StreamSets in Healthcare Services

Data is the lifeblood of the healthcare industry, but the world of data is exploding, offering new opportunities and challenges in the form of pervasive intelligence, which is the ability of myriad analytics groups to self-service ready-to-use healthcare data. Machine learning and artificial intelligence, applied by groups throughout an organization, can help in a variety of areas, from predicting sepsis rates to driving better accuracy in claims prediction to delivering precision medicine. The end result is better patient outcomes and more cost-effective service delivery.

In the healthcare industry, things are changing all the time. Bringing in all available data regardless of size, shape, or structure and landing it in a centralized data hub is the goal of many organizations, but how do they achieve that without soaking up resources that distract them from their core focus of healthcare innovation? Priorities for managing health care data include:

Leveraging new data sources:

Healthcare companies worldwide are tasked with digitizing the vast majority of medical records, leveraging instrumented medical devices for predictive capabilities, and optimizing healthcare delivery at facilities. New standards like HL7 provide a framework for the exchange, integration, sharing, and retrieval of electronic health information, but there are variations in how different organizations implement it.

Establishing Patient 360:

Healthcare companies want to have a full 360-degree picture of a patient’s health, but often, that information spans systems and is siloed in separate locations.

Enabling a unified data lake:

To overcome data silos, as well as issues of growing data volume and variety, a centralized enterprise data lake provides many benefits. However, ingesting data into the lake usually requires custom coding and specialized skills, translating to high costs and lengthy projects that delay getting data to data scientists and analysts.

Data ingestion into the cloud:

As many companies in this industry step away from legacy on-prem systems (including mainframes) to explore new use cases and different ways to leverage their data, migrating to the cloud becomes an integral part of their data strategy. However, it often becomes a challenge due to limited IT resources: teams are unsure of what will be compatible with the cloud, and even though there are many tools available, they have limited knowledge when it comes to understanding how to build and debug data pipelines. This is also a challenge when organizations go through mergers and acquisitions.

Complex data transformation (ETL):

Many healthcare organizations are implementing solutions that will increase their ability to apply real-time analytics and expand data access with self-service. To achieve these outcomes, they must first extract, transform, and load their data into a destination where it can be analyzed. Organizations with a fully on-prem environment usually have limited resources to handle this process.

Cybersecurity:

As companies move to modern data platforms, the inspection, detection and disposition of personal health information (PHI) is a challenge, especially when data is in motion. HIPAA requirements are increasingly unforgiving and often evolve at a pace that is difficult for companies to adapt in practice. This involves protecting data at origin, in-flight, and at its destination.

StreamSets helps healthcare companies accelerate drug research and development, improve patient outcomes, and make new data sources like bedside equipment, patient records and radiology images available to analytics teams and applications while ensuring data is protected in motion.

The StreamSets Platform helps companies design and run batch and streaming pipelines in a fraction of the time using a drag-and-drop environment that minimizes coding and facilitates collaboration. It also detects and handles data drift, which may manifest as added fields or changed data types that can occur without notice when data sources StreamSets in Health Care Services are upgraded. Hundreds of complex data flow topologies can be managed with StreamSets, giving you end-to-end visibility into your data movement. In addition, users can create data processing pipelines that execute on any Apache Spark environment or on Snowflake. Using a simple to use drag and drop UI, pipelines for performing ETL, stream processing, and machine learning operations can be created with ease.

StreamSets enables healthcare organizations to:

• Develop a DataOps practice and manage the health, delivery, and security of critical pipelines feeding the organization’s analysis and discovery.
• Gain quicker, governed access to data generated across multiple sources.
• Achieve compliance with industry regulations and data security, governance, and integrity guidelines.
• Quickly analyze and act on insights generated from large data volumes spread across disparate sources.

While the problems that healthcare companies face are indeed complex in nature and transformative in scope, the business impact and strategic advantages are paying off for those companies that decide to leverage data in a way that addresses future compliance requirements.

Companies like Availity use StreamSets to ingest data into a vast repository which helps lower cost and increase data discovery. Pharma companies like GlaxoSmithKline use StreamSets to accelerate the drug development process from 10 years down to 2.

StreamSets is helping healthcare companies leverage the data they need to realize business value from a growing list of strategic use cases. By providing a platform that intelligently handles the eminent evolution of data systems and practices, monitors and reports on data pipeline operations, and removes barriers from leveraging complex data types, healthcare companies can build groundbreaking functionality that impacts the health and well-being of our society.