Healthcare has recently been transformed by two remarkable innovations: Medical Interoperability and machine learning (ML). Medical Interoperability refers to the ability to share healthcare information across multiple systems. To take advantage of these transformations, we launched a new HIPAA-eligible healthcare service, Amazon HealthLake, now in preview at re:Invent 2020. In the re:Invent announcement, we talk about how HealthLake enables organizations to structure, tag, index, query, and apply ML to analyze health data at scale. In a series of posts, starting with this one, we show you how to use HealthLake to derive insights or ask new questions of your health data using advanced analytics.
The primary source of healthcare data are patient electronic health records (EHR). Health Level Seven International (HL7), a non-profit standards development organization, announced a standard for exchanging structured medical data called the Fast Healthcare Interoperability Resources (FHIR). FHIR is widely supported by healthcare software vendors and was supported at an American Medical Informatics Association meeting by EHR vendors. The FHIR specification makes structured medical data easily accessible to clinical researchers and informaticians, and also makes it easy for ML tools to process this data and extract valuable information from it. For example, FHIR provides a resource to capture documents, such as doctor’s notes or lab report summaries. However, this data needs to be extracted and transformed before it can be searched and analyzed.
As the FHIR-formatted medical data is ingested, HealthLake uses natural language processing trained to understand medical terminology to enrich unstructured data with standardized labels (such as for medications, conditions, diagnoses, and procedures), so all this information can be normalized and easily searched. One example is parsing clinical narratives in the FHIR DocumentReference resource to extract, tag, and structure the medical entities, including ICD-10-CM codes. This transformed data is then added to the patient’s record, providing a complete view of all of the patient’s attributes (such as medications, tests, procedures, and diagnoses) that is optimized for search and applying advanced analytics. In this post, we walk you through the process of creating a population health dashboard on this enriched data, using AWS Glue, Amazon Athena, and Amazon QuickSight.
Building a population health dashboard
After HealthLake extracts and tags the FHIR-formatted data, you can use advanced analytics and ML with your now normalized data to make sense of it all. Next, we walk through using QuickSight to build a population health dashboard to quickly analyze data from HealthLake. The following diagram illustrates the
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