One of the most overwhelming challenges for hospitals is to collect, manage and store thousands of health data points per day. Such data ranges from unstructured free text to images and waveforms to data from sensors and monitoring devices. Similarly, analyzing patient data for clinical decision support, treatment, medication and drug interactions is very critical to impact the patient outcome.
The traditional relational database alone cannot analyze patient data quickly, nor can hospital data be easily combined with external data sources such as those from pharmaceutical companies and researchers. To overcome such challenges, we extensively use knowledge graph database of our AI applications.
Deploying knowledge graphs in the healthcare services space has proven to be an effective method to map relationships between the enormous variety and structure of healthcare data. Graphs provide an uncanny ability to model latent relationships between information sources and capture linked information (i.e., entity relationships) that other data models fail to capture. This enables doctors and service providers to more easily find the information they need among a wide array of variables and data sources.