Home Data Science and GovernanceData Management The critical role of HL7 and FHIR in Pharmaceutical Innovation: A Case Study Approach

The critical role of HL7 and FHIR in Pharmaceutical Innovation: A Case Study Approach

by Massimo

HL7 (Health Level Seven International) and FHIR (Fast Healthcare Interoperability Resources) are two critical standards in the healthcare industry, primarily focusing on the exchange, integration, sharing, and retrieval of electronic health information.

In the rapidly evolving landscape of healthcare and pharmaceuticals, the importance of efficient and secure data exchange cannot be overstated. Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR) have emerged as pivotal standards in this domain, revolutionizing how healthcare data is shared and utilized. For pharmaceutical companies, these standards are not just technical guidelines but essential tools that drive innovation, enhance patient care, and streamline operations.

The importance of HL7 and FHIR

HL7 and FHIR standards play a crucial role in the pharmaceutical industry. They enable seamless communication between disparate healthcare systems, ensuring that critical patient data is accessible and interpretable across platforms.
This interoperability is vital for pharmaceutical companies in several ways:

  1. Enhanced Drug Development: Access to comprehensive and standardized patient data can significantly accelerate the drug development process. HL7 and FHIR facilitate this by ensuring data from clinical trials and research is easily shareable and analyzable.

  2. Improved Patient Outcomes: With standardized data exchange, pharmaceutical companies can more effectively track drug efficacy and patient outcomes, leading to improved medication and therapy plans.

  3. Regulatory Compliance: Adhering to HL7 and FHIR standards helps pharmaceutical companies comply with stringent data handling regulations, reducing legal risks and ensuring patient privacy.


Technical example

An example of a data standard in the form of a JSON object is posted below. This example could represent a simplified patient record in a healthcare system, following a standardized format that might be used in systems adhering to FHIR or similar data exchange standards.

This JSON object includes various fields such as patient identification, contact details, medical history, and recent visits, all organized in a structured and easily readable format. This standardization of data enables efficient sharing and processing across different healthcare systems and applications.

Patient Information

ID: 123456

Name: John Doe

Date of Birth: 1980-01-01

Gender: male

Phone: +123456789

Email: johndoe@example.com

Address: 123 Main St, Anytown, Anystate, 12345

Medical History

Allergies: Penicillin, Aspirin

Chronic Conditions: Diabetes

  • Metformin (500mg, twice a day)
Recent Visits
  • Date: 2023-03-15

    Reason: Routine check-up

    Physician: Dr. Smith

    Notes: General good health

  • Date: 2023-02-10

    Reason: Flu symptoms

    Physician: Dr. Jones

    Notes: Prescribed flu medication

Case Study: integration of FHIR in a pharma company

A real-world example of the impact of these standards can be seen in the case of I worked on in the past for a global R&D company that  integrated FHIR standards into its data management systems with the following results:

  1. Streamlined clinical trials: By adopting FHIR, were were able to integrate data from various clinical trial sites seamlessly. This integration led to a 30% reduction in the time required to collate and analyze clinical trial data, expediting the drug development process.
  2. Enhanced data security and compliance: FHIR’s robust framework ensured that patient data collected during trials was securely managed and compliant with global data privacy regulations, such as GDPR and HIPAA.
  3. Improved Collaboration with Healthcare Providers: FHIR-enabled systems allowed to share trial results and drug information efficiently with healthcare providers, ensuring that patients received the most up-to-date and effective treatments.

This case illustrates how the adoption of FHIR facilitated improvements in data management and analysis, directly impacting research and development (R&D) efficiency and clinical trial processes.

Implementation Phases

1. Assessment and Planning

– Gap Analysis: we conducted a thorough gap analysis to understand the discrepancies between their current data systems and the requirements of FHIR.
– Strategic Planning: we developed a strategic plan focusing on infrastructure upgrades, software development, and training needs to align with FHIR standards.

2. Infrastructure Overhaul

– Upgrading Data Repositories: we upgraded data storage and processing capabilities to handle FHIR-based data structures. I will not go too much in details of this step because it is quite depending on the infrastructure a Company might have already deployed.
– Network Security: Given the sensitivity of clinical trial data, our IT enhanced network security to comply with FHIR’s strict data security protocols.

3. Software Development

– FHIR-Compatible Tools: We  developed and integrated FHIR-compatible software tool from Cerner for data capture, storage, and analysis.
– API Integration: APIs conforming to FHIR standards were implemented to ensure seamless data exchange between internal systems and external entities like trial sites.

4. Data Migration and Validation

– Data Conversion: Existing data was converted into FHIR-compliant formats.
– Quality Assurance: Rigorous testing was conducted to ensure the integrity and accuracy of the migrated data. Quality Assurance (QA) is indeed a critical component in the data migration process, ensuring the integrity and accuracy of the data being transferred to the new FHIR-compliant system. In our Quality Assurance phase, we conducted rigorous testing to ensure the integrity and accuracy of the data migrated to the FHIR-compliant format as listed below. This thorough validation process was crucial in confirming that the data not only conformed to the new standards but also maintained its reliability, a key factor in supporting our subsequent R&D and clinical trial activities. 

  1. Data Validation Test (Ensuring the migrated data matches the original source data in terms of content and format)
  2. Schema Compliance Test (Verifying that the data structure adheres to the FHIR schema and standards)
  3. Integrity Test (Checking for data completeness and accuracy, ensuring no corruption occurred during migration)
  4. Functionality Test (Assessing if the data can be successfully retrieved, updated, and deleted in the new system)
  5. Performance Test (Evaluating the system’s response time and speed in handling FHIR-compliant data)
  6. Security Test (Ensuring that data is protected and complies with privacy regulations like HIPAA)
  7. Interoperability Test (Confirming that the migrated data can interact effectively with other FHIR-compliant systems)
  8. User Acceptance Test (Validating the system with end-users to ensure it meets their needs and expectations)

Practical Activities in R&D Analytics

  1. Data Standardization in R&D: we research data, including genomic, clinical, and drug efficacy data, into FHIR formats, facilitating easier aggregation and analysis.
  2. Advanced Analytics Implementation:  The use of FHIR standards allowed for the implementation of advanced analytics and machine learning models to predict drug responses and identify potential new drug targets. 

Clinical Trial Enhancements

  • Real-time Data Access:  The adoption of FHIR enabled real-time access to patient data from clinical trial sites, allowing immediate analysis and decision-making.
  • Enhanced Patient Recruitment: FHIR’s interoperability features allowed for more efficient patient recruitment by matching patient profiles with trial requirements across different systems.
  • Remote Monitoring and Reporting: the implementation of FHIR facilitated remote monitoring of trial participants and automated reporting of trial results, significantly reducing manual data entry and potential errors.
  • Regulatory Compliance:  clinical trial data handling was in compliance with global standards, facilitating smoother regulatory approval processes.
Read more here and here.

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