An Introduction to Query Understanding

2020 ◽  
pp. 1-13
Author(s):  
Hongbo Deng ◽  
Yi Chang
Keyword(s):  
Query Understanding

Guest Editorial: Query understanding

2018 ◽  
Vol 274 ◽  
pp. 1-2
Author(s):  
Liqiang Nie ◽  
Richang Hong ◽  
Peng Cui
Keyword(s):  
Guest Editorial ◽  
Query Understanding

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 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.

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