Medication Name Comprehension of Intelligent Virtual Assistants: A Comparison of Amazon Alexa, Google Assistant, and Apple Siri Between 2019 and 2021

Palanica, Adam; Fossat, Yan

doi:10.3389/fdgth.2021.669971

Cited by 15 publications

(5 citation statements)

References 10 publications

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“…Apple Siri showed the lowest response rate. This specific advantage of Google Assistant is consistent with previous studies [27,29,32,38,39]. Moreover, Apple Siri often replied I found this on the web and presented visually a list of results instead of voicing a unique response.…”

Section: Response Rate Across Developers and Modalitiessupporting

confidence: 88%

Reliability of Commercial Voice Assistants’ Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information

Bérubé¹,

Kovacs²,

Fleisch³

et al. 2021

J Med Internet Res

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Background Noncommunicable diseases (NCDs) constitute a burden on public health. These are best controlled through self-management practices, such as self-information. Fostering patients’ access to health-related information through efficient and accessible channels, such as commercial voice assistants (VAs), may support the patients’ ability to make health-related decisions and manage their chronic conditions. Objective This study aims to evaluate the reliability of the most common VAs (ie, Amazon Alexa, Apple Siri, and Google Assistant) in responding to questions about management of the main NCD. Methods We generated health-related questions based on frequently asked questions from health organization, government, medical nonprofit, and other recognized health-related websites about conditions associated with Alzheimer’s disease (AD), lung cancer (LCA), chronic obstructive pulmonary disease, diabetes mellitus (DM), cardiovascular disease, chronic kidney disease (CKD), and cerebrovascular accident (CVA). We then validated them with practicing medical specialists, selecting the 10 most frequent ones. Given the low average frequency of the AD-related questions, we excluded such questions. This resulted in a pool of 60 questions. We submitted the selected questions to VAs in a 3×3×6 fractional factorial design experiment with 3 developers (ie, Amazon, Apple, and Google), 3 modalities (ie, voice only, voice and display, display only), and 6 diseases. We assessed the rate of error-free voice responses and classified the web sources based on previous research (ie, expert, commercial, crowdsourced, or not stated). Results Google showed the highest total response rate, followed by Amazon and Apple. Moreover, although Amazon and Apple showed a comparable response rate in both voice-and-display and voice-only modalities, Google showed a slightly higher response rate in voice only. The same pattern was observed for the rate of expert sources. When considering the response and expert source rate across diseases, we observed that although Google remained comparable, with a slight advantage for LCA and CKD, both Amazon and Apple showed the highest response rate for LCA. However, both Google and Apple showed most often expert sources for CVA, while Amazon did so for DM. Conclusions Google showed the highest response rate and the highest rate of expert sources, leading to the conclusion that Google Assistant would be the most reliable tool in responding to questions about NCD management. However, the rate of expert sources differed across diseases. We urge health organizations to collaborate with Google, Amazon, and Apple to allow their VAs to consistently provide reliable answers to health-related questions on NCD management across the different diseases.

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Section: Response Rate Across Developers and Modalitiessupporting

confidence: 88%

Reliability of Commercial Voice Assistants’ Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information

Bérubé¹,

Kovacs²,

Fleisch³

et al. 2021

J Med Internet Res

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show abstract

“…As Amazon, Google and Microsoft provide more HIPAA compliant AI-as-a-services (which range from ASR, NLP models, speech-to-text, text-to-speech, and translation mechanisms) modern cloud and mobile application ecosystems will become increasingly easier to integrate into patient-facing apps at scale (with minimal quality of service loss for large user bases and populations). Such services are constantly improving, and the accuracy can be expected to increase over time (e.g., Amazon Medical Scribe for medical terms in transcriptions) ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

“Hey Siri, Help Me Take Care of My Child”: A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management

