The healthcare and life sciences fields are responsible for an astounding 30% of the world’s data volume and, by 2025, are projected to experience a compound annual growth rate of 36% in data generation, outpacing other industries like media and entertainment. This surge is driven by several factors, like advancements in medical imaging, which are pushing image sizes to unprecedented levels. Medical images, a crucial component of medical records, are subject to a variety of laws and standards. Depending on the type of record and geographical region, these records are typically retained for 5 to 10 years.
The storage and archiving of this data present a challenge, leading to escalating security and data management costs. This issue is becoming a concern for healthcare CTOs and CIOs, who are grappling with these rising costs while striving to maintain the integrity and accessibility of critical medical data, without putting patient-centered outcomes at risk. It is time for healthcare organizations to self-diagnose, chart a path forward, and take a page out of the Radiology book for guidance.
Experts in the medical imaging field consider costs, storage, and bandwidth as some of the key blocking points for developing digital imagery in modalities like pathology and radiology. Typical costs are comprised of development, maintenance, and archiving & management of the data created, and implementing digital image storage involves high upfront costs for scanners and long-term archiving, which may strain new project budgets. In addition, real-time image compression and transmission within limited bandwidth communication channels is crucial, particularly in low-speed environments, and may disqualify some advancements until sufficient bandwidth becomes available. Although regulated and mostly intolerant of lossy methods where some data is lost, medical image compression is feasible due to the presence of ‘statistical redundancy’, where large, smooth sections of an image contain nearly identical pixel values or duplicate information with the goal of eliminating this redundancy and efficiently encode the remaining data.
Historically, technological developments in medical imaging have been driven by real clinical needs and continuous improvements. Radiology, for example, started exploring and developing digital image processing several years before other clinical disciplines. In the same vein, MRI systems have evolved over 40 years, becoming less time-consuming and more intelligent. The motivation behind this early development was twofold: to reduce the cost of archiving conventional X-ray images and to expedite the production of these images compared to traditional photographic methods. However, the journey towards digitalization in pathology has been slower. The digital images in pathology are approximately ten times larger than those in radiology, requiring more sophisticated storage management throughout their lifecycle.
The primary hurdle in the path of digital pathology is the cost associated with its implementation. Despite the challenges, the transition towards digitalization in pathology, though expensive, is an inevitable and crucial step forward.
In recent years, healthcare and life sciences have leaned toward a reactive rather than proactive approach to administering care. This is characterized by a “treating for” rather than “planning for” approach. Interestingly, this mirrors what is happening in the world of data management. Companies allocate significant resources to collecting data in response to the massive volumes generated by advancements in generative AI technologies and the proliferation of IoT devices. However, a more thoughtful approach to both administering care and data planning is essential for long-term success.
In conclusion, a shift towards proactive strategies could benefit both the healthcare and data management sectors, leading to better outcomes and more efficient practices. By doing so, healthcare organizations may better address the challenges related to costs, ultimately leading to improved patient outcomes and more efficient diagnostics.
Dr. Ofer Markman
is Filo Systems' Co-founder and VP Business Development
