Electronic access to care system: improving patient's access to clinical information through an Interactive Voice Response (IVR) system and Web portal.
Our clinical providers spend an estimated four hours weekly answering phone messages from patients. Our nurses spend five to ten hours weekly on returning phone calls. Most of this time is spent conveying recent clinical results, reviewing with patients the discharge instructions such as consults or studies ordered during the office visits, and handling patients' requests for medication renewals. Over time this will lead to greater patients' dissatisfaction because of lengthy waiting time and lack of timely access to their medical information. This would also lead to greater nursing and providers' dissatisfaction because of unreasonable work load. (+info)
Usage, performance, and satisfaction outcomes for experienced users of automatic speech recognition.
This paper presents a variety of outcomes data from 24 experienced users of automatic speech recognition (ASR) as a means of computer access. To assess usage and satisfaction, we conducted an in-person survey interview. For those participants who had a choice of computer input methods, 48% reported using ASR for 25% or less of their computer tasks, while 37% used ASR for more than half of their computer tasks. Users' overall satisfaction with ASR was somewhat above neutral (averaging 63 out of 100), and the most important role for ASR was as a means of reducing upper-limb pain and fatigue. To measure user performance, we asked users to perform a series of word processing and operating system tasks with their ASR systems. For 18 of these users, performance without speech was also measured. The time for nontext tasks was significantly slower with speech (p < 0.05). The average rate for entering text was no different with or without speech. Text entry rate with speech varied widely, from 3 to 32 words per minute, as did recognition accuracy, from 72% to 94%. Users who had the best performance tended to be those who employed the best correction strategies while using ASR. (+info)
Effect of high-frequency spectral components in computer recognition of dysarthric speech based on a Mel-cepstral stochastic model.
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, a hidden Markov model (HMM) was constructed and conditions investigated that would provide improved performance for a dysarthric speech (isolated word) recognition system intended to act as an assistive/control tool. In particular, we investigated the effect of high-frequency spectral components on the recognition rate of the system to determine if they contributed useful additional information to the system. A small-size vocabulary spoken by three cerebral palsy subjects was chosen. Mel-frequency cepstral coefficients extracted with the use of 15 ms frames served as training input to an ergodic HMM setup. Subsequent results demonstrated that no significant useful information was available to the system for enhancing its ability to discriminate dysarthric speech above 5.5 kHz in the current set of dysarthric data. The level of variability in input dysarthric speech patterns limits the reliability of the system. However, its application as a rehabilitation/control tool to assist dysarthric motor-impaired individuals such as cerebral palsy subjects holds sufficient promise. (+info)
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. (+info)
Virtual vocalization stimuli for investigating neural representations of species-specific vocalizations.
Most studies investigating neural representations of species-specific vocalizations in non-human primates and other species have involved studying neural responses to vocalization tokens. One limitation of such approaches is the difficulty in determining which acoustical features of vocalizations evoke neural responses. Traditionally used filtering techniques are often inadequate in manipulating features of complex vocalizations. Furthermore, the use of vocalization tokens cannot fully account for intrinsic stochastic variations of vocalizations that are crucial in understanding the neural codes for categorizing and discriminating vocalizations differing along multiple feature dimensions. In this work, we have taken a rigorous and novel approach to the study of species-specific vocalization processing by creating parametric "virtual vocalization" models of major call types produced by the common marmoset (Callithrix jacchus). The main findings are as follows. 1) Acoustical parameters were measured from a database of the four major call types of the common marmoset. This database was obtained from eight different individuals, and for each individual, we typically obtained hundreds of samples of each major call type. 2) These feature measurements were employed to parameterize models defining representative virtual vocalizations of each call type for each of the eight animals as well as an overall species-representative virtual vocalization averaged across individuals for each call type. 3) Using the same feature-measurement that was applied to the vocalization samples, we measured acoustical features of the virtual vocalizations, including features not explicitly modeled and found the virtual vocalizations to be statistically representative of the callers and call types. 4) The accuracy of the virtual vocalizations was further confirmed by comparing neural responses to real and synthetic virtual vocalizations recorded from awake marmoset auditory cortex. We found a strong agreement between the responses to token vocalizations and their synthetic counterparts. 5) We demonstrated how these virtual vocalization stimuli could be employed to precisely and quantitatively define the notion of vocalization "selectivity" by using stimuli with parameter values both within and outside the naturally occurring ranges. We also showed the potential of the virtual vocalization stimuli in studying issues related to vocalization categorizations by morphing between different call types and individual callers. (+info)
Six characteristics of effective structured reporting and the inevitable integration with speech recognition.
