Lifeline of New Products- Destination: The Patient\u27s Bedside

The lifeline of a new product begins with an innovative process driven by an on-line request through our academic medical center’s intranet system. The individual championing the new product completes the request. Staff nurses have a voice in the decision making process of the acceptance or the rejection of new product(s) being used in a hospital setting. All requested products are thoroughly investigated as the item relates to new technology, cost, reimbursement, medical evidence and evidence-based practice. These critical factors assist in the decision making process to improve the quality of patient care while ensuring cost-containment and standardization, thereby, translating into positive patient outcomes. A nurse educator is intimately involved in the Product Evaluation and Implementation Process. As the nurse coordinating the evaluation of a product, there is opportunity to work in concert with supply chain/contract buyers, inventory control/distribution managers, requestors and the sales representatives to produce an efficient evaluation. Clinical effectiveness, quality and acceptance are criteria used to evaluate the product. Once the evaluation period is complete, data is collected and analyzed. A summary report is generated and shared electronically with the appropriate organizational representatives. Based upon the results the product is either accepted or rejected. If a product is accepted, the process moves onto the implementation phase. The nursing website and the hospital intranet is used to provide the nursing staff with Product Announcements, Product Information, Summary of Committee Actions and communicates in what phase the product rests. Presented at: National Conference: National Nursing Staff Development Organization 2009 Conference, July 8-12, 2009, Philadelphia, PA

Delivering on the Promise of Support for Growth? Evaluator Perceptions of a New State Teacher Evaluation System

This cross-case synthesis gives voice to evaluators in EC-12 and higher education settings who are enacting a state-mandated system of teacher evaluation and support by examining their perceptions of the Texas Teacher Evaluation and Support System (T-TESS). Questions addressed included: How do differently situated school administrators and supervisors 1) understand the model, 2) describe the implementation of its elements, 3) understand and enact their roles, and 4) assess the impact of the model? Data from EC-12 school principals and clinical supervisors at the university level indicates the system establishes a comprehensive definition of quality teaching. However, model complexity creates challenges. Coaching and mentoring requires time and expertise, and impact on student learning is unclear, raising the question of whether there is space for support through supervision in a model also used for accountability. Combining support with a reified model of evaluation leaves evaluators to negotiate inherent tensions

Neural encoding of voice pitch and formant structure at birth as revealed by frequency-following responses

Detailed neural encoding of voice pitch and formant structure plays a crucial role in speech perception, and is of key importance for an appropriate acquisition of the phonetic repertoire in infants since birth. However, the extent to what newborns are capable of extracting pitch and formant structure information from the temporal envelope and the temporal fine structure of speech sounds, respectively, remains unclear. Here, we recorded the frequency-following response (FFR) elicited by a novel two-vowel, rising-pitch-ending stimulus to simultaneously characterize voice pitch and formant structure encoding accuracy in a sample of neonates and adults. Data revealed that newborns tracked changes in voice pitch reliably and no differently than adults, but exhibited weaker signatures of formant structure encoding, particularly at higher formant frequency ranges. Thus, our results indicate a well-developed encoding of voice pitch at birth, while formant structure representation is maturing in a frequency-dependent manner. Furthermore, we demonstrate the feasibility to assess voice pitch and formant structure encoding within clinical evaluation times in a hospital setting, and suggest the possibility to use this novel stimulus as a tool for longitudinal developmental studies of the auditory system

A database and digital signal processing framework for the perceptual analysis of voice quality

