TY  - JOUR
AU  - de Pennington, Nick
AU  - Mole, Guy
AU  - Lim, Ernest
AU  - Milne-Ives, Madison
AU  - Normando, Eduardo
AU  - Xue, Kanmin
AU  - Meinert, Edward
PY  - 2021
DA  - 2021/7/28
TI  - Safety and Acceptability of a Natural Language Artificial Intelligence Assistant to Deliver Clinical Follow-up to Cataract Surgery Patients: Proposal
JO  - JMIR Res Protoc
SP  - e27227
VL  - 10
IS  - 7
KW  - artificial intelligence
KW  - natural language processing
KW  - telemedicine
KW  - cataract
KW  - aftercare
KW  - speech recognition software
KW  - medical informatics
KW  - health services
KW  - health communication
KW  - delivery of health care
KW  - patient acceptance of health care
KW  - mental health
KW  - cell phone
KW  - internet
KW  - conversational agent
KW  - chatbot
KW  - expert systems
KW  - dialogue system
KW  - relational agent
AB  - 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. International Registered Report Identifier (IRRID): PRR1-10.2196/27227 
SN  - 1929-0748
UR  - https://www.researchprotocols.org/2021/7/e27227
UR  - https://doi.org/10.2196/27227
UR  - http://www.ncbi.nlm.nih.gov/pubmed/34319248
DO  - 10.2196/27227
ID  - info:doi/10.2196/27227
ER  -