Ambient Scribe in General Practice: a Multi-perspective Before-after Longitudinal Mixed-methods Study

Introduction Over the years the introduction of the electronic health record (EHR) and escalating demands for documentation have led to a mounting burden on GPs. Clinical documentation is a major barrier to efficient patient care as last year more than half the GPs spend more than 20% of their time on administrative duties. This burden leads to reduced job satisfaction and well-being, increased rates of burnout, and employee attrition. The documentation burden also negatively influences patient-provider communication. It leads to GPs making less eye contact, having a more closed body posture, and conveying less information to their patients. The documentation in the EHR however also has positive effects, such as reduced cognitive load and improvements in patient safety and care. Reducing the provider-computer interaction during the consultation may improve patient-provider interaction and provider well-being while retaining the benefits of EHRs.

In fact, studies showed that employing medical assistants for documentation during consultations leads to an increase in face-to-face time and improves patient satisfaction. Moreover, speech-to-text technologies for dictation after the consultation alleviated the documentation burden by increasing documentation speed, patient experience, and provider satisfaction. These interventions may lower burnout and attrition rates, strengthening the well-being of the providers as well as that of the healthcare sector.

The rapid advancement of large language models (LLMs) has opened new avenues to reduce documentation burden. LLMs, such as ChatGPT, are artificial intelligence (AI) models that can interpret and generate text. These models can transcribe and summarize a consultation with the GP in real-time removing the need for medical assistants or dictation after the consultation. However, if the LLM makes mistakes, this may lead to increased administrative workload. The investigators aim to assess the effect of a AI-based transcription and reporting tool in general practice.

Objectives Our primary objective is to assess the effect of a transcription and reporting tool on time spent on clinical documentation in general practice.

Secondary objectives are to assess the effect of a transcription and reporting tool in general practice on