Bed Management Simulator

Bed management has always been a very tricky logistical problem. As both census and the incidence of chronic disease in the population rise, it is become more difficult all the time. The symptoms of a difficult bed management system can manifest in many different ways. Here are just a few examples:

• Excessive surgery cancellations, usually because the right kind of post-surgical bed is not available
• Too many patients in off-service or less-than-ideal beds for their condition
• Longer than expected stays

There are many potential factors that can lead to the symptoms listed above. Because every hospital system is composed of many interacting components, and because each staff member often only has visibility into his or her own area, the source of the problem will appear to be different depending upon which staff member you ask. Even if each answer is correct, and they may very well be, the only way to come to a solution is to understand how all of the pieces fit together.

With all of the data collected today, the most obvious answer seems to be to turn to the hard evidence waiting in the data. Even that answer, however, is not as simple as it may seem. Currently, data is contained in various systems, and each one might give a slightly different view of the problem. Moreover, each may reflect only a part of any patient’s stay, or only a slice of the total patient population.

We believe that the best way to move toward more efficient bed management is to tackle the problem in multiple steps. The first stage is to understand the flow patterns of your particular patient population, both in aggregate and by individual patient type. Only by understanding these aggregate flows, and the associated demands they create on each unit, is it possible to develop optimal bed capacity allocations and bed management rules (such as policies on bed reservation, appropriate utilization levels, off-service placement, priorities, and so on).

Having understood the flows, it is then enormously helpful to simulate them against the bed allocation and management rules in a virtual environment, where you can test out the impact of these policies on overall system performance measures such as total length of stay by patient type.

We have developed a simulation that provides you with the capability to test, understand and improve your bed allocation strategies. Contact us today for a personalized demonstration.

A simulation-based decision support tool for Bed Management Analysis and Improvement, if performed by an experienced team, has a large number of benefits including:

• Reduction in number of patients off-service.
• Fewer delays
• Reduced risk decision making
• Cost reduction. Because the simulation will allow the client organization to have more confidence in the number of resources needed to maximize throughput, the need to hire extra equipment/staff will be greatly reduced.
• Reduced risk decisions. The simulation tool will allow the team to understand—in advance - likely outcomes across a broad range of possible scenarios, which will in turn allow you to be prepared to respond appropriately to actual conditions as they occur.
• More efficient decision making. The simulation tool, in addition to serving as a great communication platform, will facilitate rapid evaluation of potential move strategies, thereby leading to a more time-efficient decision making process.
• Effective use of up-to-date data. For initial planning purposes, the model is typically built with a simulated list of starting inventory. However, during on-going use, the simulation tool is built to allows for the import of actual census levels. 
• Improved patient goodwill.  Because the simulation tool enables client organizations to develop smoother process flow, the overall experience for patients is improved.

The innovative integration of extensive patient data analyses with simulation technology has allowed our clients to better understand the needs of their patient populations, as well as served to uncover non-intuitive and surprising ways that patients had been flowing through the system.