Discover the transformative power of FHIR analytics in healthcare and gain a comprehensive understanding of how it’s reshaping the analytics landscape. You will also learn about the specific challenges that come with FHIR use for analytics and explore how we’ve overcome these challenges in our in-depth case study of a FHIR-based analytics platform.
Analytics are essential for uncovering patterns and gaining insights from massive volumes of raw data, such as patient histories or data from health apps.
For example, with personalized AI, doctors and health companions can use predictive analytics to tailor treatment recommendations based on a patient’s medical history and real-time health data. In public health, predictive models can analyze vast datasets to forecast global health crises, allowing for early intervention.
Similarly, detecting unusual biomarkers in lab results can indicate potential diseases before symptoms appear, while advanced statistical methods in clinical trials help evaluate new treatments by identifying significant trends in patient outcomes.
From a data analysis perspective, prediction and evaluation rely on large datasets, where statistical methods are used to derive summarized insights. These methods include summation, calculating averages and deviations, and identifying maximum and minimum values.
In healthcare organizations, such data serves a dual purpose: primary care delivery and secondary uses like research, quality improvement, and FHIR data analytics. The secondary usage has recently become as important as the primary, paving the way for a health data transformation with FHIR. However, there will always be challenges in healthcare data analytics, including data interoperability issues, privacy concerns, and the need for advanced analytical tools to process the vast and complex data generated in the healthcare ecosystem.
These challenges must be addressed effectively to fully harness the potential of FHIR and OMOP healthcare data standards for improving patient care and advancing medical research.
In this article, we share our approach to addressing technical challenges and overcoming the limitations of FHIR in data analytics that allow us to get the most out of healthcare data.
The Rising Tide of FHIR Adoption
FHIR has positioned itself as the go-to standard for healthcare data interoperability. It facilitates standardization and data exchange, which significantly benefits healthcare analytics with FHIR.
In the United States, compliance with the 21st Century Cures Act requires that EHR systems include patient-facing apps based on FHIR standards. Starting in 2023, the Centers for Medicare and Medicaid Services (CMS) will mandate that healthcare payers integrate diverse FHIR-based applications.
Meanwhile, the European Union’s InteropEHRate initiative adopts FHIR protocols for personal health records and facilitates data exchange between providers. Additionally, New Zealand’s Ministry of Health and the United Kingdom’s National Health Service have incorporated FHIR standards in their national patient identifier systems.
Benefits of Healthcare Analytics on FHIR
The integration of a healthcare data analytics solution with FHIR standards marks a pivotal advancement in medical technology, offering unprecedented opportunities for enhancing patient care through more effective data utilization and analysis.
Healthcare data analytics using FHIR offers several significant advantages in business and technology:
Improved Data Interoperability
FHIR’s standardized data formats and APIs facilitate an easier exchange of healthcare information. This streamlined data interoperability allows organizations to analyze diverse data sets cohesively.
Comprehensive View of Patient Data
FHIR’s rich and extensible data model can capture a wide range of clinical concepts, providing a more complete picture of patient care. This enables more nuanced analytics that can drive personalized treatment plans.
Lower Costs
Standardization reduces the need for custom integration work, thereby lowering operational costs. Businesses can focus more on deriving actionable insights than struggling with data compatibility issues.
Regulatory Compliance
FHIR often aligns well with healthcare regulations requiring standardized electronic data sharing, such as HIPAA in the U.S., thus simplifying compliance efforts.
Innovation and Ecosystem Growth
The adoption of FHIR standards can stimulate innovation by enabling third-party developers to build applications that can easily integrate with existing systems, offering new avenues for analytics.
The FHIR Analytics Challenge
FHIR (Fast Healthcare Interoperability Resources) has become one of the most popular data exchange standards, and it has made it into many healthcare data management regulatory requirements around the world. In our Ultimate Guide To FHIR, you can find everything you need to know about this standard, from the background of creating FHIR to a detailed description of its technical aspects.
The only guide to FHIR you need
to unlock the full potential of healthcare data interoperability
However, despite its advantages for interoperability, FHIR’s structure was not originally designed for analytical queries. Its complex format, particularly its use of nested JSON objects, presents challenges when transforming data into a tabular structure suitable for analytics. As a result, organizations looking to use FHIR for analytics must overcome performance and structural limitations to extract meaningful insights efficiently.
“There is a growing interest in using FHIR for analytic purposes. However, to use FHIR effectively, analysts require a thorough understanding of the specification, including its conventions, semantics, and data types.”
Source: build.fhir.org
Why Consider FHIR for Analytics?
Despite its limitations, FHIR remains a strong candidate for analytics due to its structured validation features and interoperability. Since data exchange is the backbone of analytics, ensuring that exchanged data is accurate, validated, and structured correctly is essential.
Learn to validate, process, and store healthcare data effortlessly with the Kodjin FHIR Server
FHIR is known for its advanced validation capacity or, more precisely, for the availability of tools that leverage validation techniques to ensure only high-quality, consistent data enters the analytics pipeline. This prevents errors from propagating into analytical models, thus improving data reliability.