Bermúdez de Alvear RM, Corral J, Tardón LJ, Barbancho AM, Fernández Contreras E, Rando Márquez S, Martínez-Arquero AG, Barbancho I. A database and digital signal processing framework for the perceptual analysis of voice quality. Pan European Voice Conferenc: PEVOC 11 Abstract Book. Aug. 31-Sept.2, 2015.Introduction. Clinical assessment of dysphonia relies on perceptual as much as instrumental methods of analysis [1]. The perceptual auditory analysis is potentially subject to several internal and external sources of bias [2]. Furthermore acoustic analyses which have been used to objectively characterize pathological voices are likely to be affected by confusion variables such as the signal processing or the hardware and software specifications [3]. For these reasons the poor correlation between perceptual ratings and acoustic measures remains to be a controversial matter [4]. The availability of annotated databases of voice samples is therefore of main importance for clinical and research purposes. Databases to perform digital processing of the vocal signal are usually built from English speaking subjects’ sustained vowels [5]. However phonemes vary from one language to another and to the best of our knowledge there are no annotated databases with Spanish sustained vowels from healthy or dysphonic voices. This work shows our first steps to fill in this gap. For the aim of aiding clinicians and researchers in the perceptual assessment of voice quality a two-fold objective was attained. On the one hand a database of healthy and disordered Spanish voices was developed; on the other an automatic analysis scheme was accomplished on the basis of signal processing algorithms and supervised learning machine techniques. Material and methods. A preliminary annotated database was created with 119 recordings of the sustained Spanish /a/; they were perceptually labeled by three experienced experts in vocal quality analysis. It is freely available under Links in the ATIC website (www.atic.uma.es). Voice signals were recorded using a headset condenser cardioid microphone (AKG C-544 L) positioned at 5 cm from the speaker’s mouth commissure. Speakers were instructed to sustain the Spanish vowel /a/ for 4 seconds. The microphone was connected to a digital recorder Edirol R-09HR. Voice signals were digitized at 16 bits with 44100 Hz sampling rate. Afterwards the initial and last 0.5 second segments were cut and the 3 sec. mid portion was selected for acoustic analysis. Sennheiser HD219 headphones were used by judges to perceptually evaluate voice samples. To label these recordings raters used the Grade-Roughness-Breathiness (GRB) perceptual scale which is a modified version of the original Hirano’s GRBAS scale, posteriorly modified by Dejonckere et al., [6]. In order to improve intra- and inter-raters’ agreement two types of modifications were introduced in the rating procedure, i.e. the 0-3 points scale resolution was increased by adding subintervals to the standard 0-3 intervals, and judges were provided with a written protocol with explicit definitions about the subintervals boundaries. By this way judges could compensate for the potential instability that might occur in their internal representations due to the perceptual context influence [7]. Raters’ perceptual evaluations were simultaneously performed by means of connecting the Sennheiser HD219 headphones to a multi-channel headphone preamp Behringer HA4700 Powerplay Pro-XL. The Yin algorithm [8] was selected as initial front-end to identify voiced frames and extract their fundamental frequency. For the digital processing of voice signals some conventional acoustic parameters [6] were selected. To complete the analysis the Mel-Frequency Cepstral Coefficients (MFCC) were further calculated because they are based on the auditory model and they are thus closer to the auditory system response than conventional features. Results. In the perceptual evaluation excellent intra-raters agreement and very good inter-raters agreement were achieved. During the supervised machine learning stage some conventional features were found to attain unexpected low performance in the classification scheme selected. Mel Frequency Cepstral Coefficients were promising for assorting samples with normal or quasi-normal voice quality. Discussion and conclusions. Despite it is still small and unbalanced the present annotated data base of voice samples can provide a basis for the development of other databases and automatic classification tools. Other authors [9, 10, 11] also found that modeling the auditory non-linear response during signal processing can help develop objective measures that better correspond with perceptual data. However highly disordered voices classification remains to be a challenge for this set of features since they cannot be correctly assorted by either conventional variables or the auditory model based measures. Current results warrant further research in order to find out the usability of other types of voice samples and features for the automatic classification schemes. Different digital processing steps could be used to improve the classifiers performance. Additionally other types of classifiers could be taken into account in future studies. Acknowledgment. This work was funded by the Spanish Ministerio de Economía y Competitividad, Project No. TIN2013-47276-C6-2-R has been done in the Campus de Excelencia Internacional Andalucía Tech, Universidad de Málaga. References [1] Carding PN, Wilson JA, MacKenzie K, Deary IJ. Measuring voice outcomes: state of the science review. The Journal of Laryngology and Otology 2009;123,8:823-829. [2] Oates J. Auditory-perceptual evaluation of disordered voice quality: pros, cons and future directions. Folia Phoniatrica et Logopaedica 2009;61,1:49-56. [3] Maryn et al. Meta-analysis on acoustic voice quality measures. J Acoust Soc Am 2009; 126, 5: 2619-2634. [4] Vaz Freitas et al. Correlation Between Acoustic and Audio-Perceptual Measures. J Voice 2015;29,3:390.e1 [5] “Multi-Dimensional Voice Program (MDVP) Model 5105. Software Instruction Manual”, Kay PENTAX, A Division of PENTAX Medical Company, 2 Bridgewater Lane, Lincoln Park, NJ 07035-1488 USA, November 2007. [6] Dejonckere PH, Bradley P, Clemente P, Cornut G, Crevier-Buchman L, Friedrich G, Van De Heyning P, Remacle M, Woisard V. A basic protocol for functional assessment of voice pathology, especially for investigating the efficacy of (phonosurgical) treatments and evaluating new assessment techniques. Guideline elaborated by the Comm. on Phoniatrics of the European Laryngological Society (ELS). Eur Arch Otorhinolaryngol 2001;258:77–82. [7] Kreiman et al. Voice Quality Perception. J Speech Hear Res 1993;36:21-4 [8] De Cheveigné A, Kawahara H. YIN, a fundamental frequency estimator for speech and music. J. Acoust. Soc. Amer. 202; 111,4:1917. [9] Shrivastav et al. Measuring breathiness. J Acoust Soc Am 2003;114,4:2217-2224. [10] Saenz-Lechon et al. Automatic Assessment of voice quality according to the GRBAS scale. Eng Med Biol Soc Ann 2006;1:2478-2481. [11] Fredouille et al. Back-and-forth methodology for objective voice quality assessment: from/to expert knowledge to/from automatic classification of dysphonia. EURASIP J Appl Si Pr 2009.Campus de Excelencia Internacional Andalucía Tech, Universidad de Málaga. Ministerio de Economía y Competitividad, Projecto No. TIN2013-47276-C6-2-R

