an automated, interactive voice response (IVR) (QIDS-IVR16) telephone system. This report compares the performance of these three versions of the QIDS and the 17-item Hamilton Rating Scale for Depression (HRSD17). METHODS: Data were acquired at baseline and exit from the first treatment step (citalopram) in the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial. Outpatients with nonpsychotic MDD who completed all four ratings within +/-2 days were identified from the first 1500 STAR*D subjects. Both item response theory and classical test theory analyses were conducted. RESULTS: The three methods for obtaining QIDS data produced consistent findings regarding relationships between the nine symptom domains and overall depression, demonstrating interchangeability among the three methods. The HRSD17, while generally satisfactory, rarely utilized the full range of item scores, and evidence suggested multidimensional measurement properties. CONCLUSIONS: In nonpsychotic MDD outpatients without overt cognitive impairment, clinician assessment of depression severity using either the QIDS-C16 or HRSD17 may be successfully replaced by either the self-report or IVR version of the QIDS.

Adult↗

PubMed2005

Voice recognition for radiology reporting: is it good enough?

AIM: To compare the efficiency and accuracy of radiology reports generated by voice recognition (VR) against the traditional tape dictation-transcription (DT) method. MATERIALS AND METHODS: Two hundred and twenty previously reported computed radiography (CR) and cross-sectional imaging (CSI) examinations were separately entered into the Radiology Information System (RIS) using both VR and DT. The times taken and errors found in the reports were compared using univariate analyses based upon the sign-test, and a general linear model constructed to examine the mean differences between the two methods. RESULTS: There were significant reductions (p<0.001) in the mean difference in the reporting times using VR compared with DT for the two reporting methods assessed (CR, +67.4; CSI, +122.1s). There was a significant increase in the mean difference in the actual radiologist times using VR compared with DT in the CSI reports; -14.3s, p=0.037 (more experienced user); -13.7s, p=0.014 (less experienced user). There were significantly more total and major errors when using VR compared with DT for CR reports (-0.25 and -0.26, respectively), and in total errors for CSI (-0.75, p<0.001), but no difference in major errors (-0.16, p=0.168). Although there were significantly more errors with VR in the less experienced group of users (mean difference in total errors -0.90, and major errors -0.40, p<0.001), there was no significant difference in the more experienced (p=0.419 and p=0.814, respectively). CONCLUSIONS: VR is a viable reporting method for experienced users, with a quicker overall report production time (despite an increase in the radiologists' time) and a tendency to more errors for inexperienced users.

England↗

PubMed2004

A simple error classification system for understanding sources of error in automatic speech recognition and human transcription.

OBJECTIVES: To (1) discover the types of errors most commonly found in clinical notes that are generated either using automatic speech recognition (ASR) or via human transcription and (2) to develop efficient rules for classifying these errors based on the categories found in (1). The purpose of classifying errors into categories is to understand the underlying processes that generate these errors, so that measures can be taken to improve these processes. METHODS: We integrated the Dragon NaturallySpeaking v4.0 speech recognition engine into the Regenstrief Medical Record System. We captured the text output of the speech engine prior to error correction by the speaker. We also acquired a set of human transcribed but uncorrected notes for comparison. We then attempted to error correct these notes based on looking at the context alone. Initially, three domain experts independently examined 104 ASR notes (containing 29,144 words) generated by a single speaker and 44 human transcribed notes (containing 14,199 words) generated by multiple speakers for errors. Collaborative group sessions were subsequently held where error categorizes were determined and rules developed and incrementally refined for systematically examining the notes and classifying errors. RESULTS: We found that the errors could be classified into nine categories: (1) announciation errors occurring due to speaker mispronounciation, (2) dictionary errors resulting from missing terms, (3) suffix errors caused by misrecognition of appropriate tenses of a word, (4) added words, (5) deleted words, (6) homonym errors resulting from substitution of a phonetically identical word, (7) spelling errors, (8) nonsense errors, words/phrases whose meaning could not be appreciated by examining just the context, and (9) critical errors, words/phrases where a reader of a note could potentially misunderstand the concept that was related by the speaker. CONCLUSIONS: A simple method is presented for examining errors in transcribed documents and classifying these errors into meaningful and useful categories. Such a classification can potentially help pinpoint sources of such errors so that measures (such as better training of the speaker and improved dictionary and language modeling) can be taken to optimize the error rates.

Automation↗

PubMed2006

Impact of noise and other factors on speech recognition in anaesthesia.

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.

Adult↗

PubMed2005

Investigation of an HMM/ANN hybrid structure in pattern recognition application using cepstral analysis of dysarthric (distorted) speech signals.