As we’ve discussed in our posts on CQL on FHIR and OMOP on FHIR, one of the biggest questions isn’t which standard to choose but rather how to combine different standards and technologies to address the specific needs of each use case and achieve the best possible results.
When combined with complementary tools that optimize data transformation and querying, FHIR can provide a solid foundation for healthcare analytics.
Addressing FHIR Analytics Challenges with OLAP
Given FHIR’s limitations for analytical queries, leveraging Online Analytical Processing (OLAP) can help transform FHIR data into a more efficient, structured format for large-scale analysis.
What Is OLAP?
OLAP is a technology that enables multidimensional data processing, supporting complex calculations, reporting, and predictive “what-if” planning, such as budgeting and forecasting.
What does “multidimensional data processing” mean?
In OLAP, data is stored in a cube structure, which includes:
dimensions (categories like time, location, or product type for organizing data);
measures (numerical values such as sales revenue, units sold, or patient counts).
In healthcare data analytics, OLAP cubes enable the simultaneous analysis of multiple data dimensions, such as time, patient demographics, and treatment types, facilitating comprehensive insights required for efficient and accurate decision-making.
“OLAP technology has been defined as the ability to achieve “fast access to shared multidimensional information.”
Source: olap.com
3 Steps to Get Quality Data for Analytics with OLAP and FHIR
High-quality data is the foundation of accurate analytics, as unreliable or inconsistent data can lead to flawed insights and poor decision-making. Preparing a well-structured dataset for statistical sampling is one of the most critical steps in the analytics process.
Without proper validation mechanisms, such as FHIR validation, large volumes of data may be rejected due to inconsistencies, errors, or missing values, ultimately preventing them from being included in calculations. This reduces the available dataset and affects the reliability of predictive models, making it difficult to draw meaningful conclusions in areas like personalized medicine, public health forecasting, or clinical trial evaluations.
Ensuring high data quality through rigorous validation helps maintain the integrity of analytical outcomes and improves the overall effectiveness of data-driven decision-making.
1. FHIR Validation – Clean and Accurate Data
High-quality data is the foundation of accurate analytics, as unreliable or inconsistent data can lead to flawed insights and poor decision-making. Preparing a structured dataset through rigorous validation is essential to ensure reliability in analytics.
FHIR validation plays a critical role in this process by:
Preventing inconsistencies at the data ingestion stage.
Ensuring only structured and validated records enter analytical models.
Reducing data rejection rates while maintaining data integrity.
FHIR ensures only standardized and accurate data is stored, preventing inconsistencies from the start through:
Data Type Validation for Accuracy: Ensures only the correct data types (e.g., numbers, dates, text) are accepted.
Value Set Binding for Consistency: Links healthcare data elements to predefined sets of acceptable values.
Cardinality for Completeness: Defines how often an element must or may appear in FHIR messages.
Slicing for Specific Use Cases Validation: Allows elements to be split into multiple instances based on conditions.
FHIRPath Validation for Customized Approach: Uses the FHIRPath language to navigate resources, extract specific information, and apply custom validation rules for better accuracy.
Validation is crucial in ensuring the accuracy, consistency, and trustworthiness of data. For more insights and real-world use cases of FHIR profile validation, check out the Edenlab and HL7 Webinar on Data Quality in Healthcare.
2. Transforming FHIR Data for Analytics with SQL
Since FHIR data is stored in complex, nested formats, converting it into SQL-friendly structures is crucial for performing efficient queries and generating meaningful reports.
To address this, the SQL on FHIR specification describes a standardized way to convert FHIR data into tabular formats optimized for SQL-based analysis.
The central component of the SQL on FHIR specification is the ViewDefinition, which uses FHIRPath expressions to define tabular projections of FHIR resources, specifying columns and inclusion criteria while functioning as part of a larger system that encompasses three distinct layers:
The Data Layer;
The View Layer;
The Analytics Layer.
The Data Layer
The Data Layer consists of lossless representations that enable FHIR compatibility with various query technologies and can optionally be persisted and annotated to enhance the efficiency of the View Layer without requiring a specific structure.
The View Layer
The View Layer implies View Definitions to convert FHIR resources into flattened, tabular formats for easier analysis. These definitions, based on FHIRPath expressions, create reusable, standardized views, simplifying data preparation.
View Runners execute these definitions by transforming the Data Layer into outputs like database tables or files. They come in two types:
In-memory runners process data on the fly, ideal for ETL workflows (e.g., converting FHIR NDJSON to Parquet files).
In-database runners translate definitions into SQL queries over FHIR-native databases, offering higher efficiency but greater complexity.
3. ClickHouse for OLAP Processing
Designed for online analytical processing (OLAP), ClickHouse excels in handling large datasets by leveraging its columnar storage architecture. This structure enhances data compression, speeds up query execution, and facilitates real-time analytics while maximizing hardware usage.
