Sharing and analyzing healthcare data is much easier using the relatively new Fast Healthcare Interoperability Resources (FHIR) format. As more providers adopt FHIR standards, analytics will provide greater insights into patient care and outcomes on a wider scale than ever before.

What is FHIR?

FHIR (Fast Healthcare Interoperability Resources) is a standard framework for exchanging healthcare data electronically. Unlike previous standards like HL7, FHIR data is easy for both humans and machines to understand and integrate into applications and analysis. This interoperability unlocks the tremendous potential for more robust and meaningful analytics using healthcare data.

What are the Benefits of FHIR for Healthcare?

1. Improved interoperability: 

FHIR enables seamless data exchange between different healthcare systems, regardless of the vendor. This eliminates the need for complex data conversions and integrations, streamlining healthcare workflows and improving patient care. 

2. Enhanced patient access to data: 

FHIR empowers patients to access their medical information easily and securely through various channels, including mobile apps and online portals. This transparency and accessibility can promote patient engagement and informed decision-making.

3. Increased data portability: 

FHIR allows patients to easily transfer their medical records between healthcare providers, facilitating continuity of care and ensuring that healthcare professionals have access to complete and accurate information.

4. Simplified data analysis and reporting: 

FHIR’s standardized data structure facilitates advanced data analysis and reporting, enabling healthcare organizations to glean valuable insights from patient data. This allows for improved decision-making and resource allocation using healthcare analytics software.

5. Reduced costs: 

By eliminating the need for custom data integrations and promoting efficient data exchange, FHIR can significantly reduce operational costs for healthcare organizations. This allows them to allocate resources more effectively and provide better quality care at lower costs.

How is FHIR Data Structured?

With the FHIR framework, data is structured into modular, reusable “resources” like patients, labs, medications, etc. This standardized format enables seamless sharing and analysis, including integration with non-healthcare systems. The open-source nature of FHIR also allows developers to build innovative models, applications, and analytics using FHIR healthcare data.

By overcoming long-standing barriers to data exchange, FHIR enables analytics at a scale and depth never before possible. Its potential to unlock insights to improve care quality and outcomes makes FHIR a real game-changer for healthcare.

Harness the Power Of Your Data With Embedded Analytics

A key benefit of FHIR is that data remains hosted in the cloud applications where it originates, rather than extracted and transferred to a separate data warehouse. FHIR allows SaaS providers to leverage embedded analytics within their cloud applications to derive insights instantly, without having to move data around.

Embedded analytics eliminate delays in waiting for data transfers to be completed before analysis can begin. This means faster and more efficient healthcare analytics to uncover trends and opportunities to optimize patient care.

For example, wearable patient monitoring devices can transmit near real-time FHIR data to cloud apps. Embedded analytics instantly processes this data to alert providers to concerning changes in vital signs or emerging health conditions. This allows for much quicker intervention compared to periodic analysis in a data warehouse model.

FHIR Enables Deeper Analysis for Improved Outcomes

By keeping data in the cloud and taking advantage of embedded analytics, FHIR allows for deeper and more broad-based analysis than legacy models dependent on data warehousing. Healthcare providers can perform analytics on wider datasets going back longer periods of time.

FHIR analytics that include genomic, socioeconomic, and environmental factors can uncover predictive trends and patterns difficult to discern with siloed data. These expanded insights enable providers to better understand influences on health outcomes and target preventative interventions more precisely.

Healthcare Analytics Use Case Examples Using FHIR Data

1. Patient Care Management:

Identify patients at risk 

Analyze patient data to identify individuals with a high risk of developing chronic diseases or experiencing adverse events. This information can be used to implement preventive measures and improve care coordination.

Improve medication adherence:

Monitor medication adherence by analyzing medication dispensing and administration data. This can help identify patients who are not taking their medications as prescribed and provide interventions to improve adherence.

Optimize treatment plans:

Analyze patient data to identify patterns and trends in treatment outcomes. This information can be used to personalize treatment plans and improve patient care outcomes.

healthcare analytics dashboard

2. Public Health Surveillance:

Track the spread of infectious diseases:

Analyze data on diagnoses and laboratory test results to monitor the spread of infectious diseases in real-time. This information can be used to implement public health interventions and contain outbreaks.

Identify vaccine hesitancy:

Analyze vaccination data to identify populations with low vaccination rates. This information can be used to target vaccination campaigns and improve immunization coverage.

