nb_4_full_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ncHDTAo6n8kE"
      },
      "source": [
        "# Set Parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, let's change the parameters for our simulation using the sliders below - or, leave them with their default values and move on to the next step."
      ],
      "metadata": {
        "id": "2cFD7VNrRqdu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NkQjhFL4n8kF"
      },
      "outputs": [],
      "source": [
        "# @title Model Parameters {display-mode: \"form\"}\n",
        "# General Details\n",
        "\n",
        "# @markdown ---\n",
        "# @markdown ### Overall Emergency Department Settings\n",
        "# @markdown #### Set the number of triage bays\n",
        "n_triage = 3 # @param {type:\"slider\", min:1, max:10, step:1}\n",
        "# @markdown #### Set the probability that a client arriving is a trauma patient (0 = no-one is a trauma patient, 1 = everyone is a trauma patient)\n",
        "prob_trauma = 0.3 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "\n",
        "###################################\n",
        "\n",
        "\n",
        "# @markdown ---\n",
        "# @markdown ### Trauma Pathway Settings\n",
        "# @markdown #### Set the number of trauma stabilisation cubicles\n",
        "n_trauma = 6 # @param {type:\"slider\", min:1, max:10, step:1}\n",
        "# @markdown #### Set the number of trauma treatment cubicles\n",
        "n_cubicles_2 = 6 # @param {type:\"slider\", min:1, max:10, step:1}\n",
        "\n",
        "###################################\n",
        "\n",
        "\n",
        "# @markdown ---\n",
        "# @markdown ### Non-trauma pathway settings\n",
        "# @markdown #### Set the number of non-trauma registration bays\n",
        "n_reg = 3 # @param {type:\"slider\", min:1, max:10, step:1}\n",
        "# @markdown #### Set the number of non-trauma examination rooms\n",
        "n_exam = 7 # @param {type:\"slider\", min:1, max:10, step:1}\n",
        "# @markdown #### Set the number of non-trauma treatment cubicles\n",
        "n_cubicles_1 = 2  # @param {type:\"slider\", min:1, max:10, step:1} # Probability that a generated non-trauma patient is a trauma patient (set between 0 and 1). DEFAULT: 0.7\n",
        "# @markdown #### Set the probability that a client arriving is a trauma patient (0 = no-one on the non-trauma pathway needs treatment, 1 = everyone on the non-trauma pathway needs treatment)\n",
        "non_trauma_treat_p = 0.7 # @param {type:\"slider\", min:0, max:1, step:0.01}\n",
        "\n",
        "###################################\n",
        "\n",
        "\n",
        "# @markdown ---\n",
        "# @markdown ### Model Settings\n",
        "# @markdown WARNING: Adjusting the n_reps and run_time days upwards will make the model take significantly longer to run.\n",
        "# @markdown #### Set the random seed\n",
        "seed = 42 # @param {type:\"integer\"}\n",
        "# @markdown #### Set the number of model replications\n",
        "n_reps = 3 # @param {type:\"slider\", min:0, max:10, step:1}\n",
        "# @markdown #### Set the number of simulated days the model will run for\n",
        "run_time_days = 5 # @param {type:\"slider\", min:0, max:30, step:1}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fRo284GXn8kE"
      },
      "source": [
        "\n",
        "\n",
        "Once you have made any changes you want to the values of the sliders above, click 'runtime' in the menu bar at the top of the screen, and then 'run all'.\n",
        "\n",
        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)\n",
        "\n",
        "Alternatively, you can use the keyboard shortcut CTRL + F9 to run all of the code.\n",
        "\n",
        "It will take about 90 seconds for all of the outputs to update. While the code is running, you will see a circular 'stop' button with a bar rotating around the outside. This means something is processing.\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "If something is waiting to run, it will look like this:\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "When the code has finished running, it will change back to this:\n",
        "\n",
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACsAAAApCAYAAACsldDLAAAABHNCSVQICAgIfAhkiAAAABl0RVh0U29mdHdhcmUAZ25vbWUtc2NyZWVuc2hvdO8Dvz4AAAAqdEVYdENyZWF0aW9uIFRpbWUAVHVlIDEyIERlYyAyMDIzIDExOjMxOjIzIEdNVDVkaT4AAAGnSURBVFiF7Zcxj8IgFMf/Xm7sXna+hEl32R3t4MbS0aSbDnXjI3TTwe+As3yM7rDX2VvO5K5VW6DcxYTfSHiPXwK8B7P5fH7Dm/Dx3wI2RNlQRNlQRNlQRNlQvJXsp08wYwxZliHLsl/jSilcLhecz2cvuS4zl7cBpRRVVYEQ8nKe1hq73Q5N0zgL/sT6GDDGUNf1oCgAEEJQ1zUYY05yXaxkKaUoy9J6kbIsQSm1jutiJbvf73tjWutR21xVlc1SDxktyxhDmqa9ca01OOcQQqBt26fxhBDv4zBatnvju0gpkec5jsejc44hJpMFgLZtcTgckOc5lFJOOV4RpCncS9Zms5msbAGBO9jtNu33zquDPSNNUxRF4b3tXUbLKqUGF0+SBMvlEuv1+mkOHyaTXSwWKIoCSZK8zOGD1dvgdDr1aq3WGtfrdbBDGWOwWq3cLL+xumDb7bY3RggZ1UofxdpiJds0DYQQ1osIISYpYdalS0oJzjmMMYNzjTHgnENK6STXxek9e+fV41spNZnkHS/Zv+at/mBRNhRRNhRRNhRRNhRRNhRfdkyQnl2/LrcAAAAASUVORK5CYII=)\n",
