scholarly journals Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment

10.2196/30704 ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. e30704
Author(s):  
Timothy W Bickmore ◽  
Stefán Ólafsson ◽  
Teresa K O'Leary
Keyword(s):  
Mixed Methods ◽  
Natural Language ◽  
Health Professional ◽  
Medical Information ◽  
Medical Advice ◽  
Medical Problems ◽  
Consumer Safety ◽  
Safety Risks ◽  
Query Understanding ◽  
College Educated

Background Prior studies have demonstrated the safety risks when patients and consumers use conversational assistants such as Apple’s Siri and Amazon’s Alexa for obtaining medical information. Objective The aim of this study is to evaluate two approaches to reducing the likelihood that patients or consumers will act on the potentially harmful medical information they receive from conversational assistants. Methods Participants were given medical problems to pose to conversational assistants that had been previously demonstrated to result in potentially harmful recommendations. Each conversational assistant’s response was randomly varied to include either a correct or incorrect paraphrase of the query or a disclaimer message—or not—telling the participants that they should not act on the advice without first talking to a physician. The participants were then asked what actions they would take based on their interaction, along with the likelihood of taking the action. The reported actions were recorded and analyzed, and the participants were interviewed at the end of each interaction. Results A total of 32 participants completed the study, each interacting with 4 conversational assistants. The participants were on average aged 42.44 (SD 14.08) years, 53% (17/32) were women, and 66% (21/32) were college educated. Those participants who heard a correct paraphrase of their query were significantly more likely to state that they would follow the medical advice provided by the conversational assistant (χ21=3.1; P=.04). Those participants who heard a disclaimer message were significantly more likely to say that they would contact a physician or health professional before acting on the medical advice received (χ21=43.5; P=.001). Conclusions Designers of conversational systems should consider incorporating both disclaimers and feedback on query understanding in response to user queries for medical advice. Unconstrained natural language input should not be used in systems designed specifically to provide medical advice.

scholarly journals Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment (Preprint)

2021 ◽  
Author(s):  
Timothy W Bickmore ◽  
Stefán Ólafsson ◽  
Teresa K O'Leary
Keyword(s):  
Mixed Methods ◽  
Natural Language ◽  
Health Professional ◽  
Medical Information ◽  
Medical Advice ◽  
Medical Problems ◽  
Consumer Safety ◽  
Safety Risks ◽  
Query Understanding ◽  
College Educated

BACKGROUND Prior studies have demonstrated the safety risks when patients and consumers use conversational assistants such as Apple’s Siri and Amazon’s Alexa for obtaining medical information. OBJECTIVE The aim of this study is to evaluate two approaches to reducing the likelihood that patients or consumers will act on the potentially harmful medical information they receive from conversational assistants. METHODS Participants were given medical problems to pose to conversational assistants that had been previously demonstrated to result in potentially harmful recommendations. Each conversational assistant’s response was randomly varied to include either a correct or incorrect paraphrase of the query or a disclaimer message—or not—telling the participants that they should not act on the advice without first talking to a physician. The participants were then asked what actions they would take based on their interaction, along with the likelihood of taking the action. The reported actions were recorded and analyzed, and the participants were interviewed at the end of each interaction. RESULTS A total of 32 participants completed the study, each interacting with 4 conversational assistants. The participants were on average aged 42.44 (SD 14.08) years, 53% (17/32) were women, and 66% (21/32) were college educated. Those participants who heard a correct paraphrase of their query were significantly more likely to state that they would follow the medical advice provided by the conversational assistant (<i>χ</i><sup>2</sup><sub>1</sub>=3.1; <i>P</i>=.04). Those participants who heard a disclaimer message were significantly more likely to say that they would contact a physician or health professional before acting on the medical advice received (<i>χ</i><sup>2</sup><sub>1</sub>=43.5; <i>P</i>=.001). CONCLUSIONS Designers of conversational systems should consider incorporating both disclaimers and feedback on query understanding in response to user queries for medical advice. Unconstrained natural language input should not be used in systems designed specifically to provide medical advice. CLINICALTRIAL

scholarly journals Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant (Preprint)

