Autonomous Telephone Follow-up After Cataract Surgery

A Clinical Investigation of an Autonomous Phone Conversational Agent for Cataract Surgery Follow-up

This project will apply AI technology to meet the gap between increasing demand and limited capacity of high- volume healthcare services. The project will develop evidence that will support the safe deployment of Ufonia's automated telemedicine platform to deliver calls to cataract surgery patients at two large NHS hospital trusts.

The proposed study will implement DORA in addition to the current standard of care for a cohort of patients at Imperial College Healthcare Trust and Oxford University Hospitals NHS Foundation Trust. The study will evaluate the agreement of DORA's decision with an expert clinician. In addition it will test the acceptability of the solution for patients and clinicians; the sensitivity and specificity of the system in deciding if a patient requires additional review; and the health economic benefits of the solution to patients (reduced time and travel) and the local healthcare system. If successful, a proposal will be developed to roll the solution out to all patients at each site in anticipation of an application to a late phase award for wider NHS deployment.

Study Overview

• Status: Completed
• Conditions: Cataract; After Cataract
• Intervention / Treatment: Other: Dora

Detailed Description

Background

Due to an ageing population and increased expectation, 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 establish efficacy of DORA's decision making in comparison to an expert human clinician; baseline sensitivity and specificity for detection of true complications; evaluation of patient acceptability; evidence for cost-effectiveness; and to capture data that may support further studies.

Project plan and methods used

Based on implementation science, the interdisciplinary study will be a mixed-methods phase one pilot establishing inter-observer reliability; as well as usability and acceptability.

Timelines for delivery

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.

Anticipated Impact and Dissemination

The project's key contributions will be evidence on artificial intelligence voice conversational agent effectiveness, and associated usability and acceptability. Results will be disseminated in peer-reviewed journals and at international medical sciences and engineering conferences.

• Study Type: Interventional
• Enrollment (Actual): 225
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United Kingdom
  • London, United Kingdom
    • Imperial College Healthcare NHS Trust
  • Oxford, United Kingdom
    • Oxford University Hospitals NHS Foundation Trust

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• Willing and able to provide informed consent;
• Aged 18 years or older;
• On the waiting list for routine cataract surgery. Cataract surgery as part of a combined procedure with other ocular surgery will not be included;
• No history or presence of significant ocular comorbidities that would be expected to alter the risks of cataract surgery or normal post-operative follow-up schedule. Note that significant ocular comorbidities do not include stable, chronic, or inactive ocular conditions such as amblyopia, drop-controlled stable glaucoma or ocular hypertension, previous squint surgery, inactive macular pathology, previous refractive surgery, or previous vitreoretinal surgery with stable retina.

Exclusion Criteria:

• Individuals with any condition that could preclude the ability to comply with the study or follow-up procedures;
• Presence of ocular or systemic uncontrolled disease (unless deemed not clinically significant by the Investigator and Sponsor);
• Involved in current research related to this technology or been involved in related research to this technology prior to recruitment;
• Cognitive difficulties, hearing impairment or non-English speakers;
• History of current or severe, unstable or uncontrolled systemic disease (unless deemed not clinically significant by the Investigator and Sponsor).

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Screening
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

Participant Group / Arm

Intervention / Treatment

Experimental: Dora follow-up phone call; DORA uses a variety of AI technologies to deliver the patient follow-up call, including: speech transcription, natural language understanding, a machine-learning conversation model to enable contextual conversations, and speech generation. Together, these technologies cover the input, processing and analysis, and output needed to maintain a natural conversation. DORA is configured to deliver calls through a telephone connection as a real-time, stand-alone system: the operator inputs individual patient details to initiate the call and completes a summary in the electronic health record (EHR) afterwards. The entire conversation will be supervised by a clinician. This clinician will be able to interrupt the call at any point if the system fails, the patient struggles to interact with it, or DORA does not collect sufficient information from the patient. The clinician will record a clinical assessment which will be compared to the DORA assessment.

