Abstract
BackgroundDue 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.ObjectiveThis 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.MethodsUsing 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.ResultsThe 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.ConclusionsThe 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
Highlights
Clinical problemThe UK’s ageing population is causing an increased demand for healthcare services that is exceeding clinical capacity [1]
The cataract pathway is an ideal case for optimisation because there is little variability and high levels of patient safety; the most significant complication occurs in fewer than 1 in 1,000 cases [7]. To address this clinical need, this study aims to collect data on the effectiveness, usability, and acceptability of an artificial intelligence (AI) natural language assistant for delivering cataract surgery follow-up checks
The population at the Imperial College site is drawn from the North West London (NWL) Collaboration of Clinical Commissioning Groups (CCGs) and is densely populated, highly diverse, highly mobile, and relatively young [13]
Summary
The UK’s ageing population is causing an increased demand for healthcare services that is exceeding clinical capacity [1]. The solution developed to improve clinical efficiency for cataract surgery follow-up is a natural language, voice telemedicine conversation delivered to patients via telephone call For the patient, this is intended to be no different than a regular telemedicine consultation with a doctor or nurse; it does not require the download of an app, the provision of a device, or any training. Reducing the number of in-person follow-ups will allow clinicians to perform other clinical activities, making patients more likely to receive timely care for their initial cataract surgery or for other conditions. This will be evaluated as part of the study by examining the number of surgeries conducted
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