For example, when building an FHIR semantic layer analysis platform for our partners at Zoadigm, Edenlab’s team optimized the data storage with ClickHouse, thus speeding up queries and making extracting valuable insights from Zoadigm’s dataset quicker.
Key Features of ClickHouse
ClickHouse is designed to efficiently handle analytical tasks through the following features:
Columnar Storage: Data is organized by columns rather than rows, which reduces storage size and speeds up operations like filtering and aggregations.
Distributed Architecture: Data can be split across multiple nodes, enabling parallel processing for large datasets and allowing the system to scale horizontally.
Vectorized Query Execution: Queries are processed in batches, handling multiple rows at once to improve execution speed.
Specialized Table Engines: Table engines like MergeTree handle high-volume data storage, while SummingMergeTree simplifies calculations by pre-aggregating numerical data.
Real-Time Analytics: Supports continuous data input and immediate querying, which is helpful for time-sensitive applications.
Materialized Views: Stores precomputed query results during data insertion, reducing the complexity and time needed for repetitive queries.
Healthcare organizations can benefit from ClickHouse’s scalability and performance, especially when analyzing large-scale FHIR data. Its distributed architecture and support for advanced query optimization ensure seamless data processing across multiple nodes.
“ClickHouse helps us efficiently and reliably analyze logs across trillions of internet requests to identify malicious traffic and provide customers with rich analytics.”
Source: Cloudflare
How Edenlab Tackled the Challenges of Building a FHIR Analytics Solution
While the challenges of FHIR analytics hinder the development of comprehensive solutions that support data-driven decision-making in healthcare, they are not an insurmountable peak.
Our team at Edenlab was tasked by our client, Zoadigm, to create an FHIR-based analytics solution to solve a pressing issue in healthcare data: unifying disparate and siloed data sources to uncover hidden patterns within clinical data.
The project’s goal was to empower clinicians, payers, and patients by offering actionable intelligence and insights into patient cohorts, treatment pathways, and payment models.
A project of such complexity comes with its own challenges, some of which are tied to the FHIR data model. Below, we will explore how our team approached and overcame these challenges and built a multi-dimensional FHIR data analytics platform.
Main Challenges
FHIR is known for its interoperability benefits and complex, nested data structures, which makes its implementation within typical relational data models difficult. Other challenges include incomplete or inaccurate data, the necessity for real-time interactivity, and the need for an intuitive user interface. To address these complexities, a comprehensive FHIR implementation guide is essential for the successful adoption and integration of FHIR standards in healthcare systems.
Platform Architecture
Built on a scalable, cloud-agnostic microservices architecture, the platform is powered by Kubernetes and utilizes the Kodjin FHIR Server for efficient data storage. By incorporating information asset graph technology and semantic layer technology, the platform offers a robust analytics solution. It unifies data from multiple EHRs, allowing for advanced, user-defined analyses and data visualizations.
Our Approach
Nested Data Complexity: The complex and nested nature of FHIR data makes query building challenging. Our team tackled this using ClickHouse, a powerful column-oriented DBMS, to create custom schemas and queries. This optimized the data storage and query speed, making the complex FHIR data more accessible and easier to analyze.
Speed and Scalability: Using the Kodjin FHIR Server and its microservices architecture ensures fast, asynchronous data handling. This architecture efficiently addresses slow query execution due to large datasets inherent in healthcare.
Data Integrity and Quality: Our team created pipelines for data extraction, loading, transformation, and quality that utilize new information asset graph technology. The Kodjin FHIR Server also validates resources upon upload to maintain data integrity.
Multidimensional Data Navigation: Navigating through multidimensional healthcare data for actionable insights is not straightforward and can be overwhelming for end-users. We implemented a semantic layer API to enable users to explore data through a ubiquitous language. Core components like the Cohort & Pathway Builder and Explorer were introduced to facilitate easy and interactive data analysis.
User Experience and Real-Time Interactivity: We created dynamic dashboards (liveboards) designed with clinicians and other end-users in mind. These liveboards allow users to explore patient cohorts and treatment pathways interactively, drilling into the data to reveal hidden patterns without requiring SQL or advanced technical skills. The interface was designed to be visually appealing and intuitive, minimizing the learning curve while maximizing real-time, actionable insights.
The Results
By addressing these challenges head-on, we’ve built a comprehensive solution that unifies healthcare data, streamlines data analytics, and ensures regulatory compliance, delivering actionable insights for improved patient care and operational efficiency.
Read an in-depth case study on how we built this FHIR semantic layer analysis platform here.
The Final Thought
While FHIR alone may not be the optimal choice for analytics due to its complex structure, integrating it with complementary tools like OLAP, SQL transformation, and ClickHouse bridges the gap between data interoperability and analytical efficiency.
At Edenlab, our experts specialize in leveraging FHIR alongside advanced analytics solutions to meet the unique requirements of each healthcare project. Contact us today to explore tailored FHIR analytics solutions that drive impactful data insights.
