Interactive Voice Response Calls to Promote Smoking Cessation after Hospital Discharge: Pooled Analysis of Two Randomized Clinical Trials.

BACKGROUND: Hospitalization offers smokers an opportunity to quit smoking. Starting cessation treatment in hospital is effective, but sustaining treatment after discharge is a challenge. Automated telephone calls with interactive voice response (IVR) technology could support treatment continuance after discharge. OBJECTIVE: To assess smokers' use of and satisfaction with an IVR-facilitated intervention and to test the relationship between intervention dose and smoking cessation. DESIGN: Analysis of pooled quantitative and qualitative data from the intervention groups of two similar randomized controlled trials with 6-month follow-up. PARTICIPANTS: A total of 878 smokers admitted to three hospitals. All received cessation counseling in hospital and planned to stop smoking after discharge. INTERVENTION: After discharge, participants received free cessation medication and five automated IVR calls over 3 months. Calls delivered messages promoting smoking cessation and medication adherence, offered medication refills, and triaged smokers to additional telephone counseling. MAIN MEASURES: Number of IVR calls answered, patient satisfaction, biochemically validated tobacco abstinence 6 months after discharge. KEY RESULTS: Participants answered a median of three of five IVR calls; 70% rated the calls as helpful, citing the social support, access to counseling and medication, and reminders to quit as positive factors. Older smokers (OR 1.36, 95% CI 1.20-1.54 per decade) and smokers hospitalized for a smoking-related disease (OR 1.65, 95% CI 1.21-2.23) completed more calls. Smokers who completed more calls had higher quit rates at 6-month follow-up (OR 1.49, 95% CI 1.30-1.70, for each additional call) after multivariable adjustment for age, sex, education, discharge diagnosis, nicotine dependence, duration of medication use, and perceived importance of and confidence in quitting. CONCLUSIONS: Automated IVR calls to support smoking cessation after hospital discharge were viewed favorably by patients. Higher IVR utilization was associated with higher odds of tobacco abstinence at 6-month follow-up. IVR technology offers health care systems a potentially scalable means of sustaining tobacco cessation interventions after hospital discharge. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifiers NCT01177176, NCT01714323.

Assessment of an interactive voice response system for identifying falls in a statewide sample of older adults.

OBJECTIVE: Interactive voice response (IVR) systems offer great advantages for data collection in large, geographically dispersed samples involving frequent contact. We assessed the quality of IVR data collected from older respondents participating in a statewide falls prevention program evaluation in Pennsylvania in 2010-12. METHOD: Participants (n=1834) were followed up monthly for up to 10 months to compare respondents who completed all, some, or no assessments in the IVR system. Validity was assessed by examining IVR-reported falls incidence relative to baseline in-person self-report and performance assessment of balance. RESULTS: While a third of the sample switched from IVR to in-person calls over follow-up, IVR interviews were successfully used to complete 68.1% of completed monthly assessments (10,511/15,430). Switching to in-person interviews was not associated with measures of participant function or cognition. Both self-reported (p<.0001) and performance assessment of balance (p=.05) at baseline were related to falls incidence. CONCLUSION: IVR is a productive modality for falls research among older adults. Future research should establish what level of initial personal research contact is optimal for boosting IVR completion rates and what research domains are most appropriate for this kind of contact.

The accuracy of radiology speech recognition reports in a multilingual South African teaching hospital.