Voice and speech functions (B310-B340)

The International Classification of Functioning, Disability and Health for Children and Youth (ICF-CY) domain ‘voice and speech functions’ (b3) includes production and quality of voice (b310), articulation functions (b320), fluency and rhythm of speech (b330) and alternative vocalizations (b340, such as making musical sounds and crying, which are not reviewed here)

A systematic review of speech recognition technology in health care

BACKGROUND To undertake a systematic review of existing literature relating to speech recognition technology and its application within health care. METHODS A systematic review of existing literature from 2000 was undertaken. Inclusion criteria were: all papers that referred to speech recognition (SR) in health care settings, used by health professionals (allied health, medicine, nursing, technical or support staff), with an evaluation or patient or staff outcomes. Experimental and non-experimental designs were considered. Six databases (Ebscohost including CINAHL, EMBASE, MEDLINE including the Cochrane Database of Systematic Reviews, OVID Technologies, PreMED-LINE, PsycINFO) were searched by a qualified health librarian trained in systematic review searches initially capturing 1,730 references. Fourteen studies met the inclusion criteria and were retained. RESULTS The heterogeneity of the studies made comparative analysis and synthesis of the data challenging resulting in a narrative presentation of the results. SR, although not as accurate as human transcription, does deliver reduced turnaround times for reporting and cost-effective reporting, although equivocal evidence of improved workflow processes. CONCLUSIONS SR systems have substantial benefits and should be considered in light of the cost and selection of the SR system, training requirements, length of the transcription task, potential use of macros and templates, the presence of accented voices or experienced and in-experienced typists, and workflow patterns.Funding for this study was provided by the University of Western Sydney. NICTA is funded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Centre of Excellence Program. NICTA is also funded and supported by the Australian Capital Territory, the New South Wales, Queensland and Victorian Governments, the Australian National University, the University of New South Wales, the University of Melbourne, the University of Queensland, the University of Sydney, Griffith University, Queensland University of Technology, Monash University and other university partners

Patient-based mobile alerting systems- requirements and expectations

Patients with chronic conditions are not well supported by technical systems in managing their conditions. However, such systems could help patients to self-reliantly comply with their treatment. This help could be rendered in the form of alerting patients about condition-relevant issues, transmitting relevant parameters to healthcare providers and analysing these parameters according to guidelines specified by both patients and healthcare staff. If necessary, this analysis of condition parameters triggers the alerting of patients and healthcare providers about actions to be taken. In this paper, we present the results of a survey we have undertaken to verify and extend requirements we have identified for the design of a Mobile Alerting System for patients with chronic conditions. First of all, the results show that a Mobile Alerting System is desired by patients. Moreover, due to the inter- and intra-user variance of patients and healthcare staff, the system has to work in a context-aware manner and allow for personalised parameters in order to be adaptable to every user’s needs