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The Challenges

In an effort to provide higher quality medical care, our client needed to improve the rate at which they successfully identified follow-up appointments, tests, or procedures that were needed for patients.  

The Solution

CEI’s client created an input application, which provides an interface through which HL7 (Health Level Seven) data can be extracted from Epic EHRs (Electronic Health Records). The HL7 data is then converted into a proprietary data format and processed by the client’s Natural Language Processing (NLP) application.

The NLP application parses the doctor’s notes, searching for specific language or phrases that indicate that follow up medical work or testing is needed.

CEI partnered with the client to create an application that receives the data into the FHIR (Fast Healthcare Interoperability Resources) format, where it is stored in a database for later retrieval. To manage this, we utilized Lambda functions and the Kafka service to handle batch processing.

A separate client application then calls the REST API to retrieve any new data. These applications run on EC2 servers within an auto-scaling group. They are front-ended with AWS’s WAF, Route53 and network load balancer to handle routing. The applications use AWS’s RDS PostgreSQL database for storage. If new data is found, it is converted back into HL7 format and sent to Epic. At this point, healthcare providers are able to retrieve that data from Epic and identify patients that need follow up appointments, tests, or other medical work to be completed.

The Result

After implementing this solution, CEI’s client has seen a 50% increase in follow-up cases that were identified through this process that were not previously identified manually. As a result, healthcare providers are equipped with the knowledge and technology to deliver higher quality care and better health outcomes for patients.

Follow Up Cases

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Leading to Quality medical care

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