Streaming data and event-driven architectures are revolutionizing the way companies send data in and between systems. Streaming data in healthcare is no exception.
Imagine being a medical provider using streaming data to improve and predict patient outcomes instead of reacting to patient needs. Medical staffs are beginning to use streaming data to speed-up their immediate decision making while also using the information to enhance patient experience, spot trends and improve overall quality of life.
Distributed streaming platforms, like Apache Kafka, provide high-throughput, low latency, real-time messaging, data retention and scalability. In industries like healthcare, the ability to stream, monitor and update data in real time offers countless benefits.
For example, streaming software quickly communicates data such as lab results, warning signs for infectious diseases, and changes in patient conditions. The software also communicates prescription medications, medical records and documentation, provider information and procedure costs.
Streaming data not only improves healthcare delivery, but also helps healthcare providers comply with complex regulations.
Healthcare is one of the most highly regulated industries in the United States with an array of stringent laws. However, by monitoring patients in real time, providers improve patient outcomes. Streaming platforms can communicate changes to data in motion, which can help ensure compliance, decision making and improved patient care.
Using messaging platforms like Apache Kafka, streaming data in healthcare empowers providers to monitor their patients chronic conditions like diabetes, asthma, epilepsy, hypertension and lung and heart function to name a few. In real time, health providers can use the analytics to detect early signs of problems, while delivering personalized care.
Individual states also have rigid requirements regarding the accuracy of information in online portals that display provider and patient information. Health payers work with tens of thousands of providers, which means they need quick access to accurate and current provider information, like addresses, specialties, phone numbers, hours of operation and National Provider Identifier (NPI) number. Streaming platforms help update providers’ information instantaneously, avoiding potential hefty regulatory fines for displaying inaccurate information.
With the risks to confidential health information, data quality is fundamental to ensuring regulatory compliance and successful patient care. By implementing an integrated data intelligence platform, healthcare companies safeguard the integrity of their streaming data and provide data quality checks in real time.
A platform like Apache Kafka helps healthcare organizations set in-line data quality checks to ensure data conformity, consistency and completeness of patient and provider data, and verifies the accuracy of online healthcare portals. These checks happen continuously and any data that fails is rerouted for investigation, tracking and resolution before it corrupts systems.
Additionally, the right platform provides automated 3D lineage capabilities.
3D lineage gives healthcare providers complete transparency into the origins, route and transformations of data. It also provides an impact analysis to determine which assets are affected by changes and errors. Clarity into data’s journey across the healthcare enterprise empowers providers to quickly uncover and proactively resolve data quality issues.
The foundation to any event-driven architecture is data integrity, regardless of the velocity or veracity of data.
Apache Kafka and streaming data will continue to change the way healthcare companies collect, store, share, manage and leverage data.
Are you looking to solve data quality challenges for streaming data? Check out our white paper: Managing Data in Motion: Considerations in Data Quality for Streaming Data.
For additional information about streaming data quality, read our CEO, Early Stephens, Forbes article, Nine Tactics for Ensuring Streaming Data Quality.
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