From Analytics to Action: How Data-Driven Insights Can Transform Healthcare Operations

By Pang Adomat | Analyst Journey

Dashboard View

In today’s complex healthcare environment, the difference between a smooth patient experience and a missed opportunity often comes down to operational timing. Schedulers, contact centers, and front-line teams are the connective tissue of patient access, and when operations falter, patients feel it first.

That’s why I believe data is not just a reporting function—it’s a call to action. As a data analyst supporting scheduling operations at one of the nation’s largest academic health systems, I’ve learned that numbers alone don’t solve problems. But when used wisely, they give leaders the foresight to respond, recover, and redesign for impact.

Let me take you into a real-world scenario where analytics moved from insight to intervention.

The Forecast Said Stable. The Reality Said “Code Red.”

We started the month with a solid forecast: call volumes across Radiology and Ambulatory Services were predicted to remain steady, with minimal impact from seasonal fluctuations. Our workforce plans were approved, schedules were optimized in Genesys Cloud, and we had confidence going into the quarter.

But then something changed. Fast.

Within 48 hours, we saw a 30% drop in service level on multiple queues. Hold times spiked. Abandonment rates soared. And patient complaints began pouring in—not about the care, but about access to care.

At first glance, our dashboards didn’t explain the collapse. There was no major holiday. No weather emergency. No tech outage.

But a deeper look at the intraday performance metrics and adherence data revealed a pattern: multiple agents were either calling out or being pulled into tasks outside of call handling. At the same time, new appointment types were added by departments without routing updates or agent training.

What looked like a “mystery dip” was, in fact, a perfect storm of staffing strain, knowledge gaps, and workflow disconnects.

Analytics Gave Us the Warning. Action Closed the Gap.

Here’s what we did next—and why this matters for healthcare leaders at every level:

1. Escalated an Operational Alert:

   Within 3 hours of recognizing the deviation, I drafted a Red Alert Dashboard using Power BI, combining Genesys Cloud real-time data with historical staffing ratios. It flagged every queue with a deviation over 20% from the forecast. Managers got an immediate snapshot—no waiting until end-of-day reporting. This wasn’t just quick thinking—it was a textbook example of how data-driven healthcare operations can respond to crises with agility, using real-time insights instead of reactive fixes.

   
   

2. Translated the Data for Decision Makers:

   I met with leadership, not just to report the numbers, but to explain the “so what”:

   “If we don’t stabilize Priority Line and MRI queues within 24 hours, we risk losing new patient appointments that impact both access scores and downstream revenue.”

   
   

3. Suggested a Smart, Low-Disruption Move:

   Instead of calling for overtime or urgent hiring (both slow and costly), we proposed a temporary skills reassignment: re-routing trained but underutilized agents from low-volume specialties to assist in radiology high-demand lines for the week.

4. Provided Just-In-Time Training:

   I collaborated with training leads to develop a “Quick Card” training guide, addressing common questions about MRI and CT scans. We uploaded this into the Genesys knowledge base and ensured supervisors had access.

   
   

Why This Was a Smart Operational Move

In just 48 hours, we improved:

• Radiology Service Level from 58% to 83%

• Average Speed of Answer from 10+ minutes to under 3 minutes

• Abandonment Rate dropped by 18%

And we didn’t hire a single new staff member.

That’s the power of aligning data, people, and process—fast.

From Forecast to Foresight: Lessons in Data-Driven Healthcare Operations

This scenario isn’t unique. Every hospital, every call center, every scheduling team faces moments when the forecast breaks.

But what separates operational chaos from operational agility is how quickly and smartly we respond.

We’ve seen that the most effective data-driven healthcare operations aren’t built on perfect forecasts, but on systems that adapt and alert when things go off track.

Here’s what I’ve learned from leading these efforts on the ground:

1. Forecasting is Not a One-Time Job

Forecasts can’t be “set and forget.” I’ve built models that incorporate:

• Seasonality (e.g., flu season spikes)

• Clinic event calendars (new MRI machines coming online)

• Agent behavior patterns (adherence post-lunch, call fatigue)

We update our models weekly—not monthly—to stay responsive. And when the model is wrong (because life happens), we treat it as a signal, not a failure.

2. Data Has to Travel Fast

Dashboards that take two days to update are already stale. We’ve shifted to intraday dashboards—and more importantly, real-time alerts for managers.

Think: color-coded snapshots that say “Act now.”

If your analytics can’t tell the right people the right message at the right moment, it’s just decoration.

3. Build Bridges Between Data and Action

This is where most healthcare systems struggle. Analysts live in reports. Ops leaders live in reality. The translator role is often missing.

That’s the role I play:

Helping leaders not just see the fire, but decide what kind of water to use.

Why This Matters More Than Ever

In a post-pandemic healthcare world, patient expectations have changed.

Everyone deserves timely access to scan appointments; waiting weeks is unacceptable. No one should have to endure long holds.

And as a healthcare system, we don’t just lose satisfaction when that happens—we lose trust. We lose revenue. We lose retention.

We also lose talented staff to burnout when operations aren’t aligned with reality.

Analytics is no longer optional—it’s an operational imperative.

The Role of the Next-Gen Director of Operations

A modern Director of Operations isn’t just a project manager. They’re:

• A systems thinker

• A data translator

• A real-time responder

• A people-first strategist

They know how to take a forecast and say: What’s the backup plan? What’s the risk signal? Where do we flex?

They bring together tech, empathy, metrics, and urgency to lead from the front.

That’s the kind of leader I strive to be. And that’s the mindset I bring to every challenge, especially the unexpected ones.

Final Thoughts

In operations, every number tells a story. But it’s up to us to read it—and respond before the story gets worse.

If you’re leading a healthcare system, or managing a call center, or trying to schedule the unschedulable—know this:

The answers are in your data. You need someone who knows how to handle it.

And I’m here for that.

Further Discussion

Every healthcare team faces moments when the data doesn’t match reality. What’s your process for turning insight into action when time is tight and stakes are high? Share your thoughts—I’m always learning from others in the field.

1. How can data analysis improve decision-making in healthcare systems and call centers?

2. What specific challenges do you face when interpreting data in your operations, and how can they be addressed?

3. In what ways can effective data management prevent potential issues before they escalate?

4. What skills or expertise do you believe are essential for someone handling data in operational settings?

5. How can organizations ensure they are utilizing their data to tell the most accurate and useful stories?