Monitor the impact of public health interventions: 

Analyze data on health outcomes to evaluate the effectiveness of public health interventions. This information can be used to inform future public health policies and programs.

healthcare analytics dashboard

3. Clinical Research:

Conduct clinical trials: 

Use FHIR data to recruit patients for clinical trials and collect data on outcomes. This can help to accelerate the development of new drugs and therapies.

Identify potential biomarkers: 

Analyze patient data to identify potential biomarkers for disease diagnosis and prognosis. This information can lead to the development of new diagnostic and therapeutic tools.

Conduct observational studies: 

Use FHIR data to conduct large-scale observational studies to investigate the relationships between risk factors and disease outcomes.

healthcare analytics dashboard

4. Administrative and Financial Management:

Identify patients with high healthcare costs: 

Analyze data on healthcare utilization and costs to identify patients with high healthcare costs. This information can be used to develop targeted interventions to reduce costs.

Improve resource allocation: 

Analyze data on resource utilization to identify areas where resources are being underutilized or overutilized. This information can be used to improve resource allocation and optimize healthcare delivery.

Reduce fraud and abuse: 

Analyze data on claims and billing to identify potential instances of fraud and abuse. This information can be used to protect healthcare systems from financial losses.

healthcare analytics dashboard

5. Patient Engagement:

Provide patients with access to their medical data: 

Share patient data with patients through patient portals and mobile apps. This can empower patients to be more involved in their care and make informed decisions about their health.

Deliver personalized health information:

Use patient data to personalize health information and interventions. This can help patients to improve their health outcomes and reduce their risk of developing chronic diseases.

Develop patient-centered care programs: 

Use patient data to develop and implement patient-centered care programs that address the specific needs and preferences of individual patients.

healthcare analytics dashboard

Secure and Scalable: The Benefits of Keeping Data In The Cloud

The cloud offers the scalability, resilience, and security required to store, process, and analyze today’s vast and increasing healthcare datasets. FHIR is designed to maximize these benefits of the cloud by enabling analysis directly where the data resides.

Benefits of FHIR Analytics in the Cloud

Preventing unnecessary data transfers

This prevents the need for risky bulk patient data transfers from secure cloud platforms to other locations for analysis purposes.


It provides elastic scalability to grow storage and analysis capacity on demand. With FHIR, scaling to support larger datasets and faster analysis is as simple as provisioning more cloud resources.


The distributed nature of cloud infrastructure limits the impact potential outages could have on analytics.


Often healthcare SaaS companies host their SaaS platform within secure cloud environments such as AWS GovCloud for added levels of security, especially when processing private FHIR data.

One of the many reasons Qrvey chose to architect its solution with on-demand technology is that it’s the only way to achieve both scalability and security without massive budgets.  Oh and we do it as a self-hosted deployment, which makes it a great fit for healthcare SaaS.

The Potential of FHIR is Limitless

The capabilities unlocked by FHIR’s standardized, interoperable data exchange model are revolutionary. As more providers adopt FHIR across their systems, the volume and variety of data available for analysis will grow exponentially. In parallel, the best embedded analytics tools that integrate directly into healthcare apps will bring insights closer to real-time.

Together, these innovations will allow tapping into healthcare data like never before. The provider will be empowered to continuously analyze metrics from across the care continuum to uncover trends and correlations not previously possible. This will ultimately enable continuously optimizing the quality, efficiency, and outcomes of patient care through data-driven analytics.

How Qrvey Helps with FHIR Analytics

Qrvey is an embedded analytics solution for SaaS companies that excels with SaaS companies that take a security-first approach…exactly where healthcare and healthTech companies exist. Qrvey is white-label analytics software that SaaS companies embed within their SaaS applications serving as a healthcare solution on the AWS platform as Qrvey is a deployed solution only within AWS cloud environments.

Companies are increasingly aiming to offer an advanced analytics solution within their healthcare SaaS application – meaning a solution that allows individual users to create their own unique dashboards. This empowers end users with a comprehensive solution for use cases such as healthcare analytics software for patient data analysis using FHIR formatted data. Qrvey includes a built-in data lakehouse that makes it easy to prepare FHIR formatted data for analytics within multi-tenant platforms.

Curious? Let’s chat and we can show you how we work with healthcare-focused SaaS companies to take their analytics to the next level.

Get a demo of Qrvey

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