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Deriving value from documentation
Clinical documentation is extensive across care delivery, but too often data recorded is unstructured, lacks format and does not deliver its true value. Natural Language Processing (NLP) technology transcribes verbal clinician notes and then uses AI to detect and categorise themes.
Miya Language improves the scope and quality of clinical coding by highlighting additional information from the patient record, allowing clinicians to validate and extend the narrative describing the patient condition. It provides immediate impact on the quality and completeness of clinical documentation and coding.
How it works
Smart clinical documentation
Using the capabilities available with NLP, Miya Language assesses all data available across all documentation types to suggest terms and concepts that will enrich the information in the patient record and contribute to improved clinical coding.
Clinical documentation is scanned in real-time to identify clinical risk and highlight areas of complexity. Identified concepts are highlighted in the documentation referencing a standard terminology such as SNOMED-CT and can instantly be added to the patient’s record.
How AI will change patient care
Our Chief Medical Officer, Dr Malcolm Pradhan, recently spoke to Australian Health Journal about the possibilities of using Artificial Intelligence (AI) to monitor and help triage patients.
Concepts are identified from free text using real-time NLP. Clinicians confirm the concepts in the documentation process and instantly add their findings to the patient record. Application of learning models continuously improve the process.
The structure of the noting can be configured to adapt to findings as the documentation is progressed and evaluated. This dynamic process results in complete documentation and supports alignment with local best practice.
Concepts are categorised to SNOMED as the reference terminology for CDS. These terms can be mapped to ICD-10 and other local idioms used to classify care delivery.
Application of clinical decision support, as well as decision theoretic and statistical models, contributes to the continuous improvement and efficacy of the process. This delivers increased value and depth to the patient record.
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