Application developers and solution partners often need to process healthcare data outside of the electronic medical record (EMR) system. EMRs are primarily designed to directly support individual patient care, with a focus on clinical documentation, regulatory compliance, and billing. But when applications require intensive analytics, large-scale data aggregation, or real-time processing, EMRs can become bottlenecks. Many EMRs often limit their APIs to be read-only, restricting developers from storing genomic, medical device, and other advanced data types that make the foundation of modern healthcare innovation.
To overcome these limitations, organizations can turn to a clinical data repository (CDR) that operates independently of the EMR. A CDR can offer more a more flexible, high performance foundation for analytics, predictive modeling, population health management, and clinical decision support, which typically lie beyond the capabilities of an EMR.
Unlocking Data Across Systems
Healthcare data often comes from diverse sources: multiple EMRs, labs, imaging systems, and even wearable devices. Integrating this data into a single repository means healthcare applications can share a holistic view of patient information, enhancing clinical insights and improving care coordination. A centralized repository also supports compliance with healthcare standards like HL7 FHIR for data exchange across disparate systems.
By using a dedicated CDR, application developers gain the ability to process, share, and analyze data at scale. This architecture supports building data-driven healthcare solutions can also be powered by modern artificial intelligence (AI) and machine learning (ML) tools.
EMR and CDR: Complementary, Not Competitive
While the focus of the EMR is individual patients and their care and histories, a CDR can focus on the data itself. The creation of a separate repository allows the EMR and the CDR to efficiently serve their respective distinct purposes, complementing each other’s functions, without interfering with each other. By focusing on data, an independent clinical data repository enables datacentric applications—unlike the priorities of an EMR, which are real-time clinical documentation and support for immediate patient care.
A CDR can store, normalize, and manage data from multiple sources —such as lab results, imaging, and medical device data —into a unified format for secondary uses like analytics, research, and population health initiatives. For example, data from a CDR can be exported in to the Observational Medical Outcomes Partnership (OMOP) Common Data Model, a commonly used open community data standard for population-level research and analytics.
The separation of patient-centric and data-centric functions enables both healthcare organizations and solution providers more direct and immediate access to healthcare data without interfering with the EMR’s operational performance. They can support a range of data-driven applications, including real-time decision support, machine learning, and predictive analytics. Because data is stored in a centralized and accessible repository, they can accommodate complex queries and high-throughput data retrieval.
Real world impact: Reducing Hospital Readmissions
Consider a healthcare provider aiming to reduce 30-day hospital readmissions. To accomplish this, they set up a clinical data repository outside the EMR, integrating data from various sources, including lab results, medication histories, socioeconomic data, past hospitalizations, and patient-generated data like activity levels from wearable devices.
With this data in their custom CDR, data scientists can build and train predictive models using machine learning algorithms that analyze multiple variables affecting readmissions, such as comorbidities, patient demographics, and lifestyle factors. The AI model continually learns from new data, improving its accuracy over time. Clinicians proactively identify patients at risk, allowing for targeted follow-up care like regular check-ins, telehealth sessions, and home care services, significantly reducing the likelihood of readmission. By aggregating this data independently of the EMR, the organization can use advanced analytics and machine learning models to predict readmission risk, free of the processing limitations of the EMR.
InterSystems IRIS for Health: An Ideal Platform for a CDR
For application developers and solution partners serious about building a clinical data repository outside the constraints of an EMR, InterSystems IRIS for Health is an unparalleled choice. With InterSystems IRIS for Health, solution partners can leverage a flexible, scalable, and interoperable platform to drive innovative, data-driven applications that enhance patient care, streamline workflows, and open new possibilities in data aggregation and analysis, population health, and personalized medicine.