Summary
In this chapter, we discussed the fundamentals of FHIR and how to use it in the healthcare industry to solve challenges such as healthcare data interoperability. We also discussed Amazon HealthLake and its core features for storing, transforming, and analyzing health data. Amazon HealthLake’s NLP models interpret medical insights such as medical condition, medication, dosage, medical ontology linking, and more from health data, which can be further leveraged to create additional models with Amazon SageMaker or visualizations.
We then walked through the console and code to see how to create an Amazon HealthLake FHIR data store and how to input FHIR resources into our data store. We also discussed a sample architecture and implementation to ingest document-based health data into Amazon HealthLake to create a centralized, secure, scalable, HIPAA-eligible health data lake.
In the next chapter, we will extend the discussion to healthcare data interoperability. We will...
