Preparing for a large-scale patient relocation

Preparing for a Large-Scale Patient Relocation

Moving patients involves uncertainty, as any healthcare leader knows. Safety, in 2013, a U.S.-based health system reported that 219 patients fell while being transported across its hospitals and clinics, must be balanced with the right staffing and procedures; this requires precision and careful planning. On a specific Saturday, a major hospital relocated 185 patients from an existing facility to a new adjacent building more than 1,200 feet away. The organization aimed to deliver a safe and efficient relocation by combining systems engineering principles and methods with a digital planning approach.

To support the operation, an EYF digital planning solution was used to create a 3D model of the move conditions. Building this model helped identify potential problems that could occur during the relocation and evaluate solutions in a risk-free virtual environment. The model also supported defining the optimal staffing required for the move and estimating how long the operation would take.

Solving problems before they arise

Because completion time could vary due to delays, a wide range of potential impediments was considered. Using data gathered from several practice moves, it was possible to identify delays and bottlenecks at every stage of the journey, and to understand how a delay in one unit could affect other units and the overall move time. Key insight: the move could be completed in 6 hours and 5 minutes with no delays, and the model also provided alternative finish times across multiple delayed scenarios.

Maximizing valuable human resources

Since staff time was the primary resource required during the relocation, determining the right number of move teams was critical. These teams consisted of transport and equipment staff, led by either a physician and a nurse (for critical patients) or two nurses (for non-critical patients). Different configurations were evaluated, and five teams were found to be ideal. The analysis showed that while four teams could have completed the move, five teams ensured there would be no downtime.

Predicting a successful outcome

The actual move started at 7:00 AM and finished at 1:35 PM, taking 6 hours and 35 minutes. This aligned with the model’s predicted finish time of 1:15 PM to 1:39 PM, assuming a 3–4% delay caused by patients not being ready. In addition to validating the move plan and forecasting the final time, the model delivered important added benefits: by finishing early, work hours were saved for dozens of staff involved in the relocation, as well as for staff in the new building who had been asked to work a double shift. The organization also now has a baseline model of its system that can be used for future situations.