Project dbignite: Interoperability for Patient Analytics

Interoperability policies set by Centers for Medicaid & Medicare Services (CMS) in the United States started to go into effect in 2021, changing how clinical and administrative information are exchanged between payers, providers and patients.

In its policies, CMS has adopted the standards of Health Level Seven (HL7) International, a not-for-profit, ANSI-accredited standards developing organization dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing and retrieval of electronic health information¹. The most important of these standards in terms of recent policies is the Fast Healthcare Interoperability Resources, commonly known as FHIR (pronounced "fire"). FHIR is a relatively new specification, built on resources spanning domains such as clinical, administrative, and financial. A FHIR bundle, or collection of resources, can represent a longitudinal patient record.

As CMS goes, commercial health plans follow. Moreover, FHIR is a global standard that many countries and healthcare organizations have adopted. The end result is that there is more data than ever being shared across healthcare organizations. As an industry, we are closer than ever to that 360 degree of the patient. And yet we remain far away from translating these data into actionable analytics. Why is that?

While FHIR solves the challenge of having a standard format to exchange healthcare information, it was not designed for analytics.

The following challenges exist in working with FHIR bundles:

1. Converting FHIR (often serialized in JSON format) to tables for longitudinal analytics
2. Supporting streaming, real-time data to reflect the dynamic nature of patient health
3. Combining FHIR with unstructured data, or other structured data models, outside of those bundles in a common data model
4. Connecting data to advanced analytics and machine learning tooling

Project dbignite from Databricks is an open-source toolkit built to address these challenges, effectively translating transactional bundles into patient analytics at scale with the Lakehouse.

This library is designed to easily extract resources from these bundles and store the resulting tables into a Databricks SQL database, which can be readily queried using simple SQL statements as seen in the image below.

¹ https://www.hl7.org/fhir/overview.html

Information from FHIR bundles can support use cases ranging from prior authorization automation to predictive care management.

Learn more about FHIR Interoperability with dbignite today!