Use of an ambient artificial intelligence tool to improve quality of clinical documentation

Abstract

Background: Electronic health records (EHRs) have contributed to increased workloads for clinicians. Ambient artificial intelligence (AI) tools offer potential solutions, aiming to streamline clinical documentation and alleviate cognitive strain on healthcare providers.

Objective: To assess the clinical utility of an ambient AI tool in enhancing consultation experience and the completion of clinical documentation.

Methods: Outpatient consultations were simulated with actors and clinicians, comparing the AI tool against standard EHR practices. Documentation was assessed by the Sheffield Assessment Instrument for Letters (SAIL). Clinician experience was measured through questionnaires and the NASA Task Load Index.

Results: AI-produced documentation achieved higher SAIL scores, with consultations 26.3% shorter on average, without impacting patient interaction time. Clinicians reported an enhanced experience and reduced task load.

Conclusions: The AI tool significantly improved documentation quality and operational efficiency in simulated consultations. Clinicians recognised its potential to improve note-taking processes, indicating promise for integration into healthcare practices.

Keywords: Ambient; Artificial intelligence; Clinical documentation; Consultation; Electronic health records.

Fig. 1

This paper reports on findings from the second phase of a multiphase trial of an ambient AI tool. The results from this phase (Phase 2) have informed the protocol design for Phase 3 (currently ongoing), in turn, the results of Phase 3 will inform the Phase 4 protocol.

Fig. 2

Speech is recorded by the AI tool on the user's device. Before the recording is transferred to a local region cloud provider for transcription, the Personal Health Information (PHI) relating to the patient is redacted. The PHI is reinstated and the transcription is used to generate a clinic note and letter on the user's device, these can be copied and pasted into the EHR patient record.

Fig. 3

A visual representation of the rotation study structure for the morning and afternoon sessions, n = 4 clinicians in each session and a schedule used for the study (‘NO’ referring to the ‘Station break’). Patient/parent actor pairs rotated clockwise around the stations, moving at the conclusion of each 20-min consultation window until they had visited all four stations. Clinicians and their corresponding human timers remained stationary for the full rotation and had one break during the rotation.