BACKGROUND: Speech recognition (SR) technology, the process whereby spoken words are converted to digital text, has been used in radiology reporting since 1981. It was initially anticipated that SR would dominate radiology reporting, with claims of up to 99% accuracy, reduced turnaround times and significant cost savings. However, expectations have not yet been realised. The limited data available suggest SR reports have significantly higher levels of inaccuracy than traditional dictation transcription (DT) reports, as well as incurring greater aggregate costs. There has been little work on the clinical significance of such errors, however, and little is known of the impact of reporter seniority on the generation of errors, or the influence of system familiarity on reducing error rates. Furthermore, there have been conflicting findings on the accuracy of SR amongst users with English as first- and second-language respectively. METHODS: The aim of the study was to compare the accuracy of SR and DT reports in a resource-limited setting. The first 300 SR and the first 300 DT reports generated during March 2010 were retrieved from the hospital's PACS, and reviewed by a single observer. Text errors were identified, and then classified as either clinically significant or insignificant based on their potential impact on patient management. In addition, a follow-up analysis was conducted exactly 4 years later. RESULTS: Of the original 300 SR reports analysed, 25.6% contained errors, with 9.6% being clinically significant. Only 9.3% of the DT reports contained errors, 2.3% having potential clinical impact. Both the overall difference in SR and DT error rates, and the difference in 'clinically significant' error rates (9.6% vs. 2.3%) were statistically significant. In the follow-up study, the overall SR error rate was strikingly similar at 24.3%, 6% being clinically significant. Radiologists with second-language English were more likely to generate reports containing errors, but level of seniority had no bearing. CONCLUSION: SR technology consistently increased inaccuracies in Tygerberg Hospital (TBH) radiology reports, thereby potentially compromising patient care. Awareness of increased error rates in SR reports, particularly amongst those transcribing in a second-language, is important for effective implementation of SR in a multilingual healthcare environment.

Text-to-speech accommodations for the reading challenges of adults with traumatic brain injury.

OBJECTIVE: This study's purpose was two-fold: (a) to confirm differences in silent reading rates of individuals with and without traumatic brain injury (TBI) and (b) to determine the effect of text-to-speech (TTS) on reading comprehension and efficiency by individuals with TBI. DESIGN AND METHODS: Ten adults with severe TBI answered comprehension questions about written passages presented in three conditions: reading only (RO), listening to TTS presentation only (LO) or reading and listening to TTS simultaneously (RL). The researchers compared reading rate, comprehension accuracy and comprehension rate (efficiency) across conditions. RESULTS: Analysis revealed significantly slower silent reading rates for the participants with TBI than for readers without TBI (n = 75). Also, participants with TBI achieved higher comprehension accuracy for factual than inferential questions; however, no significant main effect for comprehension accuracy emerged across reading conditions. In contrast, using comprehension rate as the dependent measure, analysis confirmed a significant main effect for reading condition and question type; post-hoc pairwise comparisons revealed that the RL condition yielded higher comprehension rate scores than the RO condition. CONCLUSIONS: As a group, adults with TBI appear to benefit in reading efficiency when simultaneously listening to and reading written passages; however, differences exist that reinforce the importance of individualizing treatment.

Analysis of Documentation Speed Using Web-Based Medical Speech Recognition Technology: Randomized Controlled Trial.

BACKGROUND: Clinical documentation has undergone a change due to the usage of electronic health records. The core element is to capture clinical findings and document therapy electronically. Health care personnel spend a significant portion of their time on the computer. Alternatives to self-typing, such as speech recognition, are currently believed to increase documentation efficiency and quality, as well as satisfaction of health professionals while accomplishing clinical documentation, but few studies in this area have been published to date. OBJECTIVE: This study describes the effects of using a Web-based medical speech recognition system for clinical documentation in a university hospital on (1) documentation speed, (2) document length, and (3) physician satisfaction. METHODS: Reports of 28 physicians were randomized to be created with (intervention) or without (control) the assistance of a Web-based system of medical automatic speech recognition (ASR) in the German language. The documentation was entered into a browser's text area and the time to complete the documentation including all necessary corrections, correction effort, number of characters, and mood of participant were stored in a database. The underlying time comprised text entering, text correction, and finalization of the documentation event. Participants self-assessed their moods on a scale of 1-3 (1=good, 2=moderate, 3=bad). Statistical analysis was done using permutation tests. RESULTS: The number of clinical reports eligible for further analysis stood at 1455. Out of 1455 reports, 718 (49.35%) were assisted by ASR and 737 (50.65%) were not assisted by ASR. Average documentation speed without ASR was 173 (SD 101) characters per minute, while it was 217 (SD 120) characters per minute using ASR. The overall increase in documentation speed through Web-based ASR assistance was 26% (P=.04). Participants documented an average of 356 (SD 388) characters per report when not assisted by ASR and 649 (SD 561) characters per report when assisted by ASR. Participants' average mood rating was 1.3 (SD 0.6) using ASR assistance compared to 1.6 (SD 0.7) without ASR assistance (P<.001). CONCLUSIONS: We conclude that medical documentation with the assistance of Web-based speech recognition leads to an increase in documentation speed, document length, and participant mood when compared to self-typing. Speech recognition is a meaningful and effective tool for the clinical documentation process.

