The boundaries of modern healthcare systems are rapidly expanding. A cardiologist can now review an echocardiogram from anywhere across the country.
A surgeon can rely on connected systems and robotic instruments to support complex procedures at a remote location.
But this increasingly connected model means that each interaction generates data.
While this data has immense value, this value depends significantly on whether a doctor can access and use it when needed.
Medical data and the need for fast storage
The first real hurdle is the sheer amount of data produced in modern medicine. Just a decade ago, an electronic health record was mostly text, a few pages of notes and lab values. Today, the digital thread around a single patient can include high-resolution pathology reports, 3D cardiology imaging, and continuous teleconsultation feeds.
Healthcare institutions may face challenges running modern, data-intensive operations on storage architectures designed for simpler workloads: static files that got written once and rarely touched again.
That model breaks the moment a clinician needs to pull up a patient's scan history for comparison; then the likelihood is that the system begins buffering.
In a consultation, these kinds of delays can affect workflow efficiency.
Artificial intelligence (AI) may help address physician workload challenges and diagnostic backlogs, but only if the infrastructure that supports it keeps up.
Algorithm performance often depends on the speed and availability of the data it processes.
If a high-resolution oncology scan takes too long to load off a legacy server, the AI stalls with it. Even highly sophisticated models can be limited by data-access constraints, and weak data access can affect the workflow before the algorithm even gets a chance to work.
And the data can no longer sit in departmental silos. A scan from radiology often needs to be available rapidly and with relevant context to oncology, and then to whichever AI tool is assisting the surgical team. Most legacy storage systems were not built to do this.
The need for fast data lakes
A data-informed diagnosis, treatment plan, or surgical plan calls for modernisation of storage technologies employed in data lakes for AI readiness, as AI workloads often benefit from storage that is fast, scalable, and power-efficient.
In other words, high-performance and high-capacity solid-state drives (SSDs) can play an important role in supporting AI-related storage requirements.
Healthcare systems are undergoing a rapid transformation in India, and as AI becomes more ubiquitous in healthcare data management, more data than ever will be generated and used continuously. Personalised medicine, predictive diagnostics and connected care increasingly depend on the ability to store, access, and move large amounts of data efficiently. It is an exciting shift for patient care, but without appropriate infrastructure, healthcare organisations may face challenges realising the value of their data.
The author is senior director, technical product engineering and management, at Sandisk.
The opinions expressed in this article are those of the author and do not purport to reflect the opinions or views of THE WEEK.
