Data mobility for patient journey optimization in oncology
A hospital wants to optimize the oncology patient journey. They have a large database of electronic health records (EHR) from previous patients. Through consulting, they know that by leveraging this database, they could improve the patient journey by detecting pathological signs early; improving and personalizing treatments; and offering guided support.
The EHR data is unstructured, with information collected in text form. This makes analysis challenging at scale. Granting access to external data scientists for AI development involves time-consuming, costly processing steps.
The EHR data is automatically structured through Aindo’s generative AI technology. All involved attributes are recognized automatically and represented in tables. Subsequently, a synthetic database is created to mimic these tables, without containing sensitive information about real patients. This synthetic dataset can readily be transferred to an AI team.
The team uses the synthetic data to build three AI tools: a diagnostic model, helping physicians identify a collection of oncological pathologies; a prognostic model, able to predict the risk of patients developing oncological pathologies based on attributes in their EHRs; and a model that helped optimally administer treatments to oncology patients.
Through the synthetic data’s rapid availability, the project’s duration is only two months. This is a 78% decrease compared to the typical nine-month duration of AI projects in healthcare. This impressive pace is achieved as Aindo removed the need for cumbersome manual data preparation and anonymization processes and protocols. Similarly, the involved budget is significantly reduced compared to previous projects of similar scope.