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RESEARCH ARTICLE (Open Access)

Barriers and facilitators to engagement with artificial intelligence (AI)-based chatbots for sexual and reproductive health advice: a qualitative analysis

Tom Nadarzynski https://orcid.org/0000-0001-7010-5308 A * , Vannesa Puentes B , Izabela Pawlak A , Tania Mendes A , Ian Montgomery C , Jake Bayley D and Damien Ridge https://orcid.org/0000-0001-9245-5958 A
+ Author Affiliations
- Author Affiliations

A School of Social Sciences, University of Westminster, London, UK.

B Science, Engineering and Computing Faculty, Kingston University, London, UK.

C Positive East, London, UK.

D Barts NHS Trust, London, UK.

* Correspondence to: T.Nadarzynski@westminster.ac.uk

Handling Editor: Christy Newman

Sexual Health 18(5) 385-393 https://doi.org/10.1071/SH21123
Submitted: 12 March 2021  Accepted: 19 July 2021   Published: 16 November 2021

© 2021 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Background: The emergence of artificial intelligence (AI) provides opportunities for demand management of sexual and reproductive health services. Conversational agents/chatbots are increasingly common, although little is known about how this technology could aid services. This study aimed to identify barriers and facilitators for engagement with sexual health chatbots to advise service developers and related health professionals.

Methods: In January–June 2020, we conducted face-to-face, semi-structured and online interviews to explore views on sexual health chatbots. Participants were asked to interact with a chatbot, offering advice on sexually transmitted infections (STIs) and relevant services. Participants were UK-based and recruited via social media. Data were recorded, transcribed verbatim and analysed thematically.

Results: Forty participants (aged 18–50 years; 64% women, 77% heterosexual, 58% white) took part. Many thought chatbots could aid sex education, providing useful information about STIs and sign-posting to sexual health services in a convenient, anonymous and non-judgemental way. Some compared chatbots to health professionals or Internet search engines and perceived this technology as inferior, offering constrained content and interactivity, limiting disclosure of personal information, trust and perceived accuracy of chatbot responses.

Conclusions: Despite mixed attitudes towards chatbots, this technology was seen as useful for anonymous sex education but less suitable for matters requiring empathy. Chatbots may increase access to clinical services but their effectiveness and safety need to be established. Future research should identify which chatbots designs and functions lead to optimal engagement with this innovation.

Keywords: AI, artificial intelligence, chatbot, e-health, education, health promotion, health services, risk assessment.


References

[1]  World Health Organization. Global health sector strategy on sexually transmitted infections 2016–2021: toward ending STIs. Geneva: World Health Organization; 2016.

[2]  Mitchell H, Allen H, Sonubi T, Kuyumdzhieva G, Harb A, Shah A. Sexually transmitted infections and screening for chlamydia in England, 2019. London: Official Statistics of Public Health England; 2020.

[3]  Deblonde J, De Koker P, Hamers FF, Fontaine J, Luchters S, Temmerman M. Barriers to HIV testing in Europe: a systematic review. Eur J Public Health 2010; 20 422–32.
Barriers to HIV testing in Europe: a systematic review.Crossref | GoogleScholarGoogle Scholar | 20123683PubMed |

[4]  McDonagh LK, Saunders JM, Cassell J, Curtis T, Bastaki H, Hartney T, Rait G. Application of the COM-B model to barriers and facilitators to chlamydia testing in general practice for young people and primary care practitioners: a systematic review. Implement Sci 2018; 13 130
Application of the COM-B model to barriers and facilitators to chlamydia testing in general practice for young people and primary care practitioners: a systematic review.Crossref | GoogleScholarGoogle Scholar | 30348165PubMed |

[5]  Public Health England. The impact of the COVID-19 pandemic on prevention, testing, diagnosis and care for sexually transmitted infections, HIV and viral hepatitis in England. London: Public Health England; 2020.