2018 ◽  
Author(s):  
Timothy W. Bickmore ◽  
Ha Trinh ◽  
Stefan Olafsson ◽  
Teresa K O'Leary ◽  
Reza Asadi ◽  
...  
Keyword(s):  
Medical Information ◽  
Healthcare Research ◽  
Patient Harm ◽  
Medical Problems ◽  
Safety Issues ◽  
Consumer Safety ◽  
Safety Risks ◽  
Potential Safety ◽  
College Educated ◽  
Medical Task

BACKGROUND Conversational assistants, such as Siri, Alexa, and Google Assistant, are ubiquitous and are beginning to be used as portals for medical services. However, the potential safety issues of using conversational assistants for medical information by patients and consumers are not understood. OBJECTIVE To determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information. METHODS Participants were given medical problems to pose to Siri, Alexa, or Google Assistant, and asked to determine an action to take based on information from the system. Assignment of tasks and systems were randomized across participants, and participants queried the conversational assistants in their own words, making as many attempts as needed until they either reported an action to take or gave up. Participant-reported actions for each medical task were rated for patient harm using an Agency for Healthcare Research and Quality harm scale. RESULTS Fifty-four subjects completed the study with a mean age of 42 years (SD 18). Twenty-nine (54%) were female, 31 (57%) Caucasian, and 26 (50%) were college educated. Only 8 (15%) reported using a conversational assistant regularly, while 22 (41%) had never used one, and 24 (44%) had tried one “a few times.“ Forty-four (82%) used computers regularly. Subjects were only able to complete 168 (43%) of their 394 tasks. Of these, 49 (29%) reported actions that could have resulted in some degree of patient harm, including 27 (16%) that could have resulted in death. CONCLUSIONS Reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider.

scholarly journals Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant

10.2196/11510 ◽  
2018 ◽  
Vol 20 (9) ◽  
pp. e11510 ◽  
Author(s):  
Timothy W Bickmore ◽  
Ha Trinh ◽  
Stefan Olafsson ◽  
Teresa K O'Leary ◽  
Reza Asadi ◽  
...  
Keyword(s):  
Observational Study ◽  
Medical Information ◽  
Consumer Safety ◽  
Safety Risks

Testing Consultation Recordings in a Clinical Setting With the SecondEars Smartphone App: Mixed Methods Implementation Study (Preprint)

2019 ◽  
Author(s):  
Amelia Hyatt ◽  
Ruby Lipson-Smith ◽  
Bryce Morkunas ◽  
Meinir Krishnasamy ◽  
Michael Jefford ◽  
...  
Keyword(s):  
Health Care ◽  
Mixed Methods ◽  
Health Professional ◽  
Clinical Setting ◽  
Health Professionals ◽  
Implementation Strategy ◽  
Smartphone App ◽  
Implementation Study ◽  
Pilot Testing ◽  
Effective Implementation

BACKGROUND Health care systems are increasingly looking to mobile device technologies (mobile health) to improve patient experience and health outcomes. SecondEars is a smartphone app designed to allow patients to audio-record medical consultations to improve recall, understanding, and health care self-management. Novel health interventions such as SecondEars often fail to be implemented post pilot-testing owing to inadequate user experience (UX) assessment, a key component of a comprehensive implementation strategy. OBJECTIVE This study aimed to pilot the SecondEars app within an active clinical setting to identify factors necessary for optimal implementation. Objectives were to (1) investigate patient UX and acceptability, utility, and satisfaction with the SecondEars app, and (2) understand health professional perspectives on issues, solutions, and strategies for effective implementation of SecondEars. METHODS A mixed methods implementation study was employed. Patients were invited to test the app to record consultations with participating oncology health professionals. Follow-up interviews were conducted with all participating patients (or carers) and health professionals, regarding uptake and extent of app use. Responses to the Mobile App Rating Scale (MARS) were also collected. Interviews were analyzed using interpretive descriptive methodology; all quantitative data were analyzed descriptively. RESULTS A total of 24 patients used SecondEars to record consultations with 10 multidisciplinary health professionals. In all, 22 of these patients used SecondEars to listen to all or part of the recording, either alone or with family. All 100% of patient participants reported in the MARS that they would use SecondEars again and recommend it to others. A total of 3 themes were identified from the patient interviews relating to the UX of SecondEars: empowerment, facilitating support in cancer care, and usability. Further, 5 themes were identified from the health professional interviews relating to implementation of SecondEars: changing hospital culture, mitigating medico-legal concerns, improving patient care, communication, and practical implementation solutions. CONCLUSIONS Data collected during pilot testing regarding recording use, UX, and health professional and patient perspectives will be important for designing an effective implementation strategy for SecondEars. Those testing the app found it useful and felt that it could facilitate the benefits of consultation recordings, along with providing patient empowerment and support. Potential issues regarding implementation were discussed, and solutions were generated. CLINICALTRIAL Australia and New Zealand Clinical Trials Registry ACTRN12618000730202; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373915&amp;isClinicalTrial=False