Toward Successful Implementation of Speech Recognition Technology: A Survey of SRT Utilization Issues in Healthcare Settings.

PURPOSE: To evaluate physician utilization of speech recognition technology (SRT) for medical documentation in two hospitals. METHODS: A quantitative survey was used to collect data in the areas of practice, electronic equipment used for documentation, documentation created after providing care, and overall thoughts about and satisfaction with the SRT. The survey sample was from one rural and one urban facility in central Missouri. In addition, qualitative interviews were conducted with a chief medical officer and a physician champion regarding implementation issues, training, choice of SRT, and outcomes from their perspective. RESULTS: Seventy-one (60%) of the anticipated 125 surveys were returned. A total of 16 (23%) participants were practicing in internal medicine and 9 (13%) were practicing in family medicine. Fifty-six (79%) participants used a desktop and 14 (20%) used a laptop (2%) computer. SRT products from Nuance were the dominant SRT used by 59 participants (83%). Windows operating systems (Microsoft, Redmond, WA) was used by more than 58 (82%) of the survey respondents. With regard to user experience, 42 (59%) participants experienced spelling and grammatical errors, 15 (21%) encountered clinical inaccuracy, 9 (13%) experienced word substitution, and 4 (6%) experienced misleading medical information. CONCLUSIONS: This study shows critical issues of inconsistency, unreliability, and dissatisfaction in the functionality and usability of SRT. This merits further attention to improve the functionality and usability of SRT for better adoption within varying healthcare settings.

Alcohol, moods and male-female differences: daily interactive voice response over 6 months.

AIMS: The goal of this study was to better understand the predictive relationship in both directions between negative (anger, sadness) and positive (happiness) moods and alcohol consumption using daily process data among heavy drinkers. METHODS: Longitudinal daily reports of moods, alcohol use and other covariates such as level of stress were assessed over 180 days using interactive voice response telephone technology. Participants were heavy drinkers (majority meeting criteria for alcohol dependence at baseline) recruited through their primary care provider. The sample included 246 (166 men, 80 women) mostly Caucasian adults. Longitudinal statistical models were used to explore the varying associations between number of alcoholic drinks and mood scores the next day and vice versa with gender as a moderator. RESULTS: Increased alcohol use significantly predicted decreased happiness the next day (P < 0.005), more strongly for females than males. Increased anger predicted higher average alcohol use the next day for males only (P < 0.005). CONCLUSION: This daily process study challenges the notion that alcohol use enhances positive mood for both males and females. Our findings also suggest a strong association between anger and alcohol use that is specific to males. Thus, discussions about the effects of drinking on one's feeling of happiness may be beneficial for males and females as well as anger interventions may be especially beneficial for heavy-drinking males.

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.

Enhancing nursing practice by utilizing voice recognition for direct documentation.

Innovative strategies that preserve nursing time for direct patient care activities are needed. This study examined the utility, feasibility, and acceptability of voice recognition (VR) software to document nursing care and patient outcomes in an electronic health record in a simulated nursing care environment. A phase 1 trial included 5 iterative experiments with observations and nurse participant feedback to allow enhancements to the speech detection capabilities and refinement of the technology, software, and processes. Utility ratings improved over time; however, interference on nursing care remained a concern throughout. Nurse participants favored keyboard entry electronic health record, largely due to software and technical issues, but also relative to the culture shift the new technology brings to nursing practice. Successful adoption of VR technology by nursing will be dependent on receptiveness of the nurses and perceived benefits, timely access to education and training, and minimization of barriers to using the software.

