A few weeks ago, I was helping a healthcare organization build data views that would eventually power operational reporting. The team needed a specific metric that I had never worked with before, but no one could immediately point to its source.
I turned to the platform’s AI assistant and asked where it lives.
This led to some plausible table and column names. I queried the table and the name suggested it might be a good metric. However, three problems quickly became apparent:
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There was no explanation for the column. A metadata field required to describe the metric was blank.
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There was no table context. It was not possible to determine who owned the table, whether it was actively maintained, or whether it was deprecated.
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There was no other supporting documentation. Without a written definition, the AI had nothing to verify meaning.
At that point, the “AI found out” moment turned into a medical analytics challenge. What good is speed if the information is unreliable?
In healthcare environments, legacy tables often persist for long periods of time even as the logic becomes outdated. Metrics with similar names may be calculated differently by different teams. Without documentation, columns that appear to be correct can silently propagate flawed logic into dashboards and ultimately decision-making.
Search is now faster with AI. That didn’t make the answer any safer.
Why documentation is becoming the backbone of medical analysis
These days, medical operations are increasingly driven by analytics. Whether supporting patient services, managing operational performance, or monitoring large-scale programs, leaders now rely on dashboards and data tools to make decisions under real-world constraints such as time, staffing, cost, and service quality.
At the same time, AI is reshaping the way we discover and explain those metrics. AI assistants built into modern data and knowledge platforms help users find fields, view tables, and summarize definitions within seconds.
That speed is real. But in the medical field, speed without validation can quickly become a liability.
The uncomfortable truth is that while AI can accelerate analytical efforts, it cannot compensate for weak documentation. In fact, it is often exposed.
New workflow: Ask the system, not the person
Until recently, finding new metrics followed a familiar path: asking senior analysts, searching for old queries, reviewing documentation, or sending a message to a colleague who was working on a similar report. This process took time, but usually came with some helpful context, such as who built the field, how it was defined, and whether it can still be trusted.
Today, workflows are changing. AI assistants in platforms like Snowflake, Databricks, and documentation tools like Confluence can instantly answer questions like:
- Which table contains fields related to a particular metric?
- Where do certain columns appear on the dashboard?
- What object references this data element?
This migration will significantly reduce discovery time and enable more self-service for both analytics teams and business users.
But that speed also brings new risks. People may trust the system’s output without fully understanding how it was generated.
Why documentation is more important than ever in the age of AI
AI assistants don’t interpret medical data like humans. Get it by matching the pattern. Summarized based on name, metadata, and available text.
If documentation is incomplete or inconsistent, AI can do two dangerous things at the same time:
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Increase confidence for wrong answers Because information can be delivered fluently and quickly.
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scale mismatch Because more and more users are accessing the same vague definition without recognizing the uncertainty.
This risk is further amplified by the fact that AI assistants built into data and document platforms are still relatively new. Many are early or evolving versions. Although their behavior has improved, it is still dependent on the quality of the underlying information.
At the same time, users themselves are also using these tools for the first time. Technical staff such as analysts, engineers, and product managers commonly understand the need to validate the output of AI. They know to double check their logic, review their source tables, and check their assumptions.
But healthcare organizations aren’t just made up of technology users. Sales teams, marketing staff, and operational leaders are increasingly interacting directly with AI-driven tools, and natural language interfaces are making analytics accessible to everyone. This democratization is powerful, but it also comes with risks. Non-technical users may take AI results at face value, especially if they are not trained to question the underlying definitions.
If poorly documented, AI can turn small ambiguities into large operational errors.
What “good” looks like: Three practical changes
Organizations don’t need perfect documentation before they start. Consistent operational standards are required.
Three shifts are particularly impactful.
1) Treat metric definitions as products
When a metric appears on an operational dashboard, it needs a stable definition of what it measures, how it’s calculated, and what it should (and shouldn’t) be used for.
This is important when different teams use similar concepts in different ways. AI cannot resolve these differences unless the organization defines them.
2) Separate research and operational reporting
AI is great at exploration, from testing hypotheses to discovering candidate areas and accelerating discovery. Operational dashboards are different. They are decision-making tools. It requires governance, version control, and a disciplined change process.
Without this separation, dashboards become cluttered and inconsistent. Unless guardrails are in place, AI could accelerate that disruption.
3) Make ownership and verification visible
Metrics must have an owner, an update frequency, and a “last validated” indicator. These signals allow users to quickly and responsibly assess trustworthiness.
They also help with AI. If ownership and recency are documented, the assistant can not only retrieve where the metric is located, but also whether the metric is authoritative.
A real opportunity: AI as a catalyst for improving analytical governance
The goal is not to slow down your team with bureaucracy. That’s to prevent a future where faster access leads to faster misunderstandings.
In a well-managed environment, AI can assist healthcare organizations.
- Reduce time spent searching for data,
- Accelerate onboarding and knowledge transfer,
- Reduce repeated requests for explanations,
- and create a shared language around performance metrics.
However, these benefits are only realized when documentation is treated as infrastructure rather than decoration.
Speed is at a premium. Trust is non-negotiable.
Healthcare teams want faster answers, and AI can help provide them. But successful organizations will not be the first to adopt AI. They make it clear.
AI finds metrics in seconds. The question is whether organizations are doing the work to ensure their metrics are defined, up-to-date, owned, and trusted.
Speed is key in healthcare analytics. However, the key to speed is trust.
Tanaya Amar is a data and analytics expert with experience building enterprise analytics infrastructures and AI-driven decision-making systems across healthcare, insurance, and technology organizations, including eHealth, Align Technology, and CVS Health. Her work focuses on strengthening trust, governance, and transparency in data-driven decision-making.
