Feasibility of an artificial intelligence phone call for postoperative care following cataract surgery in a diverse population: two phase prospective study protocol
Bibliographic record
Abstract
INTRODUCTION: Artificial intelligence (AI) development has led to improvements in many areas of medicine. Canada has workforce pressures in delivering cataract care. A potential solution is using AI technology that can automate care delivery, increase effectiveness and decrease burdens placed on patients and the healthcare system. This study assesses the use of 'Dora', an example of an AI assistant that is able to deliver a regulated autonomous, voice-based, natural-language consultation with patients over the telephone. Dora is used in routine practice in the UK, but this study seeks to assess the safety, usability, acceptability and cost-effectiveness of using the technology in Canada. METHODS AND ANALYSIS: This is a two-phase prospective single-centred trial. An expected 250 patients will be recruited for each phase of the study. For Phase I of the study, Dora will phone patients at postoperative week 1 and for Phase II of the study, Dora will phone patients within 24hours of their cataract surgery and again at postoperative week 1. We will evaluate the agreement between Dora and a supervising clinician regarding the need for further review based on the patients' symptoms. A random sample of patients will undergo the System Usability Scale followed by an extended semi-structured interview. The primary outcome of agreement between Dora and the supervisor will be assessed using the kappa statistic. Qualitative data from the interviews will further gauge patient opinions about Dora's usability, appropriateness and level of satisfaction. ETHICS AND DISSEMINATION: Research Ethics Board William Osler Health System (ID: 22-0044) has approved this study and will be conducted by guidelines of Declaration of Helsinki. Master-linking sheet will contain the patient chart identification (ID), full name, date of birth and study ID. Results will be shared through peer-reviewed journals and presentations at conferences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.058 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".