Healthcare has a data problem. There’s too much raw data, too many silos and not enough insight.
HL7, the industry data standard, is part of the problem. It is very complex and this makes it difficult for non-technical users to get value from data such as:
Imagine if you could turn all that data into a 360-degree view of the patient and make it available to the people who need it.
HL7 is an evolving standard. Integrating different systems means normalizing HL7 data using different versions of the format.
CloverDX works with all versions of HL7 v2.x using the HL7 Consortium's XSDs to parse data. Just change the XSD to support a different version.
Build automated data pipelines with CloverDX to integrate HL7 data into your workflows.
CloverDX reads incoming data, ingests it and maps it into another format, such as JSON. CloverDX adds error and exception handling to keep your data pipelines flowing.
Instead of building custom solutions, use CloverDX as a universal integration platform. Connect hospitals, labs, devices, financial data and medical records using the same tool.
Doctors and administrators need the information buried in HL7 data. But they don't have the technical skills to extract it.
CloverDX lets you create portals in familiar tools like Excel to request or update the data. CloverDX automatically translates the information.
A healthcare technology company manages a platform that aggregates and normalizes clinical data. This lets organizations use information from any certified EHR in other applications.
The platform has to ingest HL7 data from different providers and in different formats. Then it has to map it into other formats for use downstream.
Their CloverDX solution automates the whole process:
The whole system has to be scalable enough to handle very large data volumes, processing approximately 5,000 messages a minute.
The foundation of 'data analytics' is the data itself. As they say, 'garbage in, garbage out'. If you're struggling to reconcile and analyze information from many sources, CloverDX can help.
Map different data streams into more accessible formats. For example, you can pull HL7 data sources into business intelligence tools or Excel.