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The Challenge

TeleTracking’s goal was to reduce data latency from 45 minutes to near-real-time, ensuring healthcare providers had access to up-to-date information on bed capacity and patient movements. While a new platform was in development, it lacked parity with the legacy system, and TeleTracking needed to ensure the availability of both systems while increasing the feature set of the modern platform. Additionally, TeleTracking required global availability of the new platform to serve clients worldwide. 

The Solution

To address these challenges, CEI leveraged its extensive AWS architecture and engineering expertise. CEI’s data engineering team worked closely with TeleTracking to design, plan, and execute a solution that would meet the real-time data needs of TeleTracking’s customers. The solution enabled near-real-time data processing and delivery, drastically reducing latency while modernizing TeleTracking’s platform. 

CEI’s team provided knowledge transfer, mentoring, and hands-on execution, enhancing both the legacy and modernized data platforms with the following: 

Modernized TeleTracking Data Platform
(Near-Real-Time Data Delivery)

  • Created high-performance SQL queries to add critical data points. 
  • Developed streaming jobs, structs, and tables for real-time data processing. 
  • Updated Snowpipe and Snowflake tables/views to handle new data points. 
  • Conducted end-to-end testing to ensure data accuracy. 
  • Deployed configurations globally to customers. 

Legacy TeleTracking Platform
(Batch data processing)

  • Enhanced reporting functionality via Snowflake for feature parity with the modern platform. 
  • Built additional streaming services using Spark, Scala, and other technologies. 
  • Developed custom SQL ingest, structs, and tasks in DAG. 
  • Performed comprehensive end-to-end testing. 
  • Created scripts to backfill historical data for all clients. 

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:

Reduced Data Latency from 45 Minutes to Near-Real-Time

Data latency was reduced from 45 minutes to just 1-2 minutes, allowing healthcare providers to access critical bed and patient flow data in real-time. This improvement directly impacts patient care by enabling hospitals to reduce wait times and make timely decisions. 

50% Cost Reduction

TeleTracking saw a 50% reduction in costs in areas supported by CEI. This was achieved through optimized algorithms designed to improve performance, resource utilization, and forecasting. 

75% Code Coverage

CEI implemented effective unit tests, reaching 75% code coverage, ensuring high-quality, reliable code throughout the development process. 

Impact on more than 700 healthcare facilities

Over 700 healthcare facilities now benefit from the near-real-time data provided by TeleTracking’s modernized data platform, improving the quality and efficiency of care. 

Improved Development Lifecycle

CEI enhanced the development lifecycle by implementing automation, local development tools, and CI/CD pipelines using Docker, Jenkins, Git, Spinnaker, and Sonar. Tools like Kafka, Spark, Scala, and Python were integrated to streamline the process and improve code quality. 

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