Exploring Parental Experiences of Childhood Ear Health Clinics and Their Acceptability of AI‐Based Diagnostic Tools: A Qualitative Study.
Objective: Artificial intelligence and machine learning (AI/ML) algorithms will transform the childhood otitis media (OM) diagnostic experience. However, there is limited data on parents' current experiences within clinical settings, limited research exploring AI/ML acceptability among consumers gen...
| Publicado en: | Health Expectations Vol. 28; no. 5; pp. 1 - 14 |
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| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188926553&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188926553 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13696513 EVY jtl: Health Expectations issn: 13696513 maglogo: Y pubinfo: dt: Oct2025 vid: 28 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 188926553 188926553 188926553 10.1111/hex.70421 188926553 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Exploring Parental Experiences of Childhood Ear Health Clinics and Their Acceptability of AI‐Based Diagnostic Tools: A Qualitative Study. aug: au: Stephens, Jacqueline H. Northcott, Celine Machell, Amanda Lewis, Trent Ooi, Eng H. affil: Flinders Health and Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, Australia sug: subj: Artificial Intelligence Utilization Parental Attitudes Evaluation Otitis Media Diagnosis Consumer Attitudes Evaluation Caregiver Attitudes Evaluation Audiology Human Qualitative Studies Funding Source Australia Semi-Structured Interview Thematic Analysis Academic Medical Centers Australia Purposive Sample Social Class Audiorecording Grounded Theory Data Analysis Software Child Recurrence Descriptive Statistics Male Female Adolescence Adult Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Male Female ab: Objective: Artificial intelligence and machine learning (AI/ML) algorithms will transform the childhood otitis media (OM) diagnostic experience. However, there is limited data on parents' current experiences within clinical settings, limited research exploring AI/ML acceptability among consumers generally, and none regarding consumer perspectives on its use for childhood OM. This study aimed to explore current parental experiences of, as well as their perspectives on the use of AI/ML in, clinical care for OM in children. Design: We conducted and thematically analysed semi‐structured interviews with parents of children seen for OM within the ENT or audiology departments of an Australian urban teaching hospital. Findings: Seven themes were identified: (1) Meeting children's needs; (2) Challenges in accessing and waiting for audiology and ENT care; (3) Urban versus rural healthcare experience; (4) Public versus private health system; (5) Strategies for enhancing paediatric audiology services; (6) Perceived benefits of AI/ML in ear disease diagnosis; and (7) Concerns and considerations regarding AI/ML in ear health diagnosis. Conclusions: Parents have concerns about the use and development of AI/ML tools, but also acknowledge the potential benefits of such tools for healthcare delivery. Currently, the understanding amongst parents of AI/ML tools for OM diagnosis was limited, and more education on the use and development of AI/ML for OM is warranted. Patient or Public Contribution: We did not involve patients or the public in the design of this study. However, three authors have lived experience as parents of children who have had recurrent ear infections. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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