Use case
AI Clinical Documentation
AI clinical documentation uses speech recognition and language models to help clinicians capture the clinical encounter — generating draft notes, structured summaries, and coded data that the clinician reviews and signs. The aim is to reduce time spent on paperwork so more attention returns to the patient, with the clinician always in control of the final record.
FAQ
Frequently asked questions
- Does AI documentation replace the clinician's notes?
- No. It produces a draft the clinician reviews, edits, and signs. The clinician remains responsible for the final record.
- What is ambient clinical documentation?
- Ambient documentation listens to the natural clinician–patient conversation and drafts a structured note from it, rather than requiring manual typing or dictation commands.
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Solution
AI Clinical Assistance
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Healthcare Automation
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Healthcare Automation
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Clinical Intelligence
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AI in Healthcare
Learn moreWorkflow
Clinical Documentation
Clinical documentation is the workflow by which clinicians record the details of a patient encounter — history, examination, assessment, and plan — into the medical record so that care is accurate, complete, and shareable across the team. It typically spans capturing the encounter, structuring the note, coding and review, and finalizing the record. Intelligent healthcare systems can support documentation by helping draft and structure notes from the conversation and by reducing repetitive data entry, with the clinician reviewing and signing off.
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EHR
An electronic health record (EHR) is a digital version of a patient's chart — a real-time, patient-centred record that makes information available securely to authorized users. Unlike a single-practice electronic medical record (EMR), an EHR is built to be shared across providers and care settings to support coordinated, longitudinal care.
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Clinical Intelligence
Clinical intelligence is the discipline of converting raw clinical data into timely, actionable insight and delivering it to the right clinician at the right point in the workflow. It overlaps with clinical decision support but emphasizes the data, models, and surfacing layer that make an insight relevant and trustworthy in context.
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