https://store-images.s-microsoft.com/image/apps.11667.ad702fda-eba9-45f9-92c3-322bba5f0165.aa19dec1-895f-4395-a4e0-d24f6c67b60b.8e81adc9-6100-4e05-9175-affa4ebf1863
Autoscriber Ambient
Autoscriber BV
Autoscriber Ambient
Autoscriber BV
Autoscriber Ambient
Autoscriber BV
Automated clinical note taking for healthcare professionals
Automatic clinical data capture from speech
Never type notes manually into your electronic health record again. While you can stay focused on the patient, our AI engine turns the conversation into a structured summary that includes medical codes such as SNOMED-CT and ICD-10 codes.
Our AI software automatically generates high quality, structured clinical notes directly from the audio of the conversation between doctor and patient. Unlike other solutions, we do not require the doctor to dictate notes word-for-word, but use our intelligent engine to understand unstructured speech and convert this into a variety of clinician-approved reports in real time.
Key benefits
- Time saving: Our software results in significant reduction in admin time for healthcare practitioners, and reduces follow up calls and visits by patients
- Data quality: We automatically extract discrete medical data from the conversation for better registration at the source
- Job satisfaction: Administrative overload is one of the leading causes for burnout and attrition for healthcare practitioners. We relieve that burden.
- Improved patient outcomes: Because notes are automatically generated, we can deliver them in a structured, standardised way so that they can be reused by clinical researchers to improve healthcare. Furthermore, with the confidence that Autoscriber will take clinical notes automatically in the background, the doctor is free to pay full attention to the patient, leading to a more empathetic doctor-patient relationship, which has been linked to better patient outcomes.
Autoscriber B.V. is fully GDPR compliant, ISO 27001 and NEN 7510 certified.
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https://store-images.s-microsoft.com/image/apps.385.ad702fda-eba9-45f9-92c3-322bba5f0165.aa19dec1-895f-4395-a4e0-d24f6c67b60b.74943903-ffb5-4b30-8a26-8322bfa46299