Healthcare organizations use complementary tools and solutions within their workflows which can disrupt the flow of data. In each of these applications, the data is locked in siloes, resulting in disconnected workflows. Data integration can help break down siloes and make data more accessible.
Data integration in healthcare is necessary so that users and applications have access to complete and comprehensive data. Implementing data integration has many benefits like eliminating data siloes, improving workers’ collaboration, better decision-making, and increased quality and integrity of data.
Data Integration Techniques
There are many data integration techniques, and healthcare providers can choose from various applications based on the type of integration they want. Listed below are the most popular data integration techniques used by experts.
1. Manual Integration
Manual data integration is the most basic method for integrating data. In this technique, analysts and engineers write code to collect, transform, and consolidate data.
While manual integration may seem simple, this method is only feasible when dealing with a small number of data sources. It does not require investing in any software, but it is time-consuming. If you want to scale the integration to include other data sources, manual integration is not a good solution.
2. Middleware Integration
Middleware is like a negotiator. It sits between the source and target systems, ingesting data, transforming and validating it, and sending it to the target system. Sometimes it’s called glue software because it links two or more applications that do not integrate well with one another.
This technique is beneficial when integrating older systems to new ones. Middleware transforms the legacy data into a format that the more recent systems can use.
However, using middleware can cause maintenance issues. Knowledgeable developers need to deploy and maintain them. Middleware also has limited compatibility with source applications.
3. Data propagation
Data propagation is a fancy way of saying “copy.” An application copies data from one location to another whenever an event triggers it. Enterprise Application Integration (EAI) and enterprise data replication (EDR) technologies enable this type of “copy.”
EAI gives you a bridge between two systems, for instance if you need to perform business transactions as part of your project. EDR typically moves data between two databases, although, unlike ETL, it only involves copying data, not performing any kind of transformations on it to make it compatible with the new system.
4. Data warehousing
This data integration technique uses a shared storage location where the data is cleansed, formatted, and stored. Data warehousing is sometimes referred to as common storage integration.
The data from all the different software inside the organization is copied to the data warehouse, where data analysts can query it. Analysts won’t have to worry about affecting application performance through data warehousing. Analysts can also view the data from a central location, checking data accuracy and consistency.
- Data virtualization
Data virtualization is pretty similar to taking all the groceries from various stores and putting them all inside a house so that you can see all of them at one time without having to go around looking for them. Data virtualization essentially helps make it easier for users to get a unified view of information from multiple relational databases.
If you think your data is going to be duplicated multiple times as a result of data virtualization, then you may want to think again. Your data remains in the source databases, and there is no need for extra copies to be created, so you don’t have to worry about increased storage costs.
- Data federation
This data integration technique involves creating a virtual database where the data is consolidated from disparate sources.
Users can use the virtual database as a single source of truth for all data in the organization. When users query the virtual database, the query is actually sent to relevant backend source servers, which then serve up the information accordingly.
So essentially, data is served on an on-demand basis, rather than using other techniques for data integration where the extra layers of data are integrated before anyone can retrieve any information from it. With this technique, you’re able to utilize familiar queries against a common repository across differing backend systems because all your data is now stored uniformly.
- Data consolidation
This technique combines data from disparate systems and creates centralized data.
Data consolidation is how data obtained from different sources is compiled and placed into a data store. This process builds a comprehensive database that can be retrieved later for analysis, distribution, or sharing of information.
Data consolidation makes the data formats consistent, allowing data workers to quickly improve data quality and integrity.
However, one challenge in data consolidation is latency. Data retrieval and transfer can be time-consuming.
Healthcare Data Integration Tools
You’ll need a good data integration tool to help support your efforts with any healthcare data integration approach. Choose a tool that can easily be integrated with applications you already have or one that allows you to create custom connectors if prebuilt ones don’t exist. Ideally, your data integration solution is also flexible enough to enable you to fight future changes.
Another vital feature to consider is an intuitive interface. Healthcare data can be very complex. Make sure that your healthcare data integration tools are easy to learn and use, so your team can get up and running quickly.
We know the challenges you face when connecting various systems for automating workflow processes within your company. Which is why we’ve created a service that enables people to connect all of their disparate platforms by using API integrations.
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