Beyond Better Data: What Happens After the Dashboard Is Built?

One of the things I have learned through my work in healthcare is that building the dashboard is often the easy part.

Getting people to use what the dashboard tells them is much harder.

Healthcare organizations have invested enormous resources in data infrastructure, analytics platforms, business intelligence tools, and increasingly, artificial intelligence. We can visualize trends, compare populations, identify risks, and monitor performance in ways that would have been difficult to imagine even a decade ago.

But I often find myself asking a deceptively simple question:

What happens after someone looks at the dashboard?

Because that is where the real work begins.

A dashboard is not a decision

A dashboard can tell us that something is happening.

It can tell us that emergency department utilization is increasing. That readmissions are above target. That a patient population is experiencing poorer outcomes. That a particular process is taking longer than expected.

But knowing something is happening is not the same as knowing what to do about it.

And even when we know what should happen, there is another challenge:

Who is going to act?

I've seen organizations put tremendous effort into developing sophisticated reporting tools, only to discover that the people expected to use them don't have the time, authority, resources, or workflow support to respond to what they're seeing.

The information may be accurate.

The analysis may be excellent.

The dashboard may be beautifully designed.

And still, nothing changes.

The gap between insight and action

This is one of the reasons I believe the conversation about healthcare data needs to move beyond data itself.

Consider the progression:

Data → Information → Insight → Decision → Action → Outcome

A dashboard can help move us from data to information and, in some cases, to insight.

But the remaining steps require people.

They require leadership, accountability, clinical judgment, organizational processes, and sometimes difficult decisions about how resources should be allocated.

That means the success of a data initiative shouldn't be measured only by whether the dashboard was built or whether the analytics are accurate.

We should also ask:

Is someone using it?

Are they making different decisions because of it?

Are those decisions changing what happens on the ground?

Are patients experiencing better outcomes as a result?

Those are much harder questions.

They are also much more important.

The "last mile" of healthcare data

I think of this as the last mile of data.

We spend enormous effort getting information into systems. But the final distance—from information to action—is where much of the potential value can be lost.

Imagine a clinical team receives information showing that a particular group of patients is at increased risk.

The data has done its job.

But now what?

Does the care team have a process for identifying those patients?

Does someone own the response?

Is there an intervention available?

Does the intervention fit into the existing workflow?

Does the organization have the resources to implement it?

And, perhaps most importantly, does anyone measure whether the intervention actually worked?

Without those pieces, better information can become just another notification, another report, or another item competing for someone's attention.

This matters even more as AI enters healthcare

Artificial intelligence is going to dramatically increase our ability to generate insights from healthcare data.

But I don't think the fundamental challenge changes.

In fact, it may become more important.

If we can generate ten times as many insights, but don't improve our ability to determine which insights matter and what to do with them, we may simply create more information for people to process.

The goal shouldn't be to create more alerts.

It should be to create better decisions.

And ultimately, better outcomes.

That requires designing technology and data systems around the people who will actually use them—not simply around what the technology is capable of doing.

Start with the decision, not the data

One question I increasingly encourage healthcare organizations to ask is:

What decision are we trying to improve?

Then work backward.

What information is needed to make that decision?

Who needs the information?

When do they need it?

What action should follow?

How will we measure whether that action made a difference?

That approach changes the conversation.

Instead of starting with:

"What data do we have?"

we start with:

"What are we trying to accomplish?"

The data then becomes a means to an end rather than the end itself.

From dashboards to outcomes

This is an important part of the thinking behind Beyond Better Data: What Actually Changes Healthcare?, written by TLI Chairman and President Bill Oldham.

The book challenges us to think beyond our ability to collect and analyze information and to focus on what actually changes as a result.

That is also the conversation I want TLI to continue advancing.

Because healthcare doesn't need more dashboards simply because we can build them.

We need information that helps people see something that matters, make a better decision, take meaningful action, and ultimately improve an outcome.

That's a much higher standard.

And I believe it is the standard we should be pursuing.

So here's the question for this week's Beyond Better Data Conversation:

What happens in your organization after the data reveals something that needs to change?

Is there a clear path from insight to decision to action?

Or does the dashboard simply become another place where information goes to be viewed?

I'd love to hear how others are addressing this challenge.

The Beyond Better Data Conversation is a TLI thought-leadership series inspired by Beyond Better Data: What Actually Changes Healthcare? by Bill Oldham.