scholarly journals Comparison of disclosure/concealment of medical information given to conversational agents or to physicians

2021 ◽  
Vol 27 (1) ◽  
pp. 146045822199486
Author(s):  
Nicholas RJ Frick ◽  
Felix Brünker ◽  
Björn Ross ◽  
Stefan Stieglitz
Keyword(s):  
Natural Language ◽  
Medical Information ◽  
Frame Of Reference ◽  
Future Research ◽  
Negative Consequences ◽  
Conversational Agents ◽  
Online Questionnaire ◽  
Computer Based

Within the anamnesis, medical information is frequently withheld, incomplete, or incorrect, potentially causing negative consequences for the patient. The use of conversational agents (CAs), computer-based systems using natural language to interact with humans, may mitigate this problem. The present research examines whether CAs differ from physicians in their ability to elicit truthful disclosure and discourage concealment of medical information. We conducted an online questionnaire with German participants ( N = 148) to assess their willingness to reveal medical information. The results indicate that patients would rather disclose medical information to a physician than to a CA; there was no difference in the tendency to conceal information. This research offers a frame of reference for future research on applying CAs during the anamnesis to support physicians. From a practical view, physicians might gain better understanding of how the use of CAs can facilitate the anamnesis.

Comparative Salmonella Spp. and Listeria monocytogenes Inactivation Rates in Four Commercial Mayonnaise Products

1991 ◽  
Vol 54 (12) ◽  
pp. 913-916 ◽  
Author(s):  
JOHN P. ERICKSON ◽  
PHYLLIS JENKINS
Keyword(s):  
Acetic Acid ◽  
Listeria Monocytogenes ◽  
Aqueous Phase ◽  
Population Declines ◽  
Salmonella Spp ◽  
Egg White Lysozyme ◽  
Consumer Safety ◽  
Safety Risks ◽  
Broad Cross Section ◽  
Formula Composition

Salmonella spp. and Listeria monocytogenes strains were inoculated into four commercial mayonnaise products: sandwich spread, real mayonnaise, reduced calorie mayonnaise dressing, and cholesterol-free reduced calorie mayonnaise dressing. Products represented a broad cross-section of aqueous phase acetic acid, salt, sucrose, and other compositional factors. Results showed that Salmonella spp. inactivation rates were unaffected by formula composition. The organism was rapidly inactivated, decreasing ≥8 log10 CFU/g in ≤72 h, in each of the four products. L. monocytogenes inactivation rates were directly correlated with aqueous phase acetic acid concentrations as follows: sandwich spread ≥ real mayonnaise &gt; cholesterol-free reduced calorie mayonnaise dressing &gt; reduced calorie mayonnaise dressing. L. monocytogenes inactivation rate in sandwich spread and real mayonnaise was similar to Salmonella spp. The reduced calorie mayonnaise dressings showed gradual, incremental population declines. L. monocytogenes decreased 3 and 5 log10 CFU/g in 72 h in reduced calorie and cholesterol-free reduced calorie mayonnaise dressings, respectively. The higher anti-listerial activity in the cholesterol free formulation was attributed to egg white lysozyme. This study documented that commercial mayonnaise, including reduced calorie mayonnaise dressing varieties, represent negligible consumer safety risks.

