Data originates everywhere in the healthcare industry – in laboratories, within insurance agencies, in Electronic Medical Record (EMR) systems, etc. – and each of these sources represents a valuable set of information. Healthcare professionals need a comprehensive method that collects and transfers data across systems in a way that is reliable, secure and compliant. But because healthcare system formats are rarely ever the same, it becomes challenging to securely and efficiently share data between vast and diverse data and information sources.
To help the healthcare industry with data collection and management, standards development organization Health Level Seven International (HL7) provides standards for the exchange of electronic health records between software applications used by healthcare providers, insurance companies, government agencies, vendors, patients, and others in the healthcare industry. HL7 assists with allowing entities to securely access and use the applicable health data of patients when and where they need it.
Semantic graph technology could take the HL7 standards a step further, overcoming the challenges presented by the traditional data lake – a repository of raw data containing a mess of structured and unstructured data that, until recently, has had unrecognized value to companies. But with new tools such as graph databases, businesses could now more easily link, analyze and manage large quantities of data.
Using semantic graph technology, healthcare professionals could efficiently collect, analyze and extract meaning from the electronic health data being transferred from party to party, providing its healthcare customers with valuable insight, such as demographic profiles, disease population and epidemiology. This is essential to the growth and development of clinical trials and medical research studies.
With the ever-changing landscape of healthcare regulations and standards, such as HL7, it is imperative that data discovery and analysis processes adapt accordingly to continue contributing to the advancement of interoperability in the health IT space.
To learn more about smart data lakes, download the whitepaper "Anzo Smart Data Lake: Enterprise Graph-based Data Discovery, Analytics and Governance".
