Most healthcare data like EHR, claims, socioeconomic, etc are aimed to aid a specific practice domain that is disparate and diverse. Hence these are difficult to operate under a single roof for certain purposes. So, to collect and maintain all these types of data at a single place for performing analytics and machine learning on top of it, we need a common data model approach to support this.
Predera Data model (PDM) is a healthcare common data model designed to facilitate data interoperability which is built on top of OMOP CDM and extended using FHIR data standard is used to support observational analytics on top of healthcare data.
OMOP CDM allows for the systematic analysis of disparate observational databases and making disparate coding systems be harmonized with minimal information loss into a standardized vocabulary.
In order to populate the PDM
To perform all this data ETL, stitching, etc. Apache Spark has been used.
Predera Data Model (PDM) seamlessly works with all data formats as it implicitly contains all the resources supported by FHIR which is an industry-standard.
This is designed to evolve with different data sources and formats coming in to support various applications and usages.
Using the data residing in PDM things such as feature stores, machine learning models, rule engines can be seamlessly built and various analytics can also be performed.