![]() The FHIR standard incorporates descriptions of data elements as first-class members and presentation of this context alongside the data itself promotes a richer understanding. We focus on native hierarchical schema that tends to be much more complex, with many nesting levels and optional structures.Īlthough Amazon Redshift PartiQL is an enabling technology to query and explore, analysts and scientists also require an understanding of the underlying structure they are interacting with. Our approach is different from using nested types to simplify and optimize query processing. This technology simplifies how data scientists can use SQL to directly query FHIR resources. In August, 2019, AWS announced PartiQL, an open source SQL-compatible query language that makes it easy to efficiently query data regardless of where or in what format it is stored. This approach, however, presents separate challenges because the existing analytics tools data scientists are most comfortable and productive with aren’t often well suited to interrogate deeply hierarchical data structures. ![]() Alternatively, you can leave the FHIR resources in their hierarchical form and eliminate the need to invest and support a complex ETL pipeline. In addition, consumption of the data from such a relational model is time-consuming and expensive. However, such an exercise delivers a subpar final model that results in hundreds of tables with thousands of columns that aren’t naturally extensible the same way FHIR is designed. One such way is to flatten and normalize the nested JSON FHIR documents so that it’s usable in traditional relational schema. There are multiple ways to organize and query healthcare data on AWS. Although this post focuses on a simple analysis of claims data, this approach can help data scientists and data analysts reduce the manual work and long cycles of data processing when analyzing patient data by querying and running statistical analysis that is required day to day. In this post, we walk through how to use JSON Schema Induction with Amazon Redshift PartiQL to simplify how you analyze your FHIR data in its native JSON format. In addition, data scientists need to consume FHIR format from multiple sources and connect them with each other and existing relational data that resides in existing databases. The majority of existing analytics infrastructure relies on this “flat” storage and presentation of data assets this can be challenging given FHIR’s heavily nested JSON structure. However, existing tooling for data visualization, statistical analysis, and machine learning often relies on relational schemas that can be easily transformed into vectorized inputs. By bringing analytics to their own health and operational data, leading healthcare organizations are now improving care quality, patient experience, and cost. In addition to exchanging information with other entities, healthcare organizations are recognizing the intrinsic value of their own health data flowing within their systems. FHIR is quickly becoming the standard for information exchange in the healthcare industry for example, the United States’ Centers for Medicare & Medicaid Services (CMS) recently announced the Interoperability and Patient Access final rule (CMS-9115-F), which adopts FHIR as the standard for exchanging health data. FHIR is built around resources that logically organize healthcare information in a structured but fully extensible format. More recently, HL7 introduced FHIR (Fast Healthcare Interoperability Resources) to help solve some of the complexity and pave the way for healthcare organizations to modernize how they exchange information. Naturally, these shortcomings can complicate interoperability. HL7v2, a pipe-delimited data format developed three decades ago, is still in use today despite not conforming to modern best practices for communicating between systems, such as with RESTful APIs. The healthcare industry has adopted data exchange standards, many of which are defined by Health Level Seven International (HL7), for several decades. Each of these organizations needs to exchange health data efficiently with the others to ensure care continuity and reimbursement. In a patient’s care journey, multiple organizations are often involved, including the healthcare provider, diagnostic labs, pharmacies, and health insurance payors. Healthcare organizations across the globe strive to provide the best possible patient care at the lowest cost.
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