Other: Dora; DORA uses a variety of AI technologies to deliver the patient follow-up call, including: speech transcription, natural language understanding, a machine-learning conversation model to enable contextual conversations, and speech generation. Together, these technologies cover the input, processing and analysis, and output needed to maintain a natural conversation. DORA is configured to deliver calls through a telephone connection as a real-time, stand-alone system: the operator inputs individual patient details to initiate the call and completes a summary in the electronic health record (EHR) afterwards.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement	Inter-rater reliability: the degree of agreement between DORA and the clinician on their assessments of the individual symptoms and the management plan; Whether or not the clinician had to interrupt the call to ask clarifying questions	Inter-rater reliability was assessed based on data collected during Dora calls, which lasted an average of 7.5 minutes

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Clinical Complications Identified or Missed by DORA System	Clinical data was collected from patients' electronic health record (EHR) up to 90 days postoperatively to capture numbers of participants 'recommended discharge' by Dora R1 with subsequent unexpected management change	Up to 90 days post surgery
Calls Completed Without Intervention	Number of autonomous calls that were completed without needing any intervention from the supervising clinician; Clinician-reported reasons for asking clarifying questions	Dora calls lasted an average of 7.5 minutes
System Usability	Measured using the System Usability Scale (minimum of 0, maximum of 100, higher scores indicate better usability)	Usability assessments were completed up to 6 months after the Dora call
Usability of Telehealth System Implementation	Measured using the Telehealth Usability Questionnaire (minimum score of 1, maximum score of 5, averaged across 19 items; higher scores indicate better usability)	Usability was assessed up to 6 months after the call
Qualitative Patient Perspectives of Usability	Qualitative feedback from semi-structured interviews	Semi-structured interview call, lasting up to 30 minutes, conducted up to 6 months after the Dora call
Acceptability of AI Follow-up Phone Call	Qualitative feedback from semi-structured interviews	Semi-structured interview call, lasting up to 30 minutes, conducted up to 6 months after the Dora call
Satisfaction With AI Follow-up Phone Call	Qualitative feedback from semi-structured interviews	Semi-structured interview call, lasting up to 30 minutes, conducted up to 6 months after the Dora call
Appropriateness of AI for Follow-up Assessment	Qualitative feedback from semi-structured interviews	Semi-structured interview call, lasting up to 30 minutes, conducted up to 6 months after the Dora call
Cost Impact	A cost analysis compared the direct costs of face-to-face (F2F) follow-up at Imperial with Dora R1 (in Oxford, patients do not have routine postoperative follow-up). Assumptions included annual costs for various healthcare professionals and the duration of F2F follow-up appointments (estimated at 30 min).	Conducted 6 months after baseline
Subsequent Unplanned Follow-up	Clinical data was collected from patients' electronic health record (EHR) up to 90 days postoperatively to capture numbers of participants 'recommended discharge' by Dora R1 with subsequent unplanned review.	Up to 90 days post surgery

Collaborators and Investigators

• Sponsor: University of Plymouth
• Collaborators: Imperial College London; University of Oxford
• Investigators: Study Chair: Eduardo Normando, MD, PhD, Imperial College London

Publications and helpful links

General Publications

• Milne-Ives M, de Cock C, Lim E, Shehadeh MH, de Pennington N, Mole G, Normando E, Meinert E. The Effectiveness of Artificial Intelligence Conversational Agents in Health Care: Systematic Review. J Med Internet Res. 2020 Oct 22;22(10):e20346. doi: 10.2196/20346.
• de Pennington N, Mole G, Lim E, Milne-Ives M, Normando E, Xue K, Meinert E. Safety and Acceptability of a Natural Language Artificial Intelligence Assistant to Deliver Clinical Follow-up to Cataract Surgery Patients: Proposal. JMIR Res Protoc. 2021 Jul 28;10(7):e27227. doi: 10.2196/27227.

Study record dates

Study Major Dates

• Study Start (Actual): September 17, 2021
• Primary Completion (Actual): January 31, 2022
• Study Completion (Actual): March 24, 2022

Study Registration Dates

• First Submitted: November 8, 2021
• First Submitted That Met QC Criteria: January 15, 2022
• First Posted (Actual): January 28, 2022

Study Record Updates

• Last Update Posted (Actual): March 25, 2025
• Last Update Submitted That Met QC Criteria: January 8, 2025
• Last Verified: January 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Eye Diseases; Lens Diseases; Cataract; Capsule Opacification
• Other Study ID Numbers: 21WE6780

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No