From Documentation to Clinical Intelligence: What AI Scribes Miss, and What EMRs Must Become

Electronic medical records (EMRs) were never intended to be digital filing cabinets. They were introduced with a promise to reduce paperwork, improve continuity, and make clinical information easier to use over time. Despite this, decades later, many clinicians feel that documentation—rather than patient care—has become the primary work.

In Canada, the administrative burden has reached a tipping point. Recent 2024 data shows that Canadian physicians lose an average of 15 to 20 hours per week to administrative tasks—equivalent to over 18.5 million hours of collective time annually across the country.

Medical associations and researchers have repeatedly noted that EMRs have not meaningfully reduced this burden. In fact, according to the 2024 National Survey of Canadian Physicians, more than 75% of clinicians now spend over an hour of their personal time on EMR documentation after every typical workday. While AI scribes for doctors have recently emerged as a welcome response to this frustration, they are not the end of the story: they don't address the core problem.

The Problem Was Never Just Typing

Clinicians didn't necessarily ask for better notes—they asked for less work. Over time, EMR systems have been optimized around templates, checkboxes, and "completeness" to satisfy billing and compliance needs. This has created a system where value is measured by what gets written down rather than what carries forward to the next encounter.

Most of the actual work of medicine happens in the "connective tissue" between visits:

• Remembering what mattered during the last appointment to inform the next one.

• Managing clinical decision support and deciding on the next clinical steps.

• Tracking whether a condition is improving or worsening over months.

• Avoiding the repetition of tests or history-taking.

Documentation, in its current form, does very little to support this longitudinal work.

What AI Scribes Actually Solve

AI scribes have been a meaningful improvement. Recent Canadian pilots, including those led by OntarioMD and the Doctors of BC, have shown that AI scribes can reduce after-hours documentation time by up to 3 hours per week, with some clinicians reporting a 70% to 90% decrease in total time spent on paperwork.

However, speed of capture does not change how information is used. Most AI scribes still produce the same artifact: a clinical note. While it may be cleaner and faster to generate, it often ends up in the same place free-text notes always have—archived and effectively "lost" in a digital cabinet.

Why Notes Don’t Drive Care

When clinicians prepare for a follow-up visit, they rarely reread entire historical notes. Instead, they rely on structured elements of the record to paint a longitudinal picture of the patient’s health:

• Problem lists

• Medications

• Results and Orders

These fields are prioritized not because they tell the whole story, but because they are the only parts of the record designed to be reused at scale. Meanwhile, the narrative—which contains crucial signals like how symptoms are evolving or subtle changes in treatment response—is captured and then effectively siloed.

Research into EMR data usage reveals a stark reality: in a typical patient population, only about 13% of the vital clinical concepts found in narrative notes ever make it into the structured fields of the record.

The remaining narrative contains the most important clinical signals, yet it does not travel, it does not compound, and it does not reduce future work. AI scribes help you get to this static endpoint faster, but they don't change the nature of the endpoint itself.

Why Structured Data Matters—and Why It’s Not Enough

Structured data is powerful because it is durable and queryable, enabling continuity and population-level insights. However, structure alone isn't the solution. Over-templated systems often flatten clinical nuance, and rigid data models can fail to reflect how clinicians actually think.

The goal isn't perfect standardization; it is reusable meaning. The challenge is transforming clinical workflow into data that preserves context, carries forward automatically, and remains useful without requiring extra effort from the physician.

The Missing Chain: From Workflow to Action

This is where most systems break. At Aeon, we believe the core problem can be framed as a simple progression:

Clinical workflow → Structured data → Action → Reduced future work.

If data does not trigger action, nothing changes. If it does not reduce repetition, the administrative burden remains. If it does not compound over time, it cannot truly transform care. Reducing documentation time today is helpful, but reducing the cognitive load of tomorrow is what is truly transformative for physician wellness.

What a Deeply AI-Enabled EMR Makes Possible

A deeply AI-enabled EMR is not one that simply bolts a scribe onto a documentation module. It is a system where AI is embedded in the very understanding of clinical work. In this model:

• Narrative information is automatically transformed into structured insight.

• Clinical context carries forward without manual re-entry.

• Trends surface automatically without manual tracking.

• Follow-ups are prompted without the need for external "sticky notes" or memory.

The system should do more than record what happened; it should help facilitate what should happen next. This is not about replacing clinicians—it is about finally building systems that work the way clinicians do.

A Different North Star

Fewer clicks and better notes are important, but they aren't the ultimate goal. The goal is an EMR that makes every visit reduce the work of the next one—a system that turns clinical effort into lasting intelligence instead of one-time documentation.

AI scribes are an important first step, but the real shift is the transition from static documentation to reusable clinical intelligence. That is the future of AI-enabled healthcare we are building at Aeon.