Why don't patients take their medicine? Reasons and solutions in psychiatry

2007 ◽  
Vol 13 (5) ◽  
pp. 336-346 ◽  
Author(s):  
Alex J. Mitchell ◽  
Thomas Selmes
Keyword(s):  
Health Professional ◽  
Psychotropic Medication ◽  
Medical Advice ◽  
Medication Regimen

Over the course of a year, about three-quarters of patients prescribed psychotropic medication will discontinue, often coming to the decision themselves and without informing a health professional. Costs associated with unplanned discontinuation may be substantial if left uncorrected. Partial non-adherence (much more common than full discontinuation) can also be detrimental, although some patients rationally adjust their medication regimen without ill-effect. This article reviews the literature on non-adherence, whether intentional or not, and discusses patients' reasons for failure to concord with medical advice, and predictors of and solutions to the problem of non-adherence.

Politicization of discussions of Covid-19 on Twitter

2021 ◽  
Vol 1 (6) ◽  
pp. 3-11
Author(s):  
Irina G. Ovchinnikova ◽  
◽  
Liana M. Ermakova ◽  
Diana M. Nurbakova ◽  
◽  
...  
Keyword(s):  
Social Media ◽  
Public Opinion ◽  
Semantic Analysis ◽  
Medical Information ◽  
Information Cascades ◽  
Medical Problems ◽  
Significant Information ◽  
Information Distortion ◽  
Public Figures ◽  
English Speaking

Power of social media including Twitter for English speaking community to shape public opinion becomes critical during the current pandemic because of misinformation. The existing studies on spreading misinformation on social media hypothesise that the initial message is fake. In contrast, we focus on information distortion occurring in cascades as the initial message about the Covid-19 treatment is quoted or receives a reply. Public persons discuss medical information on Twitter providing fast and simple response to complex medical problems that users find very attractive to follow. Followers generate information cascades while quoting and commenting on the initial message. In the cascades, medical information from the initial tweet is often distorted. The discussion of the Covid-19 treatment in the cascades is politicized according to users’ political sympathies. We show a significant information shift in cascades initiated by public figures during the Covid-19 pandemic. The study provide valuable insights for the semantic analysis of information distortion.

Consumer Safety

2012 ◽  
Author(s):  
David Vogel
Keyword(s):  
United States ◽  
Regulatory Policy ◽  
The United States ◽  
Pharmaceutical Regulation ◽  
Regulatory Policies ◽  
Consumer Safety ◽  
Safety Risks ◽  
European Regulatory ◽  
Drug Lag ◽  
Important Exception

This chapter analyzes European and American policies toward a range of consumer safety risks; including drugs, children's products, and cosmetics. It shows how European and American risk regulations have converged, though the dynamics through which this occurred differed substantially. Pharmaceutical regulation constitutes the most important exception to the broader pattern of increased transatlantic regulatory policy divergence. What makes this area of regulatory policy distinctive is that its political salience increased in the United States but not in Europe. Pharmaceutical regulation also represents an important exception to the dominant pattern of transatlantic regulatory policy diffusion. In this case, European regulatory policies did affect those of the United States, first by highlighting the transatlantic drug lag, and more recently by American decisions to adopt some European practices to expedite drug approvals.

Blog Snippets Based Drug Effects Extraction System Using Lexical and Grammatical Restrictions

2014 ◽  
Vol 5 (2) ◽  
pp. 1-17 ◽  
Author(s):  
Shiho Kitajima ◽  
Rafal Rzepka ◽  
Kenji Araki
Keyword(s):  
Quality Of Life ◽  
Natural Language ◽  
Large Scale ◽  
Medical Information ◽  
Drug Effects ◽  
Large Scale Data ◽  
Medical Documents ◽  
Baseline System ◽  
Scale Data

Obtaining medical information has a beneficial influence on patients' treatment and QOL (quality of life). The authors aim to make a system that helps patients to collect narrative information. Extracting information from data written by patients will allow the acquisition of information which is easy to understand and provides encouragement. Additionally, by using large-scale data, the system can be utilized for discovering unknown effects or patterns. As the first step, the purpose of this paper is to extract descriptions of the effects caused by taking drugs as a triplet of expressions from illness survival blogs' snippets. This paper proposes a method to extract the triplets using specific clue words and parsing the results in order to extract from blogs written in free natural language. Moreover, recall was improved by combining their proposed method and a baseline system, and precision was improved by filtering using dictionaries we created from existing medical documents.

Sign in / Sign up

Export Citation Format

Share Document