In a recent process improvement project, I was required to
turn 6 months of arrival information from an emergency room into entries for a
simulation model. This is difficult because there is variability from week to
week, Mondays are different from Fridays and there is significant variation throughout
the day.
A simulation model was used for this process improvement
project because process mapping and flowcharting was not helping the staff to
visualize the source of the problem. The major problem was identified as
patient stays in excess of 4 hours but with work content of less than 40
minutes. This means 17 percent of the patient stay was for work that mattered
to the care of the patient and the rest of the time was frustration, delay and
waste.
A key in making the simulation model representative of the
real system is that the model arrivals must reflect the actual patient
arrivals. The finished simulation model included some additional activities
used only to help in the creation of representative arrivals. Below is shown
the process model elements used to describe the arrivals:
The process of converting the raw data into a processsimulation is relatively straight forward once the steps are understood. The
process management system for the hospital contained the check-in date, time
and acuity for all patients treated. A representation of a small portion of the
data is shown below:
First, I prepared to analyze quantities per week, percent
arriving in the day of week and hours transpired from midnight on the day of
arrival to the time of the arrival. An example of the added analysis fields is
shown below:
Second, I created a pivot table for the weekly arrivals.
Arrivals were summed by week number.
The “count of week” column was copied into a Data fitting
program provided with ProcessModel simulation software titled Stat::Fit. Stat::Fit turns raw data into distributions used
in process improvement projects. Stat::fit is very easy to use and I have
tested the resulting distributions, finding they provide a reliable
representation of the variability of the data. Every time a weekly arrival
occurs in the simulation software, ProcessModel samples the distribution and
picks a quantity according to the probability of the distribution. The fitted
distribution is shown below:
The resulting distribution, output to the simulation
software, represents not only the data analyzed, but “what could happen” based
on laws of statistical analysis. That distribution was entered into a periodic
arrival in ProcessModel. An example of the periodic arrival is show below:
The above arrival creates the quantities for each week. In
the second part of this report I will show how to spread arrivals over the
proper day of week and then to the hour of the specific day.
Process simulation has been invaluable in helping to analyze
the complexity of the Emergency department. It has helped me to make certain
the decisions for process improvement are defined by not only data, but also
the combination of all the types of data creating a more accurate picture of
the real system.
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