Sezgın

Oiler

Abbott

et al. 2022

Front. Public Health

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BackgroundAbout 23% of households in the United States have at least one child who has special healthcare needs. As most care activities occur at home, there is often a disconnect and lack of communication between families, home care nurses, and healthcare providers. Digital health technologies may help bridge this gap.ObjectiveWe conducted a pre-post study with a voice-enabled medical note taking (diary) app (SpeakHealth) in a real world setting with caregivers (parents, family members) of children with special healthcare needs (CSHCN) to understand feasibility of voice interaction and automatic speech recognition (ASR) for medical note taking at home.MethodsIn total, 41 parents of CSHCN were recruited. Participants completed a pre-study survey collecting demographic details, technology and care management preferences. Out of 41, 24 participants completed the study, using the app for 2 weeks and completing an exit survey. The app facilitated caregiver note-taking using voice interaction and ASR. An exit survey was conducted to collect feedback on technology adoption and changes in technology preferences in care management. We assessed the feasibility of the app by descriptively analyzing survey responses and user data following the key focus areas of acceptability, demand, implementation and integration, adaptation and expansion. In addition, perceived effectiveness of the app was assessed by comparing perceived changes in mobile app preferences among participants. In addition, the voice data, notes, and transcriptions were descriptively analyzed for understanding the feasibility of the app.ResultsThe majority of the recruited parents were 35–44 years old (22, 53.7%), part of a two-parent household (30, 73.2%), white (37, 90.2%), had more than one child (31, 75.6%), lived in Ohio (37, 90.2%), used mobile health apps, mobile note taking apps or calendar apps (28, 68.3%) and patient portal apps (22, 53.7%) to track symptoms and health events at home. Caregivers had experience with voice technology as well (32, 78%). Among those completed the post-study survey (in Likert Scale 1–5), ~80% of the caregivers agreed or strongly agreed that using the app would enhance their performance in completing tasks (perceived usefulness; mean = 3.4, SD = 0.8), the app is free of effort (perceived ease of use; mean = 3.2, SD = 0.9), and they would use the app in the future (behavioral intention; mean = 3.1, SD = 0.9). In total, 88 voice interactive patient notes were generated with the majority of the voice recordings being less than 20 s in length (66%). Most noted symptoms and conditions, medications, treatment and therapies, and patient behaviors. More than half of the caregivers reported that voice interaction with the app and using transcribed notes positively changed their preference of technology to use and methods for tracking symptoms and health events at home.ConclusionsOur findings suggested that voice interaction and ASR use in mobile apps are feasible and effective in keeping track of symptoms and health events at home. Future work is suggested toward using integrated and intelligent systems with voice interactions with broader populations.

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“…But India now has a space to enhance smart humanistic services and patient satisfaction as described in [35]. Figure 4 shows the digital methods to extend healthcare services in India [36][37][38][39]. Other Uses of AI Implementation science: It is a relatively new topic, but is capable of significantly improving our understanding of AI implementation through the development of new theories, models, and frameworks.…”

Section: Enhancing Humanistic Services and Patient Experiencesmentioning

confidence: 99%

Towards Ideal Health Ecosystem With Artificial Intelligence-Driven Medical Services in India: An Overview

Kumar,

Ingole,

Choudhari

2023

Cureus

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Artificial intelligence (AI) has immense power to set up an ideal health ecosystem through "intelligent medicine" i.e., a combination of human and machine intelligence. However, the application of AI in healthcare is still unclear. Currently, India is facing huge challenges such as the scarcity of medical resources and the uneven distribution of medical services. This also highlights the opportunities linked to challenges and risks. The most recent pandemic has accelerated this process by acknowledging that medicine stands on the brink of an AI revolution. Incorporating the evidence on the role of precision medicine, cost-effective healthcare, and expanding humanistic and medical services, this paper demonstrates the digital health interventions for the "enhancement" of capabilities, "efficiency," "extension of services" and upgrading "experience" in the health sector. Through thorough literature searches from PubMed, Google Scholar, and other reliable sources, this study aims to understand the evolving needs, and greater control and to bridge gaps in access to healthcare through AI. Also, India is currently developing the potential to automate multiple tasks and calling for more human interventions. The future of AI in healthcare looks promising with digital health interventions that eventually offer flexibility and convenience to both the patient and the provider. This paper will help public health professionals address ethical considerations and policy-making where AI plays a significant role in setting up an ideal health ecosystem.

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Medication Name Comprehension of Intelligent Virtual Assistants: A Comparison of Amazon Alexa, Google Assistant, and Apple Siri Between 2019 and 2021

Cited by 15 publications

References 10 publications

Reliability of Commercial Voice Assistants’ Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information

Reliability of Commercial Voice Assistants’ Responses to Health-Related Questions in Noncommunicable Disease Management: Factorial Experiment Assessing Response Rate and Source of Information

“Hey Siri, Help Me Take Care of My Child”: A Feasibility Study With Caregivers of Children With Special Healthcare Needs Using Voice Interaction and Automatic Speech Recognition in Remote Care Management

Towards Ideal Health Ecosystem With Artificial Intelligence-Driven Medical Services in India: An Overview

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