ArticleCureus2026
Evaluation of an AI-Assisted Virtual Fracture Clinic System: Common Referral Deficiencies and Outcomes Following Automated Feedback in a Major Trauma Centre.
Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionVirtual fracture clinics (VFCs) rely on accurate and complete referral information to facilitate effective clinical review. Deficiencies in documentation or errors in the selection of the referral pathway result in additional clinical work. This study evaluated an artificial intelligence (AI)-assisted VFC referral feedback system that checks referrals against predefined local VFC criteria and provides automated feedback when required information is missing. We discuss the common referral deficiencies identified and improvements to referrals following automated feedback.
methodsA retrospective service evaluation was conducted at Imperial College Healthcare NHS Trust between 18 December 2025 and 26 March 2026. Referrals were made using a cloud-based system called FollowUp.Clinic, which incorporates an AI-assisted referral feedback function. The system assesses referrals against predefined local VFC criteria and generates automated feedback when deficiencies are identified. This allows referrers to amend their submissions before clinical review. No patient-identifiable information was transmitted to OpenAI (San Francisco, CA, USA) through the API.
resultsA total of 1,201 referrals were processed. Of these, 901 (75.0%) met the predefined referral criteria without requiring modification, while 300 (25.0%) were flagged for modification. The most frequent deficiency was specific imaging not being requested, identified in 167 referrals (55.7%), followed by missing venous thromboembolism (VTE) documentation in 70 (23.3%), insufficient clinical information in 65 (21.7%), and an incorrect referral pathway in 18 (6.0%). Categories were not mutually exclusive, as referrals could contain multiple deficiencies. Following one round of automated feedback, 282 of 300 initially flagged referrals (94.0%) were amended and subsequently progressed through the VFC pathway. The remaining 18 (6.0%) required redirection to an alternative pathway. DISCUSSION: Most referral deficiencies identified by the system were modifiable, with 94.0% successfully corrected following a single round of automated feedback. Specific imaging not being requested represented the most frequent deficiency. Missing required imaging could add extra steps before treatment. Missing VTE documentation was the second most frequent issue. In the absence of documented VTE assessment, the VFC team had to assume that this had not been completed, with potential implications for patient safety.
conclusionThe AI-assisted referral system identified common, potentially modifiable deficiencies in VFC referrals, with 94.0% of initially flagged referrals successfully corrected following a single feedback cycle.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.