Why Interoperability Matters: Connecting Digital Twins with EHR and Medical Devices

The digital twins technology is all set to further advance the AI-powered healthcare system, but its success largely depends on one crucial factor: interoperability.

Even though it is an advanced technology that leverages the power of AI, it is only as smart as the data it receives. Patient information scattered across incompatible Electronic Health Record (EHR) systems, isolated medical devices, or siloed clinical applications poses a big challenge for the successful implementation of an efficient digital twins in healthcare system, as it fails to fully reflect the real-time health status of the patient.

Interoperability in digital twins is the mechanism that enables the seamless exchange and interpretation of data across systems, ensuring the digital twins have a complete, accurate, and up-to-date picture of the patient or healthcare environment they’re simulating.

Why Interoperability Is Important for Digital Twins in Healthcare

Interoperability is the processing of actionable information by digital twins by means of seamless and continuous exchange of information between different healthcare systems like EHRs, medical devices, imaging systems, and health apps.

In practice, this means:

• A heart monitor can send real-time vitals to the patient’s digital twin
• An EHR instantly updates the twin’s medical history
• A connected insulin pump shares dosing data automatically
• Wearables, clinical software, and diagnostic tools all speak the same “data language”

For digital twins, this creates a continuous data loop essential for accurate predictions.

Why Interoperability Is Critical for Digital Twins

1. Real-time Data Synchronicity

Live data is critical to digital twins in healthcare, and interoperability ensures data from devices and EHRs arrive promptly and in compatible formats, which enables:

• Instant updates from medical devices
• Real-time physiological monitoring
• Minute-by-minute synchronization of patient status

This guarantees the digital twin behaves like a “living mirror” of the physical patient.

2. Breaking down Data Silos

Healthcare systems are historically fragmented, as different hospitals use different EHR vendors, diagnostic labs use proprietary tools, and medical devices use unique protocols. This fragmentation creates data silos in healthcare, which limit the ability of digital twins to:

• Analyze holistic patient information
• Integrate environmental, behavioral, and clinical data
• Model complete disease progression pathways

Interoperability in digital twins breaks these walls and ensures unified data availability, enabling comprehensive, lifelike simulations.

3. Enabling Precision and Personalized Care

For a digital twin to provide meaningful predictions, it needs data from:

• Genetic profiles
• Medical history
• Medication records
• Imaging and labs
• Wearables and sensor outputs
• Lifestyle and environmental factors

Interoperability allows all these sources to converge into a single patient-specific model, enabling precision medicine such as:

• Personalized drug dosages
• Optimized treatment paths
• Accurate disease risk scoring

4. Enhancing Device-to-Twin Communication

Medical devices such as:

• Insulin pumps
• Continuous glucose monitors
• Ventilators
• Pacemakers
• ICU bedside monitors

…generate valuable real-time physiological data.

Interoperability in digital twins ensures that:

• Device data is standardized
• Data flows automatically into the digital twin
• Clinicians can simulate responses to device adjustments

This makes the digital twin a powerful tool for clinical decision support.

5. Supporting Predictive and Preventive Healthcare

Accurate predictions require rich, complete datasets.

With interoperable systems:

• AI models can detect risks earlier
• Disease progression can be simulated more accurately
• Prevention strategies can be personalized

For example, a digital twin of a heart failure patient can predict a potential exacerbation days before symptoms appear — but only if all the relevant data streams are connected seamlessly.

6. Improving Hospital Operations through System-Level Twins

Interoperability isn’t just for patient-level twins.

Hospital digital twins — virtual replicas of entire facilities — rely on interoperable systems to integrate data such as:

• Bed occupancy
• Emergency department flow
• Lab turnaround times
• Staffing schedules
• Equipment usage

With this data, hospitals can:

• Predict congestion
• Optimize workflows
• Reduce waiting times
• Allocate resources efficiently

Key Standards and Frameworks of Interoperability

1. HL7 and FHIR (Fast Healthcare Interoperability Resources): Health Level 7 (HL7) and FHIR are sets of international standards for the transfer of clinical and administrative data between different healthcare applications. It allows systems to exchange structured patient data quickly and consistently.
2. DICOM: Ensures imaging devices (MRI, CT, X-ray) can share data with digital twins for modeling organs and structures.
3. IEEE 11073: Standardizes communication between medical devices and clinical systems.
4. API-driven Integration: Modern EHRs like Epic and Cerner now provide APIs that digital twin platforms can use to extract structured data.
5. IoMT Integration (Internet of Medical Things): Cloud-connected devices use standard protocols to deliver continuous patient data streams.

Interoperability Challenges for Digital Twin Implementation

Even with seismic shifts in technology capabilities, including artificial intelligence, the idea of interoperability in healthcare has not been realized to its full potential even today. The challenges that stand as a roadblock to achieving this are:

• Data Standardization: Different healthcare organizations use a variety of formats and standards for data entry, organization, and storage, making it difficult for them to exchange information. Even with standards for interoperability such as HL7, FHIR (Fast Healthcare Interoperability Resources), and C-CDA (Consolidated Clinical Document Architecture), data exchange of electronic health records can be hindered by a lack of consistent adoption and implementation.

• Fragmented Health IT Ecosystem: The lack of a unified health IT ecosystem makes it difficult to achieve true interoperability between healthcare systems. A single healthcare entity may use several health IT systems, often supplied by multiple vendors, and each one comes with its own database, software, and architecture. An integrated SaaS platform can help resolve these EHR interoperability challenges.

• Data Quality: It’s not just the deluge of information that’s the problem but also the quality and the relevance of the data. Patient data comes from many different sources—imaging centers, labs, specialists, and hospitals—and can be cluttered with information the clinician doesn’t need. Having the wrong data can lead to costly errors and delays, not to mention frustration for time-pressed physicians and clinical staff. Steps should be taken to format and integrate data from multiple systems, deduplicate and remove irrelevant content, and place it into the patient record when and where it’s needed.

• Vendor Lock-In: Some systems restrict data sharing to maintain market dominance.

• Financial and Resource Barriers: The costs of upgrading or replacing a legacy system to meet today’s interoperability standards or paying for the necessary integrations to connect with third parties, plus the technology and staff to maintain those integrations, can prove to be a significant financial burden, especially for smaller institutions. Furthermore, some practices may have trouble justifying the return on investment for adopting interoperable systems. However, a cloud-based SaaS model reduces the high upfront costs associated with on-premise infrastructure by offering subscription-based pricing that’s more predictable and manageable.

• Security and Privacy: Healthcare data is highly sensitive, and compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) is not optional. It takes significant effort to ensure that patient data remains secure, as transferring data across multiple systems increases exposure. There are also cybersecurity risks to consider. For these reasons, it’s essential to partner with an EHR vendor that has the right experience to handle patient data security and privacy on your behalf. Consider a cloud-based SaaS solution with enterprise-grade security features, including encryption, access controls, and built-in HIPAA compliance measures.

As was mentioned earlier, digital twins is a technology that has the potential to revolutionize the healthcare sector with its many applications in simulation, monitoring, prediction, and optimization of operations not just in patient care but also in the management of an entire hospital ecosystem. Nevertheless, interoperability becomes an integral part of the effective implementation of it, and that comes from a successful and efficient integration of legacy systems with the new technology.

This is where experienced partners like DeepKnit AI come to your aid. With extensive knowledge of implementing AI solutions for healthcare institutions, DeepKnit AI has both experience and expertise in handling complex deployment and integration of AI tools with your legacy systems.