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Main Authors: Jacqueline H. Stephens, Celine Northcott, Amanda Machell, Trent Lewis, Eng H. Ooi
Format: Artículo Open Access
Published: Wiley 2025
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Online Access:https://onlinelibrary.wiley.com/doi/10.1111/hex.70421
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author Jacqueline H. Stephens
Celine Northcott
Amanda Machell
Trent Lewis
Eng H. Ooi
author_facet Jacqueline H. Stephens
Celine Northcott
Amanda Machell
Trent Lewis
Eng H. Ooi
Jacqueline H. Stephens
Celine Northcott
Amanda Machell
Trent Lewis
Eng H. Ooi
collection Wiley Open Access
contents Exploring Parental Experiences of Childhood Ear Health Clinics and Their Acceptability of AI‐Based Diagnostic Tools: A Qualitative Study Jacqueline H. Stephens Celine Northcott Amanda Machell Trent Lewis Eng H. Ooi Health Expectations ABSTRACT 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. 10.1111/hex.70421 http://creativecommons.org/licenses/by/4.0/
doi_str_mv 10.1111/hex.70421
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spellingShingle Exploring Parental Experiences of Childhood Ear Health Clinics and Their Acceptability of AI‐Based Diagnostic Tools: A Qualitative Study
Jacqueline H. Stephens
Celine Northcott
Amanda Machell
Trent Lewis
Eng H. Ooi
Health Expectations
Exploring Parental Experiences of Childhood Ear Health Clinics and Their Acceptability of AI‐Based Diagnostic Tools: A Qualitative Study Jacqueline H. Stephens Celine Northcott Amanda Machell Trent Lewis Eng H. Ooi Health Expectations ABSTRACT 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. 10.1111/hex.70421 http://creativecommons.org/licenses/by/4.0/
title Exploring Parental Experiences of Childhood Ear Health Clinics and Their Acceptability of AI‐Based Diagnostic Tools: A Qualitative Study
topic Health Expectations
url https://onlinelibrary.wiley.com/doi/10.1111/hex.70421