Sponsored by Infor.
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Data management in life sciences is inherently complex. Data is fragmented across multiple systems — such as clinical trials, lab systems, regulatory databases, and electronic health records (EHRs) — in inconsistent formats. For example, patient information in the EHR is typically transferred using legacy technology. Additional data streams from wearable devices, facilities, specialists and insurance companies often come in through proprietary formats. Consolidating these disparate sources is essential to eliminate the data silos that are currently prolonging R&D processes, hindering innovation.
“When you have data in all these different formats, it takes a lot of time and effort to convert it to the annotated, structured format you need for analytics,” says Rob Brull, Senior Product Director for Infor’s Cloverleaf suite. “That time to value in life sciences is critical.”
The Fast Healthcare Interoperability Resources (FHIR) standard is gaining momentum as a vital tool in modern data integration strategies. While previous healthcare data formats like HL7 V2, CDA, and V3 have definitions of a format, they lack a consistent framework for data exchange. With FHIR, the industry gains both an API definition and a format definition, thereby supporting standardized data exchange across systems.
Infor’s solutions normalize diverse data formats to FHIR and transfer this rich dataset to a centralized FHIR repository — providing a holistic view of patient data across the population.
“If you only take the data that’s available in FHIR and try to do analytics or identify patterns off of it, you’re limiting yourself to a much smaller dataset,” explains Brull. “There is so much legacy data out there that needs to get into the mix.”
(Image: Infor.)
AI-driven tools, combined with Infor’s robust data integration platform and FHIR capabilities, are leveraging this comprehensive dataset to drive more effective decision-making. One key factor for developing AI models is having access to clean, annotated data for training and setting targets. FHIR aligns well with this requirement by providing a structured dataset.
FHIR also supports the massive volumes of data that AI needs for training and testing. “We can store up repositories of FHIR data over time and make it available for training,” says Brull. “Then, by linking to the customer’s medical record in the EHR, home health data, wearables, and any other patient or clinical trial data, FHIR-supported web APIs make this data available in real time.”
This real-time data is used to recognize patterns, make predictions and trigger alerts in production.
Infor’s partnership with NVIDIA demonstrates how AI integration through FHIR has improved decision-making in healthcare. NVIDIA’s processing power coupled with Infor’s FHIR-enabled data pipelines allow clinicians to utilize a special type of AI called retrieval-augmented generation (RAG). With this approach, clinicians can efficiently query AI models for specific insights related to patient histories, enhancing interpretability of AI recommendations.
Through FHIR and AI-driven data integration, healthcare organizations are overcoming the barriers of fragmented, siloed data and advancing interoperability across various systems. FHIR standards are not only optimizing data harmonization but also paving the way for smarter, more streamlined innovation in life sciences.
For further information please reach out to Alexis Moores with our Life Sciences Team at Alexis.moores@infor.com.
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