Healthcare Data Management:
- Interoperability (exchange and use)of data between providers and health systems
- Data access from a variety of source
- Adapting and scaling data services with flexibility
Overcoming the challenges of EHR with superior data model- Data Lake Approach
Despite the potential benefits of EHRs (electronic health records) of a digital storage of healthcare information throughout an individual’s lifetime with the purpose of supporting continuity of care, education, and research, the limitations of cost constraints, technical and standardization limitations are inherent barriers. The volume and variety of data needed for an effective PHM (Population health management) require highly structured data which is not available either with EHRs or EDWs.
In an evolving healthcare industry, where information is integral to the success of any system, the traditional “Data Warehousing” needs to be replaced with “Data Lake”.
In 2010, James Dixon came up a new architecture for data management;he was instrumental in coining the term “Data Lake”with his cutting-edge idea. In simple terms, Data Lake is a storage repository that holds vast amount of data, both structured and unstructured that is easily accessible, scalable and storable. The key differences with data warehousing are:
- Data Lake can accommodate both structured & unstructured data
- It can store data in any format, unlike data warehousing that needs modelled data before use
- Offers easy configuration on the go that is not possible with highly structured data warehousing
- Costs much less to store data
Data Lake allows an effective PHM model with its flexibility, versatility, scalability and cost effective approach. With the vast amount of unstructured data that is growing at a rate of 48% every year, the future of healthcare looks more promising with the Data Lake approach to drive clinical accuracy, quality and operational efficiency.