Background
Our client is an innovative scale-up using AI and NLP to unlock structured and unstructured clinical data and create federated clinical data warehouses. Aggregated data is used to perform Real World Evidence studies.
Challenge
Data delivered to our client from hospitals is often unclean, incomplete and messy. Our client needed help to increase the quality of extracted data and streamline their quality procedures. Getting the data fit for purpose to facilitate Oncology and Cardiology studies by using Natural Language Processing.
Approach
Keyrus facilitated the construction of standard and complaint OMOP CDM databases. Translating business requirements to data engineering teams. Keyrus helped to develop automatic Data Quality tools for effective monitoring and resolving quality problems. Further we implemented quality assurance procedures and developed new processes to tackle Data Quality issues in a pro-active and efficient way.
Key results
Benefits
Our client shifted their Data Quality Management approach from re-active to pro-active quality assurance. Reducing time spend on manual fixes, while capturing hidden errors and Data Quality issues. The implementation of different tools and automatic procedures helped our client towards their goal of shifting from a service to a product.
Technology partners
Azure
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