[6]  Jacob L, Smith L, Butler L, Barnett Y, Grabovac I, McDermott D, Armstrong N, Yakkundi A, Tully MA. Challenges in the practice of sexual medicine in the time of COVID-19 in the United Kingdom. J Sex Med 2020; 17 1229–36.
Challenges in the practice of sexual medicine in the time of COVID-19 in the United Kingdom.Crossref | GoogleScholarGoogle Scholar | 32411271PubMed |

[7]  Guse K, Levine D, Martins S, Lira A, Gaarde J, Westmorland W, Gilliam M. Interventions using new digital media to improve adolescent sexual health: a systematic review. J Adolesc Health 2012; 51 535–43.
Interventions using new digital media to improve adolescent sexual health: a systematic review.Crossref | GoogleScholarGoogle Scholar | 23174462PubMed |

[8]  Bailey J, Mann S, Wayal S, Hunter R, Free C, Abraham C, Murray E. Sexual health promotion for young people delivered via digital media: a scoping review. Public Health Res 2015; 3 1–20.
Sexual health promotion for young people delivered via digital media: a scoping review.Crossref | GoogleScholarGoogle Scholar |

[9]  Gabarron E, Wynn R. Use of social media for sexual health promotion: a scoping review. Glob Health Action 2016; 9 32193
Use of social media for sexual health promotion: a scoping review.Crossref | GoogleScholarGoogle Scholar | 27649758PubMed |

[10]  Marcus JL, Hurley LB, Krakower DS, Alexeeff S, Silverberg MJ, Volk JE. Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study. Lancet HIV 2019; 6 e688–95.
Use of electronic health record data and machine learning to identify candidates for HIV pre-exposure prophylaxis: a modelling study.Crossref | GoogleScholarGoogle Scholar | 31285183PubMed |

[11]  Marcus JL, Sewell WC, Balzer LB, Krakower DS. Artificial intelligence and machine learning for HIV prevention: emerging approaches to ending the epidemic. Curr HIV/AIDS Rep 2020; 17 171–9.
Artificial intelligence and machine learning for HIV prevention: emerging approaches to ending the epidemic.Crossref | GoogleScholarGoogle Scholar | 32347446PubMed |

[12]  Laranjo L, Dunn AG, Tong HL, Kocaballi AB, Chen J, Bashir R, Surian D, Gallego B, Magrabi F, Lau AYS, Coiera E. Conversational agents in healthcare: a systematic review. J Am Med Inform Assoc 2018; 25 1248–58.
Conversational agents in healthcare: a systematic review.Crossref | GoogleScholarGoogle Scholar | 30010941PubMed |

[13]  Tudor Car L, Dhinagaran DA, Kyaw BM, Kowatsch T, Joty S, Theng YL, Atun R. Conversational agents in health care: scoping review and conceptual analysis. J Med Internet Res 2020; 22 e17158
Conversational agents in health care: scoping review and conceptual analysis.Crossref | GoogleScholarGoogle Scholar | 32763886PubMed |

[14]  Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, Normando E, Meinert E. The Effectiveness of artificial intelligence conversational agents in health care: systematic review. J Med Internet Res 2020; 22 e20346
The Effectiveness of artificial intelligence conversational agents in health care: systematic review.Crossref | GoogleScholarGoogle Scholar | 33090118PubMed |

[15]  Brixey J, Hoegen R, Lan W, Rusow J, Singla K, Yin X, Artstein R, Leuski A. Shihbot: a facebook chatbot for sexual health information on HIV/AIDS. Proceedings of the 18th annual SIGdial meeting on discourse and dialogue; 15–17 August 2017; Saarbrucken, Germany. Stroudsburg: Association for Computational Linguistics; 2017. p. 370–3.

[16]  Crutzen R, Peters GJ, Portugal SD, Fisser EM, Grolleman JJ. An artificially intelligent chat agent that answers adolescents’ questions related to sex, drugs, and alcohol: an exploratory study. J Adolesc Health 2011; 48 514–19.
An artificially intelligent chat agent that answers adolescents’ questions related to sex, drugs, and alcohol: an exploratory study.Crossref | GoogleScholarGoogle Scholar | 21501812PubMed |

[17]  Maeda E, Miyata A, Boivin J, Nomura K, Kumazawa Y, Shirasawa H, Saito H, Terada Y. Promoting fertility awareness and preconception health using a chatbot: a randomized controlled trial. Reprod Biomed Online 2020; 41 1133–43.
Promoting fertility awareness and preconception health using a chatbot: a randomized controlled trial.Crossref | GoogleScholarGoogle Scholar | 33039321PubMed |

[18]  Bickmore T, Zhang Z, Reichert M, Julce C, Jack B. Promotion of preconception care among adolescents and young adults by conversational agent. J Adolesc Health 2020; 67 S45–51.
Promotion of preconception care among adolescents and young adults by conversational agent.Crossref | GoogleScholarGoogle Scholar | 32718515PubMed |

