Lehigh Valley Health Network Reduces Visits with Minitab Bình luận

Lehigh Valley Health Network (LVHN) has continued to achieve its quality goals for over 100 years since its inception. More recently, Lean Six Sigma tools and Minitab statistical analysis software have been added to LVHN’s quality process to improve patient-centred care.

A project team consisting of the Chief Medical Officer, Vice President of Patient Care Services, Treatment Management Division Director, Internal Medicine Services Manager, Department of Nurse Managers, Frontline Department Nurses, Care Management Staff, and Black Belt from the Organizational Efficiency Team uses Minitab in their improvement efforts. I did.


Two adjacent hospitals merged, increasing the number of patients in internal medicine and cardiology departments by 40%. To manage the increased number of patients, LVHN’s project team investigated opportunities to improve patient flow and staff satisfaction. The number of patients discharged from both departments was approximately 550 per month.

Examples of using Minitab

The LVHN project team looked at the data they had collected over a 14-month period and found that the same problem persists at about the same time every day. Using Minitab’s dotplot shown in Figure 1 below, the team was able to determine that 95% of discharges and 57% of hospitalizations occurred between 10 AM and 8 PM.

Figure 1: Timeframes for patient admission and discharge over a 14-month period Minitab dotplots show that there is a critical time period of 10 hours.

Paul Kelly, the project team’s black belt, said, “Whenever you show this dotplot to any nurse in the world, you’re like, ‘So? It’s a fact you already know. This is our daily life.’ I will say.” said. “But when the nurses see the data I see, they say, ‘Wow, this is a really important time of our day when we need to work most efficiently. A lot of people come in during these 10 hours, and 95% of the discharged patients are discharged during this time, and more than half of the inpatients are hospitalized during this time, and they are also treating patients.’ It was a turning point because I was able to visually confirm that

What could the team have to offer to better manage this “rush hour” for employees and employees? First, we present length of visit (LOS) data for 57 discharge locations shown in Figure 2 below, where the Y-axis represents length of visit and the X-axis represents discharge code. The team wanted to further streamline this data, and Minitab’s recoat feature made it much easier to interpret the LOS data.

Figure 2: LOS at 57 discharge locations.

After recoating the 57 discharge locations into three groups, as shown in Figure 3 below, the team consulted: Should the focus be on discharge locations with higher LOS, or where more patients are discharged? Although more patients were discharged home, the overall impact may be less as these patients have the lowest LOS of the three groups. Patients discharged to skilled nursing facility (SNF) had the highest LOS, but fewer patients were discharged to SNF.

Figure 3: Recoding of the 57 discharge locations with Minitab down to three groups (for the two families shown) allows for a simpler and more comprehensible view of the LOS by discharge location.


LVHN’s solution targeted all discharge locations because LVHN felt that solutions it had built in the past could be useful again. The Department of Nursing Management has dispatched two registered nurses (RNs) to assist with the admission and discharge of patients by fellow nurses responsible for treating patients directly.

Inpatient/discharge nurses handled 30% of all admissions and discharges in both departments over a 13-month period. By sharing this value-added task, we were able to complete patient admissions and discharges faster.

Minitab’s histograms have been the primary way to keep a visual view of progress and whether or not it’s working. The team was most interested in reducing the number of discharges between 6pm and midnight, the latest time of discharge. The team was able to track changes in the number of morning discharges by recoding 24 hours a day into four 6-hour groups (see Figure 4).

Figure 4: Discharge to home (top row), other (middle row), and skilled nursing facility (bottom row), 13 months before (left column) and 13 months of using inpatient/discharge nurse (right) column) Note: The Y axis is the number of patients discharged in that time period, and the X axis is the discharge time period.

The greatest improvement was achieved in the rate of discharges with SNF during the last 6 hours of the day, a decrease of 13.6%, from 42.7% to 29.1%. Late-hour discharges also declined for home and other discharge categories.

Minitab’s dotplots, boxplots, interval plots, and histograms all helped the LVHN team select areas for improvement and see the effectiveness of the solution over time.

This case is based on a presentation presented at Minitab’s 2019 Insights conference in Leesburg, Virginia.

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