The reporting of radiological images is undergoing dramatic changes due to the introduction of two new technologies: structured reporting and speech recognition. Each technology has its own unique advantages. The highly organized content of structured reporting facilitates data mining and billing, whereas speech recognition offers a natural succession from the traditional dictation-transcription process. This article clarifies the distinction between the process and outcome of structured reporting, describes fundamental requirements for any effective structured reporting system, and describes the potential development of a novel, easy-to-use, customizable structured reporting system that incorporates speech recognition. This system should have all the advantages derived from structured reporting, accommodate a wide variety of user needs, and incorporate speech recognition as a natural component and extension of the overall reporting process. (+info)
The use of additionally trained sonographers as ultrasound practitioners: our first-year experience.
OBJECTIVE: Two sonographers were trained to help manage an abrupt, permanent increase in the number of ultrasound examinations in our department. Called "ultrasound practitioners," they functioned as physician assistants and triaged 20 to 30 cases per day, allowing the cases to be batch read at a formal reading at day's end. We report our first-year experience with this program. METHODS: Two sonographers with 10 and 30 years of experience, respectively, were trained to triage and dictate cases. Once trained, they triaged the cases of 20 to 30 patients per day. Reports were predictated with voice recognition technology. A radiologist was always readily available to provide support, and consultation with a radiologist was always obtained for the infrequent verbal reports that were requested. Reports from the practitioner were graded subjectively on a 4-point scale for the first year, according to the modification required at formal readout (A, no change; B, minor change not affecting patient care; C, moderate change not affecting care in a dramatic way; and D, major change markedly affecting care). RESULTS: Practitioner 1 monitored the examinations of 2858 patients. The graded report results were as follows: A, 96.2%; B, 3.5%; C, 0.3%; and D, 0.00%. Practitioner 2 monitored the examinations of 2825 patients. The graded report results were as follows: A, 96.1%; B, 3.6%; C, 0.2%; and D, 0.00%. There were no category D reports. CONCLUSIONS: The results far exceeded expectations, with a very low rate of category B and C reports and an absence of category D reports. The practitioners allowed the cases of 20 to 30 patients to be batch read by the existing radiologist staff at the end of the day. (+info)
Interactive voice response telephone calls to enhance bone mineral density testing.
OBJECTIVE: Bone mineral density (BMD) testing is a key tool used to diagnose and treat osteoporosis. We assessed the rate of scheduling BMD tests among health plan members at risk for osteoporosis who received interactive voice response (IVR) calls. STUDY DESIGN: Cohort study. METHODS: Study patients included persons age 45 years with either a prior fracture or 90 days of glucocorticoid use and all women age 65 years during the 2-year baseline period. The IVR call provided educational content and then offered members an opportunity to transfer to schedule a BMD test. The primary outcome was scheduling a BMD test. RESULTS: We targeted 1402 health plan members, and 708 (50%) were successfully contacted. Of 54 patients who transferred to schedule a BMD test, only 3 actually did so. Because so few patients scheduled a BMD test, predictors of transfer were examined as a secondary end point. In a multivariate model, only self-reported intention to schedule a BMD test was a significant predictor (odds ratio = 4.4, 95% confidence interval = 2.2, 8.8). Members' age, sex, history of a prior fracture, self-report of a BMD test in the previous 2 years, acknowledgement of barriers to BMD testing, and discussion of BMD testing with one's physician were not related to transferring to schedule a BMD test. CONCLUSION: A letter and an IVR call prompted few to schedule a BMD test. More interventions to improve BMD testing should be developed and tested. (+info)