HL7 data analytics can reduce the time, effort, and cost of extracting actionable insights from healthcare data. In this concise guide to HL7 analytics, you will discover:

  • What HL7 analytics is
  • Benefits of Using HL7 messages for analytics
  • How to create a dashboard from HL7 message data

Let’s start with a brief definition of HL7 analytics.

What Is HL7 Data Analytics?

HL7 is a set of standards and formats for messaging used for managing, integrating, exchanging, and retrieving electronic information between healthcare systems.

HL7 is transaction-based. It is controlled by events such as admittance, discharge, or patient treatment in a clinic. However, it is highly flexible, and it does not have any fixed rules for data storage in an application.

Data analytics is the process of inspecting, cleaning, and modeling data to find trends, discover valuable insights, and draw conclusions about the data. This process is usually performed using a tool that can pull data out from a data source and present it on a dashboard in the form of tables, charts, and graphs.

HL7 data analytics, therefore, is the process of mining useful data from HL7 messages and transforming them into information and insights that are valuable to the healthcare community.

Benefits of Mining HL7 Data

Some of the main benefits of HL7 analytics include:

  • Ability to retrieve data from an unstructured data source
  • Quick access to data on spikes in diseases among the population
  • Capacity to mine data without developing a particular data model first
  • Effective building of real-time dashboards for monitoring different ailments
  • Decision support for staffing the emergency department

How to Create a Dashboard With HL7 Data

Follow these steps to make a dashboard using data obtained from HL7 messages.

  • Deploy a robust interface engine. The interface engine will reduce the work required to create HL7 interfaces and coordinate messages between different healthcare information systems.
  • Use the right tool to extract data from HL7 messages. Various tools have been designed to help you pull out the data you need from HL7 messages. Although messages may be logged and examined manually for data, it is better to deploy a tool to automate the process.
  • Train your staff to use unstructured data repositories. Most data analysts are familiar with SQL and the process of cleansing, transforming, and loading unstructured data into structured databases. However, there’s a trend towards the use of hybrid and unstructured databases like MongoDB and NoSQL. So train the members of your team to set up, query, and manage such databases.
  • Embrace natural language processing (NLP). Unstructured doctor’s notes contain a lot of valuable information. One of the best ways to analyze this data is through NLP. In some cases, doctors’ text notes can provide better conclusions and more accurate trends than structured data.

Take the First Step

Make a definite move to develop and deploy a robust HL7 analytics platform for your organization today. Call Lifepoint Informatics at 877.522.8378 now. Visit our contact page today to discuss your data analytics needs or to book a free demo.