Emergency Department Did Not Waits

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Extending the ED Simplest Waiting Room Model

We will now build on our previous model. So far we have assumed people waiting to be ED patients are very patient, but in reality, some don’t wait around for treatment. And the longer the expected wait the more people leave without being seen. We add an additional outflow to the waiting room stock named exit untreated and we collect and count these in a stock called Do not Waits. We regulate this outflow via waiting time and did not wait fraction converters. These are connected in the following way.

Waiting time (hours) = (Patients waiting /Patients per hour) as an equation

Fig. 1 - ED Waiting Room with Did Not Waits Model [Source]
Fig. 2 - Simulation Run Results [Source]

Virtual What if experiments

Discuss the sensitivity to waiting time and the shape of curve of the wait time effect on exits converter. Can you get agreement on this behavioural response? How might you confirm or improve the relationship within the model and in real experiments? How would you model changing expectations or changing perceptions ?

Relevance of this effect

People who do not wait may suffer additional harm if their condition is worse than they think it is. People leaving might be seen as a good thing for overworked staff. And it is, but only if the right patients leave. But what if the people who could most benefit from early treatment leave. What happens to them? How are they measured? How might they be followed up? This is technically called errors of omission with overcrowding.

Also we can improve the treatment rate by more staff or less time, but doesn’t reduce the number of people waiting by much, only number of patients treated. The did not waits may leave before or after being registered at the ED front desk, particularly when the place is the busiest. This makes it more difficult to get accurate figures for Do not Waits. Because of this, some improvements could unexpectedly not improve waits but show up as increased numbers of patients assessed and treated with perhaps little additional contribution to improving workload.

Other Discussion Points

  • Manipulating the queue so perceived wait seems less. stalling pts fish tank, updates on progress of the queue, or two waiting stages one in waiting room another in another space- The Disneyland solution.
  • Chronically overloaded EDs sometimes have signs posting wait times of several hours to manage expectations (Photo of ED sign), and perhaps to encourage do not waits even when not busy. This is a form of demand management, which assumes (rightly or wrongly) that those who really need emergency department care will wait as long as it takes to get it, and those who leave were not really ill. If the did not wait fraction is high, then this assumption is well worth testing. How might you do this?
  • Some quantitative modelers avoid including soft variables like the table function of the estimate of the impact of waiting time on do not wait fraction. We believe that the gain of putting it in approximately, outweighs the losses of leaving out exactly.

Extension

  • As an Exercise, enhance the model by allocating staff by hour of day

Extending the Theory of ED Did Not Waits

Questions & Comments to Geoff McDonnell
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