TY - JOUR
T1 - Safety and acceptability of a natural-language AI assistant to deliver clinical follow-up to cataract surgery patients: Proposal for a pragmatic evaluation
AU - de, Pennington N
AU - Mole, Guy
AU - Lim, Ernest
AU - Milne-Ives, Madison
AU - Normando, Eduardo
AU - Xue, Kanmin
AU - Meinert, Edward
PY - 2021/7/28
Y1 - 2021/7/28
N2 - Background: Due to an ageing 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, in the form of conversational agents, presents a possible opportunity to enable efficiencies in the delivery of care.
Aims and Objectives: This study aims to evaluate the effectiveness, usability, and acceptability of Dora - an AI-enabled autonomous telemedicine call - for detection of post-operative cataract surgery patients who require further assessment. The study’s objectives are to: 1) establish Dora’s efficacy in comparison to an expert clinician, 2) determine baseline sensitivity and specificity for detection of true complications, 3) evaluate patient acceptability, 4) collect evidence for cost-effectiveness, and 5) capture data to support further development and evaluation.
Methods: Based on implementation science, the interdisciplinary study will be a mixed-methods phase one pilot establishing inter-observer reliability of the system, usability, and acceptability. This will be done using 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 (TUQ); the Non-Adoption, Abandonment and Challenges to the Scale-up, Spread and Suitability (NASSS) framework.
Results: The results will be included in the final evaluation paper, which we aim to publish in 2022. The study will last eighteen months: seven months of evaluation and intervention refinement, nine months of implementation and follow-up, and two months of post-evaluation analysis and write-up.
Conclusions: The project’s key contributions will be evidence on artificial intelligence voice conversational agent effectiveness, and associated usability and acceptability.
AB - Background: Due to an ageing 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, in the form of conversational agents, presents a possible opportunity to enable efficiencies in the delivery of care.
Aims and Objectives: This study aims to evaluate the effectiveness, usability, and acceptability of Dora - an AI-enabled autonomous telemedicine call - for detection of post-operative cataract surgery patients who require further assessment. The study’s objectives are to: 1) establish Dora’s efficacy in comparison to an expert clinician, 2) determine baseline sensitivity and specificity for detection of true complications, 3) evaluate patient acceptability, 4) collect evidence for cost-effectiveness, and 5) capture data to support further development and evaluation.
Methods: Based on implementation science, the interdisciplinary study will be a mixed-methods phase one pilot establishing inter-observer reliability of the system, usability, and acceptability. This will be done using 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 (TUQ); the Non-Adoption, Abandonment and Challenges to the Scale-up, Spread and Suitability (NASSS) framework.
Results: The results will be included in the final evaluation paper, which we aim to publish in 2022. The study will last eighteen months: seven months of evaluation and intervention refinement, nine months of implementation and follow-up, and two months of post-evaluation analysis and write-up.
Conclusions: The project’s key contributions will be evidence on artificial intelligence voice conversational agent effectiveness, and associated usability and acceptability.
UR - https://pearl.plymouth.ac.uk/context/nm-research/article/1288/viewcontent/27227_456102_1_SM_Rev_A.pdf
U2 - 10.2196/27227
DO - 10.2196/27227
M3 - Article
SN - 1929-0748
VL - 10
JO - JMIR Research Protocols
JF - JMIR Research Protocols
IS - 7
ER -