Reducing Data Latency from 45 minutes to Near-Real-Time to Save Lives and Enhance Decision-Making
For 30 years, health systems have relied on TeleTracking’s operational platform to manage bed capacity, track patient movement through various access points, as well as identify progress into post-acute care through discharge. TeleTracking strives to provide visibility and transparency to improve the flow of operations for healthcare providers. Their platforms provide system-wide situational awareness and operational foresight to reduce complexity, increase efficiency, improve productivity, and decrease costs.
The Challenge
As constructed, the legacy TeleTracking platform was unable to show customers an accurate view of their bed capacity and patient movement, causing decision-making to be difficult. For a collection of key data points, TeleTracking needed to reduce data latency from 45 minutes to near real-time. A new platform had been built, but it did not have feature parity to the legacy TeleTracking platform. TeleTracking needed to ensure data source availability for both their standard legacy platform, as well as their new data platform.
The Goals
To maximize the provision of patient flow data for decision making, TeleTracking customers required the system to provide data more quickly. To meet these needs, TeleTracking needed to increase the feature set of the new platform. TeleTracking also required the new data platform to live in numerous regions throughout the world to be close to their customers.
The Solution
CEI was able to contribute vast AWS architecture and engineering expertise to design, plan, and execute a solution that would meet the needs of TeleTracking’s customers. CEI’s data engineering team successfully created data warehouse processes to accelerate the rate at which hospital data is collected and updated to the point that it can be delivered to the customer in near-real-time.
Throughout the engagement, CEI provided mentoring, knowledge transfer, and shared execution with TeleTracking team members. In addition, the CEI team was able to make enhancements to both TeleTracking’s legacy platform, as well as their new, modernized TeleTracking data platform, which are summarized below:
Modernized TeleTracking Data Platform
(delivering data in near-real-time to end users)
- Create Custom High Performance SQL Queries adding specific datapoints
- Create streaming jobs, structs, and tables
- Update Snowpipe for added data points and Snowflake tables and views
- End-to-end testing
- Delivered configurations to all customers
Legacy TeleTracking Platform
(batch processing data that isn’t as time sensitive)
- Enhance reporting platform to provide new functionality through Snowflake in order to achieve parity with the modernized platform
- Create additional streaming services through Spark, Scala and other technologies
- Develop SQL custom Ingest, Structs, Tasks in DAG
- Perform end-to-end testing
- Create scripts to backfill historical tables to all tenants
Michael Coen
TeleTracking Chief Technology Officer
CEI brought a level of professionalism and technical skills to expand our team and help accomplish some very critical client needs for improving our data pipelines. Their engineers were knowledgeable, engaged, and willing to roll up their sleeves and dig in to help improve a very complex healthcare operations data platform. We are fortunate to have such a great partner and I highly recommend them for any technology projects, regardless of the complexity and challenges.
The Stack
AWS | NIFI | Airflow | Kafka | EMR | Spark | Scala | Python | Snowflake | Open-Source Delta Lake | S3 | SQL
The Results
An estimated 37,000 patients die in Emergency Rooms every year because they are waiting for a bed. Having near-real-time data available indicating how many and what type of beds are available allows hospitals using TeleTracking’s platform to provide better care to more patients with less patient wait time.
This near-real-time data is housed in Snowflake and can be integrated into Tableau, or other data analysis and visualization tools to allow customers to identify bottlenecks, operational inefficiencies, and opportunities for improvement.
In addition to enhancing the care that hospitals can provide their patients, CEI was able to help TeleTracking achieve the following:
45 Minutes to Near-Real-Time
Improved data lag from 45 minutes to 1-2 minutes from the time data is collected to the time when the end user can access and utilize it.
50% Cost Reduction
For the specific teams and areas where CEI was contributing, TeleTracking was able to reduce their costs 2x through algorithms designed to optimize performance, equipment utilization and forecasting.
75% Code Coverage
CEI reached 75% code coverage through effective unit tests.
More than 700 healthcare facilities
700+ healthcare facilities are now running and benefitting from the near-real-time data provided by TeleTracking’s Modernized Data Platform.
Improved Development Lifecycle
Through automation and local development, CEI enhanced TeleTracking’s development lifecycle. CEI leveraged tools such as Kafka, Spark, and Scala/Python code using Docker and IntelliJ, Pycharm, and CI/CD deployment through Jenkins, Git, Spinnaker, and Sonar.
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