Case Study

Reducing referral-to-seen times for liver and kidney transplants

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The USC Transplant Institute at USC University Hospital, Los Angeles, CA, comprises five transplant service lines: heart, lung, liver, kidney, and pancreas.

A mid-sized transplantation program, the Institute's goal is to complement its outstanding medical services with greater operational efficiency in order to serve more patients, particularly in its kidney and liver transplant programs.

Targeting double-digit volume growth over the next few years, the Institute asked the Performance Solutions team at GE Healthcare for help in analyzing process inefficiencies in the two programs and developing capacity management solutions with long-term sustainability.

donnell_headshot.jpgMike Donnell, M.H.A. is the Chief Administrative Officer of the USC Transplant Institute. He joined the Institute in December 2009 after holding similar positions at Baylor Regional Transplant Institute in Texas and Froedtert & The Medical College of Wisconsin. Donnell spoke recently about the challenges that the USC Transplant Institute faced and the significant advances they've already made in improving patient access and capacity.

How did the operational problems at the Institute manifest?

Kidney and liver transplant patients were waiting three to four months to be seen by the transplant team. The referral-to-seen cycle time was 111 days for kidney patients and 84 days for liver patients. There was a high volume of no-shows and patients were ‗slipping through the cracks' because we lacked an effective way to track program participation. Administration realized there were problems, but had no clear understanding of where in the process the bottlenecks were occurring and why. That's why the GE Healthcare Performance Solutions team was brought on board in November 2009.

How did you go about tackling the problems?

We divided the intake process into four phases, beginning with the date of the patient's referral to the program, and established cycle-time targets for each one:

  1. Referral to medical clearance - 15 days
  2. Medical clearance to financial clearance - 10 days
  3. Financial clearance to scheduled - 5 days
  4. Scheduled to seen - 15 days

Our goal was to reduce the referral-to-seen cycle to 45 days, compared to the 111 days (kidney) and 84 days (liver) that it was currently taking. The GE facilitators then led our people through value-stream mapping exercises, analyzing every step in each phase.

What were the major roadblocks?

Two fundamental problems--sub-optimal patient access and lack of data transparency--had their roots in our paper-driven intake process. Even though the Institute had deployed OTTR (Organ Transplant Tracking Record) software, it was ineffective because the essential workflow mapping had not been done. Basically the system was running on out-of-box functionality. Once we determined the right workflow methodologies, policies, and procedures to meet our goals, we were able to reconfigure and redeploy OTTR to centralize scheduling, manage workflows, and monitor performance metrics.

The improvements were dramatic. In seven months, we surpassed our 45-day target metrics for both clinics. By July 2010, the average referral-to-seen cycle time in the liver clinic was 27 days (down from 84), and the patient backlog had been eliminated. The average cycle time in the kidney clinic was 38 days (down from 111). My expectation is that we will continue to improve on our metrics so that the referral-to-seen cycle time will be at 30 days for 80% of our patients.

Can you give us an example of how policy, workflow and technology intersect?

We had 1500 referrals in the queue that were unaccounted for, some going back to 2005. The Institute had a ―three no-shows and you're out‖ policy; if a patient missed three appointments without an excuse, they were to be terminated from the program. Once GE helped us configure OTTR correctly, we had the technological infrastructure to track patients appropriately, weed out those who were noncompliant, and manage care more efficiently for the rest. After we started enforcing the policy, we eliminated about 750 people from the queue.

By reducing clinic no-show rates and improving utilization, we've been able to create additional appointment slots for 138 liver patients and 300 kidney patients annually. These improvements also support the Institute's mission of being a good steward of the donor's gift, helping us make sure that the next person who receives that kidney or liver is the best candidate we can find.

storyquote.JPGHow have the changes helped you meet program goals?

This project has enabled us to free up capacity for 450 more patients without hiring more staff or adding more rooms. We are simply using our existing resources in a more efficient and productive way.

Increasing capacity will help us meet our goal of growing market share. And that's important for the hospital. But even more fundamental is the potential to save more lives. The quicker we can get patients through the evaluation and listed, the quicker we can get them a transplant.

About 20 to 25% of our liver transplants end up being combined liver/kidney transplants. If we can get these patients in earlier and preclude the kidneys from shutting down, we may avoid the risk and high cost of combination transplants. Our goal is to reduce those cases by 15% over the next few years.

How did the GE team contribute to the improvements?

By providing rigor and discipline. I've found GE people to be very methodical and consistent in their approach to problem-solving. And that's a good fit with my mentality. Coming from a military background, I am a firm believer in checklist discipline. Transplant cases are pretty straightforward until they get off the track. We want to minimize those instances by making sure we provide the best care for patients as promptly and cost effectively as possible. GE provided unbiased third-party expertise that helped us move to a very rigorous and disciplined approach that we will adhere to going forward.

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  • wil.fernandes@ge.com November 1, 2010 10:10 PM

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