Healthcare data is growing at 36% annually, faster than financial services, faster than media. Yet the majority of what gets collected sits idle, generating cost and complexity without generating insight.
The problem isn’t a lack of data. It’s that the dominant strategy in medtech has become “collect everything” — not because it serves a clinical goal, but because modern devices make it technically trivial and free to do so. With no penalty for over-collection, volume has quietly become a proxy for value.
The consequences go beyond wasted storage. Rare but critical signals (near-misses, configuration drift, unsafe workarounds) get buried under routine telemetry. Context about who used a device, under what conditions, and within which workflow disappears. And when AI enters the picture, it doesn’t solve this; it amplifies it. Models trained on indiscriminate data produce statistically refined noise, not meaningful clinical insight.
“More data from one device isn’t the goal anymore. Better decisions across many devices is.”
The shift Suntra is advocating for, and helping clients execute, is one of intentionality. Borrowed from lean manufacturing’s core principle of measuring only what you intend to improve, data intentionality means every data stream is defined by the downstream decision, safety signal, or regulatory evidence it supports. Intentional data gets context, governance, and traceability. Everything else expires.
Achieving this also requires confronting fragmentation. When data is siloed across devices, vendors, and departments, each reflecting its own local purpose, it’s impossible to build shared clinical intelligence at scale. The industry needs devices that embed context at the source, emit curated signals rather than raw waveforms, and integrate passively into clinical workflows rather than demanding new ones.
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