AI scribes and the changing shape of clinical documentation

AI scribes, such as Heidi and Tortus AI, are being increasingly used in GP surgeries across the country. They promise to reduce documentation burden by capturing consultations in real time, then automatically transcribing and summarising them into notes that can be integrated into patient records. Having used AI scribes in my day-to-day practice, I can see their potential. The reality is more nuanced.

One of the most meaningful benefits is the ability to focus more fully on the patient. Too often I have found myself typing while a patient is speaking, despite being trained not to do this. AI scribes significantly reduce this need. They allow GPs to be more present in the consultation and engage more naturally with patients.

Emerging data generally supports the use of AI scribes. A study by Modality Partnership, analysing over 2,800 consultations using Heidi, reported a 51% reduction in documentation time and a 61% reduction in after-hours administrative work. Clinicians also reported feeling less stressed and better able to build relationships with patients.¹ Broader systematic reviews echo these findings, although they highlight the need for clinician oversight due to occasional inaccuracies.² ³

In my own experience, I have noticed these inaccuracies. They are often minor, for example a misheard word, but can change the meaning of the whole consultation. Outputs still require careful review. At times this can feel comparable in effort to writing notes from scratch. This raises the question of whether the burden of documentation is actually reduced, or simply redistributed.

I have also found that it takes some adjustment to verbalise examination findings for transcription. It can feel slightly unnatural and can disrupt the flow of the consultation. I suspect this will improve with experience and wider adoption. There are also further practical challenges, notably around capturing and integrating numerical data and coding within the clinical systems.

Integration with existing clinical systems is a key determinant of adoption. It reflects a wider challenge across health technology in the NHS. In practice, the AI scribes I used sat alongside rather than within core clinical systems. Additional steps were still needed to complete routine tasks. Prescriptions, for example, could not be generated within the scribe tools I trialled. I still needed to switch back to the clinical system to prescribe, while the AI-generated consultation note remained separate. The final patient record was not always produced as a single, seamless output. Consultation notes and prescribing activity sat together but were not fully integrated in how they were generated. This made the workflow feel clunky rather than fully embedded in practice. On a minor note, the generated notes did not always match my usual documentation style, so some editing and reformatting was still needed.

It is also worth recognising that writing notes serves an important clinical purpose beyond administration. It supports clinical reasoning, reflection, and decision-making. With AI-generated documentation, the cognitive process shifts towards reviewing and confirming outputs. That has implications for workflow and clinical practice, and it is perhaps one of the more under-discussed changes.

We cannot talk about AI scribes without mentioning governance and data security. GDPR obligations require that patient information is handled accurately, stored securely, and stays fully auditable, particularly when third-party tools are involved. This also extends to medico-legal accountability and clear traceability of how consultation records are generated and edited. NHS England's Ambient Voice Technology Self-Certified Supplier Registry is one step towards a more transparent procurement landscape.

These technologies are evolving quickly, and many of the current limitations are likely to improve over time. There is clear potential to reduce administrative burden and enhance the consultation experience for both patients and clinicians, particularly as integration, accuracy and usability continue to develop. Looking ahead, AI in primary care is beginning to extend beyond documentation, with triage systems, workflow optimisation, and clinical decision support all advancing. AI is set to play an increasingly significant role in general practice, making it essential that these tools are evaluated in real-world practice, with open discussion of their impact and shared learning between clinicians. Their value will depend on how well they integrate into existing clinical systems and workflows, and their adoption should remain thoughtful and appropriately cautious.

References

Medicine Central is a clinical evidence review for UK primary care clinicians. Content reflects evidence current at time of publication and should be read alongside local formulary and clinical guidance. For healthcare professionals only.