Medical Technology

Cross-border data transfers are essential for medical technology companies to detect, monitor, and treat medical conditions in a safe, effective, precise, and timely manner. Such data transfers help support the real-time monitoring of patient health conditions at the request of patients and their clinicians, offering benefits from the perspectives of patient comfort and care, remote analysis and treatment, monitoring for safety and efficacy of deployed technologies, refinements to treatment pathways and clinician education, and researching and engineering therapy improvements and innovations.
Disseminating the Benefits of Medical Technologies Across Borders

Cross-border access to healthcare data improves access to medical technology solutions that can improve patient outcomes and medical treatments. Expanding cross-border access to such technologies and allowing for the secure and protected transmission of data across transnational digital networks can increase patient access, reduce health disparities, and support innovation in safe and cost-effective technologies that can enhance health outcomes and quality of life. This may include data collected in the normal use of the medical devices, in hospital and other medical records, and from other devices and consumer technologies. Such data is also typically aggregated, anonymized, pseudonymized, encrypted, and/or subject to other data security mechanisms, including privacy-enhancing technologies in the course of such transfers. Ultimately, the ability to realize the benefits of digitally connected medical technologies for patients around the world depends greatly on the medical technology industry’s ability to successfully and securely access, aggregate, and use health data across transnational digital networks.

Realizing the Cross-Border Potential of Data Analytics in Medical Technologies

Cross-border data analytics in medical technologies can help medical researchers and clinicians better understand and predict patterns and responses, including in longitudinal clinical studies, to improve patient treatments and outcomes. The data used in such analytical processes—aggregated, anonymized, pseudonymized, encrypted, and/or subject to other data security measures—may derive from clinical trials, collaborative research arrangements with hospitals and health systems, as well as the “real world” data generated by medical devices from their ongoing clinical use. For example, cross-border access to banks of surgical image data in actual clinical use or from videos recorded of surgeries anywhere in the world can help in training and developing data analytical models for safer, less costly, and more effective medical technology applications.

Improving Real-Time Patient Diagnosis and Therapy

In many cases, medical technologies function optimally through real-time measurement, display, transmittal, and interpretation of cross-border data. When this functionality is compromised, such as through data transfer restrictions, these technologies do not achieve their full diagnostic and therapeutic potential.

  • Diagnostic technologies include diagnostic electrocardiograms, magnetic resonance imaging (MRI) devices, and implantable or disposable equipment including portable testing kits. This includes insertable cardiac monitoring systems that provide long-term monitoring of the heart for suspected arrhythmias and atrial fibrillation. By leveraging predictive analytics through cross-border data, such systems have the potential to save lives by distinguishing different arrhythmia types and by providing the patient’s clinicians with actionable information to inform their diagnostic and treatment decisions.
  • Therapeutic technologies include radiotherapy equipment for oncology treatments, insertable cardiac monitors, implantable cardioverter-defibrillators, and grid mapping catheters. These technologies include robotic assisted surgery systems, which are developed based on artificial intelligence (AI)-enabled digital models and which can improve surgical precision, consistency, technical capability, and speed in the operating room. These AI-enabled models can help identify procedural steps during an operation and learn based on the outcome. They can also monitor vital signs during surgery to flag possible issues, such as blood loss, and can detect when a surgical instrument has moved outside the area of interest and cut power as a safety precaution. In the intra-operative stage, the captured video can enhance displays (providing more relevant information); in the post-operative stage it can provide useful analytics.
Ensuring Cross-Population Representation in Medical Technology Development

Cross-border data is critical to ensuring that new medical technologies are safe and effective across different demographics, populations, and regions. Diverse and representative data is critical to identify clinically relevant differences among patient cohorts to detect and eliminate potential distortion in treatment protocols and access, and other sources of bias and disparity. To avoid distortion or bias in the data sets used to develop new medical technologies, the underlying data sets should be drawn from a sufficiently large and diverse population of participants and should contain sufficient data to create and train relevant analytical models. Constraining such data sets within national borders would make the data less robust and could risk introducing unnecessary distortion or bias. Ultimately, the more data from diverse sources, the more accurate, safe, and unbiased patient outcomes will be.