[19]  Dworkin MS, Lee S, Chakraborty A, Monahan C, Hightow-Weidman L, Garofalo R, Qato DM, Liu L, Jimenez A. Acceptability, feasibility, and preliminary efficacy of a theory-based relational embodied conversational agent mobile phone intervention to promote HIV medication adherence in young HIV-positive African American MSM. AIDS Educ Prev 2019; 31 17–37.
Acceptability, feasibility, and preliminary efficacy of a theory-based relational embodied conversational agent mobile phone intervention to promote HIV medication adherence in young HIV-positive African American MSM.Crossref | GoogleScholarGoogle Scholar | 30742481PubMed |

[20]  Park H, Joonhwan L. Can a conversational agent lower sexual violence victims’ burden of self-disclosure?. CHI EA ’20: Extended Abstracts of the 2020 CHI Conference on human factors in computing systems; 25 April 2020; Honolulu, HI, USA. New York: Association for Computing Machinery; 2020. p. 1–8.

[21]  Kocielnik R, Agapie E, Argyle A, Hsieh DT, Yadav K, Taira B, Hsieh G. HarborBot: a chatbot for social needs screening. AMIA Annu Symp Proc 2019; 552–61.
| 32308849PubMed |

[22]  Nadarzynski T, Bayley J, Llewellyn C, Kidsley S, Graham CA. Acceptability of artificial intelligence (AI)-enabled chatbots, video consultations and live webchats as online platforms for sexual health advice. BMJ Sex Reprod Health 2020; 46 210–7.
Acceptability of artificial intelligence (AI)-enabled chatbots, video consultations and live webchats as online platforms for sexual health advice.Crossref | GoogleScholarGoogle Scholar | 31964779PubMed |

[23]  Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006; 3 77–101.
Using thematic analysis in psychology.Crossref | GoogleScholarGoogle Scholar |

[24]  Malterud K. Qualitative research: standards, challenges, and guidelines. Lancet 2001; 358 483–488.
Qualitative research: standards, challenges, and guidelines.Crossref | GoogleScholarGoogle Scholar | 11513933PubMed |

[25]  Espinoza J, Crown K, Kulkarni O. A guide to chatbots for COVID-19 screening at pediatric health care facilities. JMIR Public Health Surveill 2020; 6 e18808
A guide to chatbots for COVID-19 screening at pediatric health care facilities.Crossref | GoogleScholarGoogle Scholar | 32325425PubMed |

[26]  Vaira L, Bochicchio MA, Conte M, Casaluci FM, Melpignano A. MamaBot: a System based on ML and NLP for supporting Women and Families during Pregnancy. In Proceedings of the 22nd International Database Engineering & Applications Symposium; June 2018. pp. 273–277.

[27]  Nadarzynski T, Miles O, Cowie A, Ridge D. Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study. Digit Health 2019; 5 1–12.
Acceptability of artificial intelligence (AI)-led chatbot services in healthcare: a mixed-methods study.Crossref | GoogleScholarGoogle Scholar |

[28]  Mierzwa S, Souidi S, Conroy T, Abusyed M, Watarai H, Allen T. On the potential, feasibility, and effectiveness of chat bots in public health research going forward. Online Journal of Public Health Informatics 2019; 11 e4
On the potential, feasibility, and effectiveness of chat bots in public health research going forward.Crossref | GoogleScholarGoogle Scholar | 31632598PubMed |

[29]  Gao S, He L, Chen Y, Li D, Lai K. Public perception of artificial intelligence in medical care: Content analysis of social media. J Med Internet Res 2020; 22 e16649
Public perception of artificial intelligence in medical care: Content analysis of social media.Crossref | GoogleScholarGoogle Scholar | 32673231PubMed |

[30]  Garside R, Ayres R, Owen M, Pearson VA, Roizen J. Anonymity and confidentiality: rural teenagers’ concerns when accessing sexual health services. BMJ Sex Reprod Health 2002; 28 23–26.
Anonymity and confidentiality: rural teenagers’ concerns when accessing sexual health services.Crossref | GoogleScholarGoogle Scholar |

[31]  Wilson A, Williams R. Sexual health services: what do teenagers want? Ambul Child Health 2000; 6 253–260.
Sexual health services: what do teenagers want?Crossref | GoogleScholarGoogle Scholar |