A usability framework for speech recognition technologies in clinical handover: a pre-implementation study.

A multi-disciplinary research team is undertaking a trial of speech-to-text (STT) technology for clinical handover management. Speech-to-text technologies allow for the capture of handover data from voice recordings using speech recognition software and systems. The text documents created from this system can be used together with traditional handover notes and checklists to enhance the depth and breadth of data available for clinical decision-making at the point of care and so improve patient care and reduce medical errors. This paper reports on a preliminary study of perceived usability by nurses of speech-to-text technology based on interviews at a "test day" and using a user-task-technology usability framework to explore expectations of nurses of the use of speech-to-text (STT) technology for clinical handover. The results of this study will be used to design field studies to test the use of speech-to-text (STT) technologies at the point of care in several hospital settings.

Learning curve of speech recognition.

Speech recognition (SR) speeds patient care processes by reducing report turnaround times. However, concerns have emerged about prolonged training and an added secretarial burden for radiologists. We assessed how much proofing radiologists who have years of experience with SR and radiologists new to SR must perform, and estimated how quickly the new users become as skilled as the experienced users. We studied SR log entries for 0.25 million reports from 154 radiologists and after careful exclusions, defined a group of 11 experienced radiologists and 71 radiologists new to SR (24,833 and 122,093 reports, respectively). Data were analyzed for sound file and report lengths, character-based error rates, and words unknown to the SR's dictionary. Experienced radiologists corrected 6 characters for each report and for new users, 11. Some users presented a very unfavorable learning curve, with error rates not declining as expected. New users' reports were longer, and data for the experienced users indicates that their reports, initially equally lengthy, shortened over a period of several years. For most radiologists, only minor corrections of dictated reports were necessary. While new users adopted SR quickly, with a subset outperforming experienced users from the start, identification of users struggling with SR will help facilitate troubleshooting and support.

The challenges, opportunities, and imperative of structured reporting in medical imaging.

Despite dramatic innovation in medical imaging and information system technologies, the radiology report has remained stagnant for more than a century. Structured reporting was created in the hopes of addressing well-documented deficiencies in report content and organization but has largely failed in its adoption due to concerns over workflow and productivity. A number of political, economical, and clinical quality-centric initiatives are currently taking place within medicine which will dramatically change the medical landscape including Pay for Performance, Evidence-Based Medicine, and the Physician Quality Reporting Initiative. These will collectively enhance efforts to improve quality in reporting, stimulate new technology development, and counteract the impending threat of commoditization within radiology. Structured reporting offers a number of unique opportunities and advantages over traditional free text reporting and will provide a means for the radiology community to add value to its most important service deliverable the radiology report.

A clinician survey of using speech recognition for clinical documentation in the electronic health record.

OBJECTIVE: To assess the role of speech recognition (SR) technology in clinicians' documentation workflows by examining use of, experience with and opinions about this technology. MATERIALS AND METHODS: We distributed a survey in 2016-2017 to 1731 clinician SR users at two large medical centers in Boston, Massachusetts and Aurora, Colorado. The survey asked about demographic and clinical characteristics, SR use and preferences, perceived accuracy, efficiency, and usability of SR, and overall satisfaction. Associations between outcomes (e.g., satisfaction) and factors (e.g., error prevalence) were measured using ordinal logistic regression. RESULTS: Most respondents (65.3%) had used their SR system for under one year. 75.5% of respondents estimated seeing 10 or fewer errors per dictation, but 19.6% estimated half or more of errors were clinically significant. Although 29.4% of respondents did not include SR among their preferred documentation methods, 78.8% were satisfied with SR, and 77.2% agreed that SR improves efficiency. Satisfaction was associated positively with efficiency and negatively with error prevalence and editing time. Respondents were interested in further training about using SR effectively but expressed concerns regarding software reliability, editing and workflow. DISCUSSION: Compared to other documentation methods (e.g., scribes, templates, typing, traditional dictation), SR has emerged as an effective solution, overcoming limitations inherent in other options and potentially improving efficiency while preserving documentation quality. CONCLUSION: While concerns about SR usability and accuracy persist, clinicians expressed positive opinions about its impact on workflow and efficiency. Faster and better approaches are needed for clinical documentation, and SR is likely to play an important role going forward.