Computer speech recognition of individuals with dysarthria, such as cerebral palsy patients requires a robust technique that can handle conditions of very high variability and limited training data. In this study, application of a 10 state ergodic hidden Markov model (HMM)/artificial neural network (ANN) hybrid structure for a dysarthric speech (isolated word) recognition system, intended to act as an assistive tool, was investigated. A small size vocabulary spoken by three cerebral palsy subjects was chosen. The effect of such a structure on the recognition rate of the system was investigated by comparing it with an ergodic hidden Markov model as a control tool. This was done in order to determine if this modified technique contributed to enhanced recognition of dysarthric speech. The speech was sampled at 11 kHz. Mel frequency cepstral coefficients were extracted from them using 15 ms frames and served as training input to the hybrid model setup. The subsequent results demonstrated that the hybrid model structure was quite robust in its ability to handle the large variability and non-conformity of dysarthric speech. The level of variability in input dysarthric speech patterns sometimes limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor impaired individuals holds sufficient promise.

Algorithms↗

PubMedDate not supplied

Early lexical development in a self-organizing neural network.

In this paper we present a self-organizing neural network model of early lexical development called DevLex. The network consists of two self-organizing maps (a growing semantic map and a growing phonological map) that are connected via associative links trained by Hebbian learning. The model captures a number of important phenomena that occur in early lexical acquisition by children, as it allows for the representation of a dynamically changing linguistic environment in language learning. In our simulations, DevLex develops topographically organized representations for linguistic categories over time, models lexical confusion as a function of word density and semantic similarity, and shows age-of-acquisition effects in the course of learning a growing lexicon. These results match up with patterns from empirical research on lexical development, and have significant implications for models of language acquisition based on self-organizing neural networks.

Artificial Intelligence↗

PubMedDate not supplied

Online algorithm for the self-organizing map of symbol strings.

In this work an online algorithm is presented for the construction of the self-organizing map (SOM) of symbol strings. Each node of the SOM grid is associated with a model string which is a variable-vector sequence. Smooth interpolation method is applied in the training which performs simultaneous adaptation of the symbol content and the length of the model string. The efficiency of the method is demonstrated by the clustering of a 100,000-word English dictionary.

Algorithms↗

PubMed2006

[Impact of a PACS/RIS-integrated speech recognition system on radiology reporting time and report availability].

PURPOSE: Quantification of the impact of a PACS/RIS-integrated speech recognition system (SRS) on the time expenditure for radiology reporting and on hospital-wide report availability (RA) in a university institution. MATERIAL AND METHODS: In a prospective pilot study, the following parameters were assessed for 669 radiographic examinations (CR): 1. time requirement per report dictation (TED: dictation time (s)/number of images [examination] x number of words [report]) with either a combination of PACS/tape-based dictation (TD: analog dictation device/mini-cassette/transcription) or PACS/RIS/speech recognition system (RR: remote recognition/transcription and OR: online recognition/self-correction by radiologist), respectively, and 2. the Report Turnaround Time (RTT) as the time interval from the entry of the first image into the PACS to the available RIS/HIS report. Two equal time periods were chosen retrospectively from the RIS database: 11/2002 - 2/2003 (only TD) and 11/2003 - 2/2004 (only RR or OR with speech recognition system [SRS]). The mid-term (> or = 24 h, 24 h intervals) and short-term (< 24 h, 1 h intervals) RA after examination completion were calculated for all modalities and for CR, CT, MR and XA/DS separately. The relative increase in the mid-term RA (RIMRA: related to total number of examinations in each time period) and increase in the short-term RA (ISRA: ratio of available reports during the 1st to 24th hour) were calculated. RESULTS: Prospectively, there was a significant difference between TD/RR/OR (n = 151/257/261) regarding mean TED (0.44/0.54/0.62 s [per word and image]) and mean RTT (10.47/6.65/1.27 h), respectively. Retrospectively, 37 898/39 680 reports were computed from the RIS database for the time periods of 11/2002 - 2/2003 and 11/2003 - 2/2004. For CR/CT there was a shift of the short-term RA to the first 6 hours after examination completion (mean cumulative RA 20 % higher) with a more than three-fold increase in the total number of available reports within 24 hours (all modalities). The RIMRA for CR/CT/MR was 3.1/5.8/4.0 in the first 24 hours, and 2.0 for XA/DS in the second 24-hour interval. CONCLUSION: In comparison to tape-based dictation, an SRS results in a significantly higher primary time expenditure and a modified report dictation workflow. In a university institution, a PACS/RIS-integrated SRS achieves a marked improvement in both short- and mid-term RA which eventually results in an improvement in patient care.

Efficiency, Organizational↗