Our approach to accelerating research is grounded in the principle of data protection by design and default. The technologies and methodologies we develop and used are designed to both enable research whilst simultaneously protecting privacy and confidentiality. This is true innovation.
ELECTRONIC HEALTH RECORDS (EHRs)
Electronic Health Records (EHRs) hold a wealth of information regarding patients (medications, symptoms, diagnoses etc.), and have enormous potential to support patient care. These records are largely logged as free text and without any clear or consistent structure, making it difficult to extract information. Structuring and processing these notes enhance their interoperability and usability (Hong et al., 2018). More specifically, Natural Language Processing (NLP) models have been explored for such structuring. With over 4 million+ patient’s EHRs, at Akrivia, we are committed to structuring mental health records and enhancing mental health research for better patient care and outcome.
A key step to structuring EHRs is identifying clinical concepts and classifying these concepts into defined categories (for example, identifying paracetamol and citalopram as medications).
Understand how Akrivia Health identifies clinical concepts with EHRs and can provide more information on patients.
Sign up to read the Publication
Our approach to accelerating research is grounded in the principle of data protection by design and default. The technologies and methodologies we develop and used are designed to both enable research whilst simultaneously protecting privacy and confidentiality. This is true innovation.
ELECTRONIC HEALTH RECORDS (EHRs)
Electronic Health Records (EHRs) hold a wealth of information regarding patients (medications, symptoms, diagnoses etc.), and have enormous potential to support patient care. These records are largely logged as free text and without any clear or consistent structure, making it difficult to extract information. Structuring and processing these notes enhance their interoperability and usability (Hong et al., 2018). More specifically, Natural Language Processing (NLP) models have been explored for such structuring. With over 4 million+ patient’s EHRs, at Akrivia, we are committed to structuring mental health records and enhancing mental health research for better patient care and outcome.