Improvement of report workflow and productivity using speech recognition--a follow-up study.

Speech recognition (SR), available since the 1980s, has only recently become sufficiently reliable to allow utilization in medical environment. This study measured the effect of SR for the radiological dictation process and estimated differences in report turnaround times (RTTs). During the transition from cassette-based reporting to SR, the workflow of 14 radiologists was periodically followed up for 2 years in a university hospital. The sample size was more than 20,000 examinations, and the radiologists were the same throughout the study. A RTT was defined as the time from imaging at the modality to the time when the report was available for the clinician. SR cut down RTTs by 81% and the standard deviation by 83%. The proportion of reports available within 1 h escalated from 26% to 58%. The proportion of reports created by SR increased during a follow-up time of this study from 0% up to 88%. SR decreases turnaround times and may thus speed up the whole patient care process by facilitating online reporting. SR was easily adopted and well accepted by radiologists. Our findings encourage the utilization of SR, which improves the productivity and accelerates the workflow with excellent end-user satisfaction.

Voice recognition dictation: radiologist as transcriptionist.

Continuous voice recognition dictation systems for radiology reporting provide a viable alternative to conventional transcription services with the promise of shorter report turnaround times and increased cost savings. While these benefits may be realized in academic institutions, it is unclear how voice recognition dictation impacts the private practice radiologist who is now faced with the additional task of transcription. In this article, we compare conventional transcription services with a commercially available voice recognition system with the following results: 1) Reports dictated with voice recognition took 50% longer to dictate despite being 24% shorter than those conventionally transcribed, 2) There were 5.1 errors per case, and 90% of all voice recognition dictations contained errors prior to report signoff while 10% of transcribed reports contained errors. 3). After signoff, 35% of VR reports still had errors. Additionally, cost savings using voice recognition systems in non-academic settings may not be realized. Based on average radiologist and transcription salaries, the additional time spent dictating with voice recognition costs an additional $6.10 per case or $76,250.00 yearly. The opportunity costs may be higher. Informally surveyed, all radiologists expressed dissatisfaction with voice recognition with feelings of frustration, and increased fatigue. In summary, in non-academic settings, utilizing radiologists as transcriptionists results in more error ridden radiology reports and increased costs compared with conventional transcription services.

Speech recognition 2008.

Annual hospital patient visits in the United States number almost 300 million, with outpatient visits approaching one billion. While patient visits are growing at a compounding rate of 3% per year, the requirements for documentation are increasing disproportionately to that growth due to heightened regulations and the desire for greater transparency in preparation for pay for performance. Currently, dictation and transcription are the primary means of documenting these visits and populating the patient record with the relevant information. In recent years, healthcare provider organizations have reported dramatic benefits in workflow efficiency and cost-savings by adopting speech recognition technology. The KLAS study reviewed speech recognition systems in an effort to determine the state of the solutions, vendor scoring, physician usage and quantifiable benefits.

Physician experience with speech recognition software in psychiatry: usage and perspective.

OBJECTIVE: The purpose of this paper is to extend a previous study by evaluating the use of a speech recognition software in a clinical psychiatry milieu. Physicians (n = 55) at a psychiatric hospital participated in a limited implementation and were provided with training, licenses, and relevant devices. Post-implementation usage data was collected via the software. Additionally, a post-implementation survey was distributed 5 months after the technology was introduced. RESULTS: In the first month, 45 out of 51 (88%) physicians were active users of the technology; however, after the full evaluation period only 53% were still active. The average active user minutes and the average active user lines dictated per month remained consistent throughout the evaluation. The use of speech recognition software within a psychiatric setting is of value to some physicians. Our results indicate a post-implementation reduction in adoption, with stable usage for physicians who remained active users. Future studies to identify characteristics of users and/or technology that contribute to ongoing use would be of value.