Automatically analyzes raw arrival data, creates distributions and imports the restructured data into ProcessModel. You provide historical data for months of arrivals. This model object:
- Analyzes the data.
- Shows a graphical summary of raw data.
- Finds the arrival quantities for each day of the week.
- Removes outliers.
- Shows data needing conversion to a distribution.
- Creates the daily distribution to spread arrivals over the hour of the day.
- Automatically imports the data into ProcessModel.
Where to Find the Model Object
The following model object will be used to create daily pattern arrivals for healthcare and import data into ProcessModel:
- Arrivals \ Daily Pattern Arrivals.
Difficulty Level
- Ease of Use: Easy
- Ease of Modification: Easy
How to Use the Model Object
- Open Your Model: Open your model or create a new blank model.
- Insert the Model Object into Your Model: Locate the appropriate location in your model where you would like to place the model object, move the view to the location and then insert the model object.
- Connect to Your Model: Connect the route exiting from activity Acuity to the rest of your model.
- Open the Excel File: Right click on the Daily Pattern Arrivals Sheet Excel icon and click on the third option from the bottom to open the Daily Pattern Arrivals v-x-x Excel file, where x-x represents the version number of the Excel file.
Before using this Excel file for the first time, or after changing your model name, make sure that a manual export of data is done via ProcessModel. From your model, click Tools \ Export Data, click ‘Yes’ to all prompts until the data export file opens, close the data file and then use this Excel file.
Click Enable Content if prompted
- Enter Raw Data: After opening the Daily Pattern Arrivals vx-x Excel file, the Input worksheet appears.
- Copy your date and time data and paste into cell A3. You need at least 6 months of data. Use the Excel date and time format, sample data is included.
- Copy your acuity / priority levels and paste them into cell B3. Enter the number that is considered the highest acuity level in your system. You must provide a value between 1 to 9, based on what the highest acuity is in your data. Where 1 is the highest acuity.If you do not have acuity data you can leave the column empty.
- Enter any holidays into cell C3. The holidays are optional and are only used to help you review data before import.
Notes:
- Sample data must be removed prior to entry of your data.
- You must provide at-least 24 weeks worth of data
- Copy your date and time data and paste into cell A3. You need at least 6 months of data. Use the Excel date and time format, sample data is included.
- Prepare Arrival Data for Processmodel: Click the button titled Prepare Arrival Data for ProcessModel and allow 1 minute for the analysis (depends on the speed of your processor and the size of the file). Once completed you will land on the Daily Pattern Daily Qtys worksheet and should see upto three new worksheets in the same Excel file.
- Daily Pattern Daily Qtys
- Daily Pattern Hourly Qty
- Daily Pattern Acuity
- Review Your Data – Analyzing the Daily Pattern Daily Qtys
The Daily Pattern Daily Qtys worksheet is divided into three parts.- Raw Daily Arrivals: The first section of the Daily Pattern Daily Qtys is the Raw Daily Arrivals, you can review the raw data and see how the outliers change the way data is presented. The Whisker chart on the right helps review the outliers in more detail with the dots (•) representing any outliers. If you have identified “dates of holidays” in the input data, the green bars (shown on the sheet below) identify potential holiday related surges.
- Raw Daily Arrivals: The first section of the Daily Pattern Daily Qtys is the Raw Daily Arrivals, you can review the raw data and see how the outliers change the way data is presented. The Whisker chart on the right helps review the outliers in more detail with the dots (•) representing any outliers. If you have identified “dates of holidays” in the input data, the green bars (shown on the sheet below) identify potential holiday related surges.