Background
International Medical Graduates (IMGs) are a key component of the NHS workforce, particularly in primary care, but often face challenges adapting to UK clinical systems, workflows, and guideline-based practice. Umbil is an AI-enabled workflow and learning support platform designed to provide rapid access to UK guideline-aligned information while supporting workplace learning and clinical workflow tasks.
Figure 1: Simplified Umbil Workflow demonstrating conversion of clinical questions into workplace learning and reflective practice.
QR code for Umbil AI
Aim:
To evaluate the early feasibility and clinician engagement of an AI-enabled workflow and learning support platform that supports clinicians, including IMGs, in UK primary care
Methods:
An early feasibility evaluation was conducted using real-world usage data from Umbil users across UK clinical settings.
The platform provides rapid access to trusted UK guidance-aligned information, alongside features such as clinical question answering, referral support, safety-netting assistance, patient communication tools, and GMC-linked learning capture. Engagement metrics, including user registrations, clinical queries, learning capture events, workflow tool use, and website traffic, were analysed during the initial evaluation period.
Results:
The platform demonstrated substantial clinician engagement and early commercial traction. Total AI infrastructure costs were approximately £18.37. This suggests strong early clinician uptake and demonstrates the feasibility of delivering AI-enabled workflow and learning support at a low operational cost.
| Metric | Value |
| Registered clinicians | 818 |
| Clinical queries submitted | >28,000 |
| Learning events captured | 957 |
| Website visitors (previous 30 days) | 2,414 |
| Paying subscribers | First paying subscribers secured |
| AI infrastructure cost | Approximately £18.37 |
Table 1: Early clinical engagement and workflow utilisation metrics from the Umbil platform evaluation.
Discussion:
The engagement metrics suggest strong early clinician uptake, with 818 registered clinicians and more than 28,000 clinical queries indicating sustained real-world use. For IMGs, rapid access to UK guideline-aligned information may help support transition into UK primary care by reducing uncertainty and improving familiarity with local clinical processes. The capture of workplace learning events suggests potential to integrate clinical support with professional development. The low infrastructure cost observed during the evaluation period suggests that AI enabled workflow support may be scalable across a wider healthcare workforce.
Limitations include the observational nature of the evaluation and the absence of formal education or clinical outcome measures. Although the platform was designed to support clinicians, including IMGs, IMG specific outcomes were not evaluated in this early feasibility study.
Conclusion:
Early real-world evaluation demonstrated meaningful clinician engagement, successful early adoption, and low operational costs. These findings support the feasibility of AI-enabled workflow and learning support within UK primary care and justify further evaluation of educational and clinical outcomes.
References:
- General Medical Council. The state of medical education and practice in the UK. GMC; 2025.
- NHS England. International medical graduates in the NHS workforce. NHS England; 2024.
- National Institute for Health and Care Excellence (NICE). Clinical Knowledge Summaries. Available at: NICE CKS.
- Topol E. Preparing the healthcare workforce to deliver the digital future. NHS Health Education England; 2019.
- Wachter RM. The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age. McGraw Hill; 2015.
- Sutton RT, Pincock D, Baumgart DC, et al. An overview of clinical decision support systems. BMJ Health Care Inform. 2020;27:e100122.