        "\n",
        "Give it a little while to load, then scroll down and take a look at the animated event log and the graphs showing the utilisation and wait metrics for our simulated emergency department."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5Wxf8MyRn8kC"
      },
      "outputs": [],
      "source": [
        "# @title Setup {display-mode: \"form\"}\n",
        "!git clone https://github.com/hsma-programme/Teaching_DES_Concepts_Streamlit.git\n",
        "%pip install simpy\n",
        "\n",
        "import gc\n",
        "import warnings\n",
        "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
        "import pandas as pd\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from IPython.display import Markdown as md\n",
        "\n",
        "%cd /content/Teaching_DES_Concepts_Streamlit\n",
        "from model_classes import Scenario, multiple_replications\n",
        "from output_animation_functions import reshape_for_animations, animate_activity_log\n",
        "pd.options.mode.chained_assignment = None  # default='warn'\n",
        "print(\"Setup steps complete\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Total Rooms/Cubicles Allocated\n",
        "\n",
        "md(\"Total rooms in use is {}\".format(n_cubicles_1+n_cubicles_2+n_exam+n_trauma+n_triage+n_reg))"
      ],
      "metadata": {
        "cellView": "form",
        "id": "q0W01EzZVBi8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_XgF0YbKn8kF",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Animated Log\n",
        "args = Scenario(\n",
        "        random_number_set=seed,\n",
        "                 n_triage=n_triage,\n",
        "                 n_reg=n_reg,\n",
        "                 n_exam=n_exam,\n",
        "                 n_trauma=n_trauma,\n",
        "                 n_cubicles_1=n_cubicles_1,\n",
        "                 n_cubicles_2=n_cubicles_2,\n",
        "                 non_trauma_treat_p=non_trauma_treat_p,\n",
        "                 prob_trauma=prob_trauma)\n",
        "\n",
        "detailed_outputs = multiple_replications(\n",
        "                args,\n",
        "                n_reps=n_reps,\n",
        "                rc_period=run_time_days*60*24,\n",
        "                return_detailed_logs=True\n",
        "            )\n",
        "\n",
        "\n",
        "results = pd.concat([detailed_outputs[i]['results']['summary_df'].assign(rep= i+1)\n",
        "                            for i in range(n_reps)]).set_index('rep')\n",
        "\n",
        "full_event_log = pd.concat([detailed_outputs[i]['results']['full_event_log'].assign(rep= i+1)\n",
        "                            for i in range(n_reps)])\n",
        "\n",
        "del detailed_outputs\n",
        "gc.collect()\n",
        "\n",
        "animation_dfs_log = reshape_for_animations(\n",
        "    full_event_log=full_event_log[\n",
        "        (full_event_log['rep']==1) &\n",
        "        ((full_event_log['event_type']=='queue') | (full_event_log['event_type']=='resource_use')  | (full_event_log['event_type']=='arrival_departure')) &\n",
        "            # Limit to first 5 days\n",
        "        (full_event_log['time'] <= 60*24*5)\n",
        "    ],\n",
        "    every_x_minutes=5\n",
        ")['full_patient_df']\n",
        "\n",
        "del full_event_log\n",
        "gc.collect()\n",
        "\n",
        "event_position_df = pd.DataFrame([\n",
        "                # {'event': 'arrival', 'x':  10, 'y': 250, 'label': \"Arrival\" },\n",
        "\n",
        "                # Triage - minor and trauma\n",
        "                {'event': 'triage_wait_begins',\n",
        "                 'x':  160, 'y': 400, 'label': \"Waiting for<br>Triage\"  },\n",
        "                {'event': 'triage_begins',\n",
        "                 'x':  160, 'y': 315, 'resource':'n_triage', 'label': \"Being Triaged\" },\n",
        "\n",
        "                # Minors (non-trauma) pathway\n",
        "                {'event': 'MINORS_registration_wait_begins',\n",
        "                 'x':  300, 'y': 145, 'label': \"Waiting for<br>Registration\"  },\n",
        "                {'event': 'MINORS_registration_begins',\n",
        "                 'x':  300, 'y': 85, 'resource':'n_reg', 'label':'Being<br>Registered'  },\n",
        "\n",
        "                {'event': 'MINORS_examination_wait_begins',\n",
        "                 'x':  465, 'y': 145, 'label': \"Waiting for<br>Examination\"  },\n",
        "                {'event': 'MINORS_examination_begins',\n",
        "                 'x':  465, 'y': 85, 'resource':'n_exam', 'label': \"Being<br>Examined\" },\n",
        "\n",
        "                {'event': 'MINORS_treatment_wait_begins',\n",
        "                 'x':  630, 'y': 145, 'label': \"Waiting for<br>Treatment\"  },\n",
