Integrating IoMT (Medical Devices) with Your RDM Platform
- Alain Lai
- Jun 16
- 5 min read
In today’s ever-evolving healthcare landscape, the Internet of Medical Things movement is an innovative approach to collecting and acting upon patient data. From connected inhalers to heart monitors, the Internet of Medical Things is a network of connected items that generates a vast amount of real-time data. These devices collect information such as blood pressure, glucose levels, and heart rate, which can be directly sent over to healthcare providers for real-time monitoring and analysis. With this wave of transforming medical tools into data-collecting assets, IoMT ushers in new patient care paradigms and empowers patients to be more active participants in the patient care journey.
This is a sub trend that emerged from the broader Internet of Things (IoT) revolution, which gained steam during the early 2000s. However, an effective IoMT is only possible in the presence of secure data management paradigms, and this is where the power of Research Data Management (RDM) platforms comes in.
A research data management (RDM) platform is a system that promotes consistent, reliable, and standardized data across different systems and inputs. By integrating IoMT with RDM platforms, patient data can be handled more securely and in a more timely fashion. Patients receive better care, there are less data silos, and there is stronger enforcement of security, structured analytics and compliance. With healthcare trending towards real-time, personalized care, the integration of IoMT with RDM is becoming more and more important.
Integration of IoMT with RDM
There are countless benefits to integrating IoMT with an RDM platform. Firstly, it ensures secure data handling and data accuracy by eliminating the need for manual handling or transfer of the data, which is critical as the correctness of these inputs has a direct impact on patient health and wellness. Secondly, this integration promotes consistency by creating a standardized way in which data is labelled, tracked, and used across different devices - especially when standard protocols are used for that data transmission such as FHIR (Fast Healthcare Interoperability Resources). For example, a DuPont heart rate monitor might classify heart rates as “H.R”, while an ISmarch heart rate monitor might classify that same data as “Heart”. Therefore, having an RDM platform to ensure that these inputs are understood as the same metric, allows for an accurate output.
This is a simplified example, but interoperability is a consistent hurdle that those in the healthcare IT sector face. Since medical devices often speak different digital languages, RDM provides an opportunity to apply a uniform method to integrate disparate data. Lastly, RDM supports scalability, empowering datasets and inputs to grow without fear of them becoming disconnected or contaminated. With so many data inputs from different sources, healthcare organizations must have a robust system to rely on that can maintain the integrity and usability of their data. Without this, these pieces of unstandardized data can quickly become disconnected, leading to inefficiencies, false positives, and errors. In this way, RDM platforms can support digital twins that rely on massive amounts of real-time data to simulate real-world systems and ensure adequate representation of these systems.
Example: ICU Smart Bed Integration
To gain a clearer picture of how IoMT devices can be integrated with a robust Research Data Management (RDM) system, examine the process of scanning thoracic CTs for early-onset pulmonary disease or cancer. Imaging equipment in a large number of contemporary hospitals is IoMT-enabled and sends scans immediately to centralized repositories. With an RDM system in place, this data can be safely standardized, labelled, and archived in real time, available for analysis by AI agents in a few seconds.
Without a solid RDM foundation in place, these images may be stored in incompatible formats or may be missing key metadata (like timestamp, device type, or patient risk factors), which means artificial intelligence tools cannot provide timely or accurate results. Across many institutions or collaborative studies at scale, this fragmentation creates a roadblock to discovery as well as diagnosis.
But with an organized RDM system in place, institutions can process and harmonize imaging data for thousands of patients in a short amount of time, allowing AI to highlight anomalies such as early tumors or abnormal inflammation. This not only facilitates quicker clinical decisions but also increases patient throughput in critical care and radiology units, where early intervention makes the difference between recovery and worsening.
How to Integrate IoMT Devices with an RDM Platform
Step 1 - Device inventory and Mapping:
An organization must begin by identifying and cataloging all IoMT devices within its monitoring, which may include things like handheld monitors, stationary equipment, and tools. Each of these devices collects data outputs, so these must be mapped to a standard schema. This step will ensure the compatibility of outputs with the RDM system.
Step 2 - Data Harmonization:
With mapped devices, master data rules to standardize key inputs can be applied to simplify the data that needs to be transformed. For example, a rule can be set to transform all measurement outputs to use millimeters as the units. This step will ensure that, regardless of the data’s origin, all pieces will be interpreted and stored consistently.
Step 3 - Data Pipeline
Once all the data is harmonized, it is important to create a secure and real-time data flow that transmits the data from devices to the RDM platform. This can come in the form of setting up APIs or by using healthcare interoperability standards like HL7 or FHIR. With this phase, it is important to focus on speed and compliance, as seamless integration into EHRs, reporting systems, and analytics tools is the main priority.
Step 4 - Governance and Compliance
Implement data governance protocols to ensure data integrity, such as who has data ownership and access. By building this governance structure, it promotes all processes to meet healthcare privacy regulations. This not only increases credibility but also reduces risk, which is essential in a data-sensitive industry like healthcare.
Overall, the IoMT revolution is upon us, and healthcare organizations must adopt an effective RDM platform if they want to leverage these resources as a source of patient data.
myLaminin is a blockchain-enabled, general-purpose RDM platform designed to empower researchers with comprehensive governance over their research data and operations. With myLaminin, researchers have full autonomy in the management of their research data repository with support for on-premises or cloud data storage options. This flexibility is important, particularly in the healthcare industry, where the sheer number of inputs is astounding, and data is often repurposed for later uses as well such as research and development.
myLaminin provides secure audit trails, ensuring that every action, including the entry or updating of patient data, is permanently recorded. Within the healthcare industry, data integrity has a direct correlation to patient safety, treatment outcomes, and compliance. Therefore, this level of traceability is particularly important. By offering verifiable transparency and consistent governance of data, myLaminin can help to establish a robust data management protocol that builds trust across researchers, patients, providers, and regulators.
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Alain Lai (article author) is a myLaminin intern studying Business Administration at Wilfred Laurier University.
