Mixed-methods formative evaluation shows favorable user sentiment and clinical utility of AI platform.
Abstract ObjectivesTo describe the design of a UK-centred retrieval-augmented clinical reference platform, and report early real-world adoption and perceived clinical utility from a formative implementation evaluation.MethodsWe conducted a mixed-methods study comprising (i) a retrospective observational analysis of platform usage across web, iOS and Android over 16 weeks and (ii) a cross-sectional, in-product intercept survey. Usage data (unique users, engagement events, clinical queries) were sourced from Google Analytics 4, Apple App Store Connect and Google Play Console. A client-side script randomised survey prompts to ~ 10% of web sessions, displaying single items from a predefined battery. Proportions are reported with Wilson 95% confidence intervals; qualitative comments underwent thematic content analysis. No personal identifiers were collected.ResultsThe web application attracted 19,269 unique users and 202,660 engagement events (~ 10.5 per active user), with approximately 40,000 clinical queries across platforms. The intercept survey yielded 1,223 item-level responses. Among respondents: useful 86.2% (50/58); time saved 60.9% (14/23); would use again 93.3% (14/15); would recommend 88.4% (38/43); perceived accuracy 75.0% (30/40); perceived reliability 79.4% (27/34). Themes highlighted speed, guideline-grounded answers and UK specificity.DiscussionFindings provide formative signals of value for rapid, provenance-bound information retrieval. Key limitations include small item-level Ns, early-adopter/selection bias, and absence of gold-standard accuracy benchmarking; results should not be interpreted as evidence of clinical effectiveness.ConclusionA safety-governed, RAG-based platform showed early uptake and favourable user sentiment among UK clinicians. Prospective evaluations, time-and-motion studies and objective accuracy/safety audits, are warranted to assess impact on clinical workflows and care quality.
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Kolawole Tytler (2025) studied this question.
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