{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/3de54014865b4cb7aa59b87ec940626d\" frameborder=\"0\" width=\"1670\" height=\"1252\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1252,"width":1670,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1252,"thumbnail_width":1670,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/3de54014865b4cb7aa59b87ec940626d-a4aca5b9f41f4f9e.gif","duration":656.1938,"title":"Overview of HL7 Data Ingestion Pipeline Project","description":"In this video, I provide an overview of my HL7 data ingestion project, which currently features a basic pipeline for processing HL7 messages, with plans to extend it to CCDA and X12. The pipeline includes a producer generating synthetic data, a consumer for validation, and a transformer converting HL7 to FHIR using Microsoft's Fire Converter. I've integrated Grafana and Prometheus for metrics collection and alerting, and Kafka serves as the message broker. I encourage viewers to explore the code and provide feedback on the validation and QA processes, as well as any potential improvements. Your insights will be invaluable as I continue to develop this project."}