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In 2018, an estimated 100 million voice-controlled devices were installed in homes worldwide, and the apps that control them, like Amazon Alexa and Google Assistant, are getting more powerful, with new skills being added every day. Great voice apps improve how users interact with the web, whether they're checking the weather, asking for sports scores, or playing a game. \"Voice applications for Alexa and Google Assistant\" is your guide to designing, building, and implementing voice-based applications for Alexa and Google Assistant. You'll learn to build applications that listen to users, store information, and rely on user context, as you create a voice-powered sleep tracker from scratch. With the basics mastered, you'll dig deeper into multiuse conversational flow and other more-advanced concepts. Smaller projects along the way reinforce your new techniques and best practices.

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. 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. 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. 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). 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.

BackgroundHospitalization 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.ObjectiveTo assess smokers’ use of and satisfaction with an IVR-facilitated intervention and to test the relationship between intervention dose and smoking cessation.DesignAnalysis of pooled quantitative and qualitative data from the intervention groups of two similar randomized controlled trials with 6-month follow-up.ParticipantsA total of 878 smokers admitted to three hospitals. All received cessation counseling in hospital and planned to stop smoking after discharge.InterventionAfter 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 MeasuresNumber of IVR calls answered, patient satisfaction, biochemically validated tobacco abstinence 6 months after discharge.Key ResultsParticipants 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.ConclusionsAutomated 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.

\"Whether you'll use Alexa to send text messages, play music, control your thermostat, look up recipes, replenish your pantry, or just search the internet for information, you'll find detailed instructions in this fun and easy-to-understand guide. Amazon's hugely popular family of Echo devices has made Alexa a household name. She can answer your questions, entertain you, and even help around the house. Alexa for Dummies is the perfect guide for Alexa users who want to get up and running with their Echo devices. From basic setup to making the most of Alexa's powerful smart home capabilities, this is your one-stop resource to all things Alexa. Set up and personalize your Alexa device with an Amazon account and custom settings, including your preferred Alexa voice. Use Alexa to play music throughout your home, stream videos online, and meet all your entertainment needs. Unlock the power of advanced features like Alexa Skills and make your Alexa accessible. Turn your ordinary house into a modern smart home with advanced smart home features and Echo accessories. The virtual assistant you've dreamed of is now a reality with your favorite Echo device.\"--Publisher's description.

Recent advances in natural language processing and artificial intelligence have led to widespread adoption of speech recognition technologies. In consumer health applications, speech recognition is usually applied to support interactions with conversational agents for data collection, decision support, and patient monitoring. However, little is known about the use of speech recognition in consumer health applications and few studies have evaluated the efficacy of conversational agents in the hands of consumers. In other consumer-facing tools, cognitive load has been observed to be an important factor affecting the use of speech recognition technologies in tasks involving problem solving and recall. Users find it more difficult to think and speak at the same time when compared to typing, pointing, and clicking. However, the effects of speech recognition on cognitive load when performing health tasks has not yet been explored. The aim of this study was to evaluate the use of speech recognition for documentation in consumer digital health tasks involving problem solving and recall. Fifty university staff and students were recruited to undertake four documentation tasks with a simulated conversational agent in a computer laboratory. The tasks varied in complexity determined by the amount of problem solving and recall required (simple and complex) and the input modality (speech recognition vs keyboard and mouse). Cognitive load, task completion time, error rate, and usability were measured. Compared to using a keyboard and mouse, speech recognition significantly increased the cognitive load for complex tasks (Z=-4.08, P<.001) and simple tasks (Z=-2.24, P=.03). Complex tasks took significantly longer to complete (Z=-2.52, P=.01) and speech recognition was found to be overall less usable than a keyboard and mouse (Z=-3.30, P=.001). However, there was no effect on errors. Use of a keyboard and mouse was preferable to speech recognition for complex tasks involving problem solving and recall. Further studies using a broader variety of consumer digital health tasks of varying complexity are needed to investigate the contexts in which use of speech recognition is most appropriate. The effects of cognitive load on task performance and its significance also need to be investigated.

Augmented reality (AR) blurs the boundary between the physical and digital worlds. In AR's current exploration phase, innovators are beginning to create compelling and contextually rich applications that enhance a user's everyday experiences. In this book, Dr. Helen Papagiannis, a world leading expert in the field, introduces you to AR: how it's evolving, where the opportunities are, and where it's headed.

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.

Background Speech recognition software might increase productivity in clinical documentation. However, low user satisfaction with speech recognition software has been observed. In this case study, an approach for implementing a speech recognition software package at a university-based outpatient department is presented. Methods Methods to create a specific dictionary for the context “sports medicine” and a shared vocabulary learning function are demonstrated. The approach is evaluated for user satisfaction (using a questionnaire before and 10 weeks after software implementation) and its impact on the time until the final medical document was saved into the system. Results As a result of implementing speech recognition software, the user satisfaction was not remarkably impaired. The median time until the final medical document was saved was reduced from 8 to 4 days. Conclusion In summary, this case study illustrates how speech recognition can be implemented successfully when the user experience is emphasised.

The high demand for health care services and the growing capability of artificial intelligence have led to the development of conversational agents designed to support a variety of health-related activities, including behavior change, treatment support, health monitoring, training, triage, and screening support. Automation of these tasks could free clinicians to focus on more complex work and increase the accessibility to health care services for the public. An overarching assessment of the acceptability, usability, and effectiveness of these agents in health care is needed to collate the evidence so that future development can target areas for improvement and potential for sustainable adoption. This systematic review aims to assess the effectiveness and usability of conversational agents in health care and identify the elements that users like and dislike to inform future research and development of these agents. PubMed, Medline (Ovid), EMBASE (Excerpta Medica dataBASE), CINAHL (Cumulative Index to Nursing and Allied Health Literature), Web of Science, and the Association for Computing Machinery Digital Library were systematically searched for articles published since 2008 that evaluated unconstrained natural language processing conversational agents used in health care. EndNote (version X9, Clarivate Analytics) reference management software was used for initial screening, and full-text screening was conducted by 1 reviewer. Data were extracted, and the risk of bias was assessed by one reviewer and validated by another. A total of 31 studies were selected and included a variety of conversational agents, including 14 chatbots (2 of which were voice chatbots), 6 embodied conversational agents (3 of which were interactive voice response calls, virtual patients, and speech recognition screening systems), 1 contextual question-answering agent, and 1 voice recognition triage system. Overall, the evidence reported was mostly positive or mixed. Usability and satisfaction performed well (27/30 and 26/31), and positive or mixed effectiveness was found in three-quarters of the studies (23/30). However, there were several limitations of the agents highlighted in specific qualitative feedback. The studies generally reported positive or mixed evidence for the effectiveness, usability, and satisfactoriness of the conversational agents investigated, but qualitative user perceptions were more mixed. The quality of many of the studies was limited, and improved study design and reporting are necessary to more accurately evaluate the usefulness of the agents in health care and identify key areas for improvement. Further research should also analyze the cost-effectiveness, privacy, and security of the agents. RR2-10.2196/16934.