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Best Practices for Modeling Patients Who Leave Early Without Being Seen (LWBS)

Modeling LWBS patients is tricky business and can cause exception errors under certain circumstances if not done correctly. The problems usually arise due to patients who exit the model early while there are pending requests queued up in the model for future activities on the patient. The attached model demonstrates the current best practice for safely modeling LWBS patients without the possibility of generating unwanted errors.

The modeling technique is very simply. Use the On Entry trigger of the waiting room object(s) to send a delayed message to itself in X minutes, where X is a sample time from an "impatience curve" representing the amount of time a typical patient is willing to wait before they strongly consider leaving. In the On Message trigger of the waiting room object(s), I have written a code snippet that you will want to copy and modify to suit your own modeling requirement. The code snippet in the example model checks to make sure the patient is still in the waiting room waiting for an exam room at the end of the "impatience time" when the message trigger fires. Then I roll the dice using a bernoulli distribution to decide whether or not to have the patient actually leave. The code snippet shows a different probability for each of two patient types (PCI's) and then a default probability of 50 percent for anyone else.

Not only does this modeling approach avoid undesirable exception errors, but it is also more accurate and definitely more efficient than using the Quick Properties fields in the Patient Condition panel of a waiting room which requires repetitive function calls every so many minutes throughout the model run!

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Cliff King avatar image Cliff King ♦ commented ·

Lou Keller (@Lou Keller) has pointed out to me that in some cases it may not make sense to "roll the dice" at the end of the impatience time period. Lou tells me that the distribution I used in the sample model (i.e. weibull(90.4,2.08,73.3)) was derived from a study conducted by a hospital in Illinois who amassed 251 data points over a period of one calendar year that reflected approximately how long each of the sampled patients was in the ER Waiting Room before leaving without being seen. Lou points out that when using that particular Weibull distribution to model the "impatience time" in your model, you should either not roll the dice at the end of the period, or set the probability to 100%. Here's an image showing the stats associated with the Weibull distribution Lou gave me:

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weibulldist.png (41.5 KiB)



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