Academic hospitals are at the heart of medical discovery. Every day, they generate vast quantities of data across electronic health records (EHRs), patient administration systems (PAS), registries, and diagnostic systems.
This data is invaluable for advancing clinical research, evaluating real-world outcomes and developing AI models.
However, bringing together and preparing clinical data often requires extensive manual manipulation across different structured and unstructured formats, making it time-consuming and resource-intensive to produce a consolidated and quality assured dataset. This exercise also requires the skills of data science, technical, and clinical research professionals.
Such a demanding and costly process limits the availability of timely and comprehensive clinical datasets and thereby constrains the potential for clinical research initiatives and organisational learning.
As the pace of medical innovation accelerates, the ability to perform timely and comprehensive data analysis is increasingly becoming a strategic imperative to assure clinical service delivery, attract research funding, form synergistic industrial partnerships, and advance the ability of a healthcare service to become a learning healthcare system.
OMOP: from Data Silos to Standardised Research Infrastructure
The Observational Medical Outcomes Partnership (OMOP) Common Data Model provides a unified framework for the synthesis of clinical information from across multiple healthcare services, care contexts, and clinical specialties to support the efficient and effective analysis of clinical for key insights such as cohort discovery, federated analyses, and multi-site research collaboration.
The adoption of OMOP has expanded steadily within the research community, particularly among organisations engaged in observational studies and real-world evidence generation. This growth reflects a broader movement toward interoperable, high-quality research data infrastructures that support reproducible and collaborative science.
Implementing OMOP at scale, however, remains a complex undertaking. It requires harmonising heterogeneous data sources, accurately mapping them to standardised terminologies, and ensuring continuous compliance with privacy and data protection regulations. For many healthcare institutions, these tasks have historically demanded substantial technical expertise, ongoing maintenance, and sustained investment in IT infrastructure.
Broad analytics platforms that treat OMOP as one feature among many capabilities often require significant customisation, integration effort, and operational oversight to deploy and maintain effectively.
InterSystems OMOP: Cloud-Native, Automated, and Flexible
The InterSystems OMOP solution makes it easier and faster for healthcare organisations to prepare data for research. As a cloud service, it automates the process of transforming and standardising data directly from hospital systems—removing the need for manual extracts or complex setup.
InterSystems provides ready-made FHIR-to-OMOP mappings, so organisations can quickly bring their data into the OMOP Common Data Model. These mappings can also be adjusted to match local data and research needs.
Researchers then have the flexibility to choose how they work with their OMOP data. They can either:
- Use the InterSystems OMOP database with OHDSI (pronounced “Odyssey”) tools—a set of open-source applications that make it easy to explore, analyse, and visualise OMOP data, or
- Export the OMOP data into their own Trusted Research Environment (TRE) for use within their existing infrastructure.
This approach allows healthcare organisations to connect research data directly to their day-to-day systems while keeping strong data governance and security in place.
The result is research-ready data that can be safely accessed, regularly updated, and used for a range of purposes from clinical trial recruitment and quality improvement to advanced research. InterSystems manages the underlying environment, reducing technical workload and freeing researchers to focus on generating insights that improve patient outcomes.
The Economics of Data
Historically, the economic model for clinical data has been largely cost-centred and focused on compliance, storage, and operational use rather than value generation. The transition toward data-driven research and precision medicine reframes this paradigm. When harmonised into standardised, research-ready formats such as OMOP, clinical data can support externally funded studies, industry partnerships, and translational research programs that generate both scientific and financial returns.
By reducing the technical and governance barriers to data access, platforms like InterSystems OMOP, make it economically feasible for academic hospitals to maintain continuously updated research datasets.
This shifts the balance from episodic, project-based data extraction to a sustainable model of data stewardship as an institutional asset—where the same governed infrastructure supports both clinical operations and research innovation.
Ultimately, the economics of clinical data in academic hospitals hinges on transforming data management from a compliance obligation into a strategic investment. Institutions that achieve this can unlock new revenue streams, accelerate translational research, and position themselves as key partners in the evolving data economy of healthcare.
Data Governance, Privacy, and Trust
Of course, the sensitive nature of healthcare data necessitates rigorous privacy and compliance measures, adding further complexity to its management.
Effective research data enablement therefore depends not only on technical capability but also on robust governance frameworks that preserve patient privacy and institutional trust.
Automated de-identification, comprehensive audit trails, and well-defined access controls are critical components of this framework.
Modern data architectures like InterSystems OMOP embed these safeguards within the platform itself, rather than treating them as downstream or optional processes. This approach ensures that privacy protection and data quality are integrated from the outset, rather than applied retrospectively.
By aligning with internationally recognised data models such as OMOP and interoperability standards like FHIR, academic hospitals can also participate more readily in federated research networks that uphold data sovereignty and minimise the need for data transfer.
From Raw Data to Research-Ready Knowledge
Academic medicine stands at a pivotal point. The volume and richness of available data have never been greater—but neither have the challenges of transforming that data into research-ready knowledge.
Standardised data models such as OMOP, supported by robust data platforms like InterSystems, offer a path forward:
- Faster data access for researchers
- Reduced operational burden for IT
- Built-in governance and compliance for institutions
- New opportunities for cross-sector collaboration and revenue diversification
In short, by investing in the infrastructure that connects operational data to research insight, academic hospitals can strengthen their role as engines of innovation, education, and evidence generation.
