Latest / The Operations Podcast with Fexingo: Process, People, and Profit in Modern Business / How a Hospital Cut MRI Wait Times by 63 Percent
Transcript
- Lucas: So there's a regional hospital in the Midwest that went from a twenty-three-day average wait for an MRI to eight and a half days in about nine months. And the fascinating part isn't the technology—they didn't buy a single new scanner. Luna: Wait, no new equipment? How do you cut wait times by over sixty percent without adding capacity? Lucas: That's exactly the question. They essentially redesigned the whole flow around the machine. And the first thing they did was challenge an assumption that most hospitals hold onto—that the MRI scanner itself is the bottleneck. Luna: What, you mean it wasn't? I feel like everyone complains about waiting for the machine. Lucas: Right, but they tracked the actual utilization. The scanners were running at about ninety percent capacity during working hours, which sounds efficient. But when they broke down the steps—patient prep, the scan itself, and then the radiologist reading the images—they found the real constraint was the reading time. Luna: So the machine was idle but the radiologists were swamped? Lucas: Exactly. The scan itself takes maybe thirty minutes, but the report writing and dictation was taking an average of four hours per case. And radiologists were doing that in between reading CTs and ultrasounds. So they had a queue of finished scans waiting to be interpreted. Luna: So how do you fix that without hiring more radiologists? I imagine that's not a quick change. Lucas: They didn't hire anyone. They pooled the reading capacity across the whole department. Instead of the neuro-radiologist only reading brain scans and the abdominal person only doing abdomens, they created a shared queue with a triage system. Every radiologist picked up the next scan in line, regardless of subspecialty, as long as it was within their scope. Luna: That sounds like it would create a lot of pushback. Doctors don't usually love being told to work outside their comfort zone. Lucas: There was definitely resistance. But they framed it as a temporary experiment for ninety days, and they gave them the data upfront. They showed that eighty percent of scans were routine follow-ups that any board-certified radiologist could interpret. The complex cases still went to the specialist. Luna: And did the pushback die down once the numbers started improving? Lucas: It helped, but what really won them over was the next step. They also tackled scheduling. The hospital was running scanners on a first-come, first-served basis with fixed appointments. But they realized that demand spiked on Mondays and Tuesdays, and dropped off later in the week. So they rebalanced the schedule to match. Luna: So they moved appointments around, but that only does so much if the underlying demand is still there. Lucas: Right, but here's where they applied some queuing theory. They looked at the relationship between utilization and wait time. When a system is running at ninety percent utilization, the wait time grows exponentially. But if you can shave utilization down to eighty percent, the wait time drops disproportionately. Luna: So they overbooked the scanners a bit to smooth the demand? Lucas: Actually, they did the opposite. They built in idle time, which sounds counterintuitive. They deliberately left about twelve percent of scan slots open for urgent add-ons. That way, urgent cases didn't bump routine ones, and the scanners were always running at a more manageable level. Luna: But doesn't that mean you're using the machine less? Wouldn't that make the wait longer on average? Lucas: That's the counterintuitive beauty of it. By keeping utilization at around eighty-five percent instead of ninety-five, the queue essentially vanished. The average wait time dropped dramatically because there was always a slot available. And they actually increased total throughput because they eliminated the delays caused by constant interruptions and rescheduling. Luna: So the machine was always running, just with some breathing room. Lucas: Exactly. And they used a visual board to show the queue in real time—every patient, their scan type, and how long they'd been waiting. That simple transparency changed the culture. Before, the staff didn't realize that some patients were waiting for weeks because the list was buried in spreadsheets. Now it was front and center. Luna: Did that change behavior? Like, did people start prioritizing the long-wait patients? Lucas: It did. The board became a source of friendly competition among the techs. They'd see a patient who'd been waiting for two weeks and figure out how to squeeze them in by optimizing the schedule. And the radiologists could see the impact of their reading times on the queue, which motivated them to complete reports more efficiently. Luna: So it's almost like they gamified the wait time reduction. Lucas: Yeah, and it worked. They went from a twenty-three-day average to eight and a half days in nine months. But they didn't stop there. They also looked at patient no-show rates and started confirming appointments via text message 48 hours in advance, cutting no-shows in half. Luna: That's a classic operations move—reduce variability at the front end. Lucas: Exactly. And all of this was done without adding staff or new machines. It's a really clean example of how looking at the entire workflow—not just the obvious bottleneck—can unlock huge gains. Luna: I think a lot of listeners probably have a story of waiting weeks for a scan or a specialist appointment. Did they share the patient satisfaction numbers? Lucas: They did. Patient satisfaction scores for imaging went up by twenty-two points. And referring physicians noticed the turnaround time improvement, which meant they could diagnose and treat patients faster. That's a huge win for the whole hospital. Luna: It's interesting because you hear so much about healthcare being slow to adopt these kinds of operational fixes. Why do you think that is? Lucas: A big part is the culture. Healthcare is heavily regulated, and there's a fear that anything that looks like rationing could harm patients. But this case shows that better process design can actually improve care and access. And it doesn't require a huge IT overhaul—just a willingness to question assumptions and try something for ninety days. Luna: And sometimes that's exactly what's needed. You know, this reminds me of a conversation we had with a listener recently, and I think it ties perfectly into what we're talking about. They said they use our show as a way to get new ideas for their own operations challenges. And that's what this is—a real example of what's possible. Lucas: Yeah, we've heard that from a few people. It's honestly one of the best parts of doing this show. And if you've found that these conversations have moved your work forward in some small way, you can support us by buying a coffee at buy me a coffee dot com slash fexingo. It's the smallest way to keep this show ad-free and independent. Luna: Yeah, every little bit helps us keep doing these deep dives. Now, back to the hospital—I want to know if they've kept the wait times down a year later. Lucas: That's the real question. They published a follow-up after eighteen months, and the average wait time has stayed below nine days. The key was that they made the changes part of the standard operating procedure, not just a one-time project. The visual board is still up, the scheduling is still dynamic, and the reading pool is still in place. Luna: So it wasn't just a Hawthorne effect—it stuck. Lucas: Right. And they've actually exported the same model to their CT and ultrasound departments, with similar results. So the playbook is repeatable. Luna: What would you say is the one takeaway for a listener who might be facing a long wait time in their own operation? Lucas: Look at the step that comes after the obvious bottleneck. In this case, everyone assumed it was the scanner, but it was the radiologist. And that's often where the hidden constraint lives. Take a week to track the actual flow, not the perceived flow, and you might find something surprising. Luna: And don't forget to build in a little slack for the unexpected. Lucas: Exactly. Idle time isn't waste if it prevents the whole system from jamming. Sometimes the most efficient thing you can do is slow down a little to speed everything up.