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Lookup NU author(s): Madison Milne-Ives, Professor Edward MeinertORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© Nick de Pennington, Guy Mole, Ernest Lim, Madison Milne-Ives, Eduardo Normando, Kanmin Xue, Edward Meinert. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 28.07.2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included. Background: Due to an aging population, the demand for many services is exceeding the capacity of the clinical workforce. As a result, staff are facing a crisis of burnout from being pressured to deliver high-volume workloads, driving increasing costs for providers. Artificial intelligence (AI), in the form of conversational agents, presents a possible opportunity to enable efficiency in the delivery of care. Objective: This study aims to evaluate the effectiveness, usability, and acceptability of Dora agent: Ufonia's autonomous voice conversational agent, an AI-enabled autonomous telemedicine call for the detection of postoperative cataract surgery patients who require further assessment. The objectives of this study are to establish Dora's efficacy in comparison with an expert clinician, determine baseline sensitivity and specificity for the detection of true complications, evaluate patient acceptability, collect evidence for cost-effectiveness, and capture data to support further development and evaluation. Methods: Using an implementation science construct, the interdisciplinary study will be a mixed methods phase 1 pilot establishing interobserver reliability of the system, usability, and acceptability. This will be done using the following scales and frameworks: the system usability scale; assessment of Health Information Technology Interventions in Evidence-Based Medicine Evaluation Framework; the telehealth usability questionnaire; and the Non-Adoption, Abandonment, and Challenges to the Scale-up, Spread and Suitability framework. Results: The evaluation is expected to show that conversational technology can be used to conduct an accurate assessment and that it is acceptable to different populations with different backgrounds. In addition, the results will demonstrate how successfully the system can be delivered in organizations with different clinical pathways and how it can be integrated with their existing platforms. Conclusions: The project's key contributions will be evidence of the effectiveness of AI voice conversational agents and their associated usability and acceptability.
Author(s): de Pennington N, Mole G, Lim E, Milne-Ives M, Normando E, Xue K, Meinert E
Publication type: Article
Publication status: Published
Journal: JMIR Research Protocols
Year: 2021
Volume: 10
Issue: 7
Online publication date: 28/07/2021
Acceptance date: 20/01/2021
Date deposited: 26/07/2024
ISSN (electronic): 1929-0748
Publisher: JMIR Publications Inc.
URL: https://doi.org/10.2196/27227
DOI: 10.2196/27227
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