Despite industry rhetoric, healthcare technology development overwhelmingly prioritizes physicians over patients. This creates a significant gap, as the ultimate end-user's needs are often an afterthought in solution design.

Advanced AI models are ineffective in clinical settings without a robust data layer. Ambience had to solve fundamental problems like pulling messy context from inconsistent EHRs and preserving 'decision traces,' which are often destroyed by existing systems with mutable data structures.

We possess millions of data points on interventions, but they are useless to AI models because they're trapped in thousands of disparate EMRs in varied formats. The challenge is not generating more data, but solving the human incentive and alignment problems required to create unified data registries.

Embedding AI into the EHR is not a simple upgrade. A physician intuitively filters hundreds of data points down to a few critical facts for a query. An AI wading through the entire record—which can be longer than Moby Dick—may get distracted by noise, making the doctor's curated input more effective for now.

A primary barrier to modernizing healthcare is that its core technology, the Electronic Health Record (EHR), is often built on archaic foundations from the 1960s-80s. This makes building modern user experiences incredibly difficult.

The dominant "fee-for-service" payment model commodifies primary care into discrete office visits. It fails to reimburse doctors for crucial work like communicating with specialists or following up on tests. This forces high patient volumes and short appointments, undermining the physician's role as the safekeeper of a patient's full medical story.

The successful early adoption of AI in healthcare was brilliant because it first targeted the administrative burdens that clinicians hate, such as documentation (scribes) and billing. By winning the hearts and minds of powerful incumbents with immediate quality-of-life improvements, the industry built momentum for more complex clinical applications.

Simply patching existing Electronic Health Records is insufficient. The next generation must be architected from the ground up with three core principles: offline functionality for resilience, a mobile-native experience, and generative AI at their core.

The CEO of Datycs applies lessons from standardizing wireless networks in the 90s to today's healthcare challenges. He compares siloed EHRs to old proprietary cell towers, highlighting how open standards like FHIR can solve a problem that the telecom industry conquered decades ago.