        "                {'event': 'MINORS_treatment_begins',\n",
        "                 'x':  630, 'y': 85, 'resource':'n_cubicles_1', 'label': \"Being<br>Treated\" },\n",
        "\n",
        "                # Trauma pathway\n",
        "                {'event': 'TRAUMA_stabilisation_wait_begins',\n",
        "                 'x': 300, 'y': 560, 'label': \"Waiting for<br>Stabilisation\" },\n",
        "                {'event': 'TRAUMA_stabilisation_begins',\n",
        "                 'x': 300, 'y': 500, 'resource':'n_trauma', 'label': \"Being<br>Stabilised\" },\n",
        "\n",
        "                {'event': 'TRAUMA_treatment_wait_begins',\n",
        "                 'x': 630, 'y': 560, 'label': \"Waiting for<br>Treatment\" },\n",
        "                {'event': 'TRAUMA_treatment_begins',\n",
        "                 'x': 630, 'y': 500, 'resource':'n_cubicles_2', 'label': \"Being<br>Treated\" },\n",
        "\n",
        "                 {'event': 'exit',\n",
        "                 'x':  670, 'y': 330, 'label': \"Exit\"}\n",
        "            ])\n",
        "\n",
        "animated_plot = animate_activity_log(\n",
        "                    full_patient_df=animation_dfs_log[animation_dfs_log[\"minute\"]<=60*24*5],\n",
        "                    event_position_df = event_position_df,\n",
        "                    scenario=args,\n",
        "                    include_play_button=True,\n",
        "                    return_df_only=False,\n",
        "                    plotly_height=900,\n",
        "                    plotly_width=1600,\n",
        "                    override_x_max=700,\n",
        "                    override_y_max=675,\n",
        "                    icon_and_text_size=24,\n",
        "                    display_stage_labels=False,\n",
        "                    wrap_queues_at=10,\n",
        "                    time_display_units=\"dhm\",\n",
        "                    # show_animated_clock=True,\n",
        "                    # animated_clock_coordinates = [100, 50],\n",
        "                    add_background_image=\"https://raw.githubusercontent.com/hsma-programme/Teaching_DES_Concepts_Streamlit/main/resources/Full%20Model%20Background%20Image%20-%20Horizontal%20Layout.drawio.png\",\n",
        "            )\n",
        "\n",
        "del animation_dfs_log\n",
        "gc.collect()\n",
        "\n",
        "animated_plot.update_layout(\n",
        "    plot_bgcolor='black'\n",
        ")\n",
        "\n",
        "animated_plot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBUwqP-Qn8kG"
      },
      "source": [
        "## Simple Metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j5-HCarDn8kG",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Utilisation In Range\n",
        "\n",
        "in_range_util = sum((results.mean().filter(like=\"util\")<0.85) & (results.mean().filter(like=\"util\") > 0.65))\n",
        "in_range_wait = sum((results.mean().filter(like=\"wait\")<120))\n",
        "\n",
        "\n",
        "md(\"**Utilisation Metrics in Ideal Range**: {} of {}\".format(in_range_util, len(results.mean().filter(like=\"util\"))))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mEVHQIKwn8kG"
      },
      "source": [
        "The emergency department wants to aim for an average of 65% to 85% utilisation across all resources in the emergency department.\n",
        "\n",
        "The green box shows this ideal range. If the bars overlap with the green box, utilisation is ideal.\n",
        "\n",
        "If utilisation is below this, you might want to **reduce** the number of those resources available.\n",
        "\n",
        "If utilisation is above this point, you may want to **increase** the number of that type of resource available."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jv72nX2-n8kH",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Utilisation Graph\n",
        "\n",
        "util_fig_simple = go.Figure()\n",
        "# Add optimum range\n",
        "util_fig_simple.add_hrect(y0=0.65, y1=0.85,\n",
        "                         fillcolor=\"#5DFDA0\", opacity=0.25,  line_width=0)\n",
        "# Add extreme range (above)\n",
        "util_fig_simple.add_hrect(y0=0.85, y1=1,\n",
        "                         fillcolor=\"#D45E5E\", opacity=0.25, line_width=0)\n",
        "# Add suboptimum range (below)\n",
        "util_fig_simple.add_hrect(y0=0.4, y1=0.65,\n",
        "                         fillcolor=\"#FDD049\", opacity=0.25, line_width=0)\n",
        "# Add extreme range (below)\n",
        "util_fig_simple.add_hrect(y0=0, y1=0.4,\n",
        "                         fillcolor=\"#D45E5E\", opacity=0.25, line_width=0)\n",
        "\n",
        "util_fig_simple.add_bar(x=results.mean().filter(like=\"util\").index.tolist(),\n",
        "                    y=results.mean().filter(like=\"util\").tolist())\n",
        "\n",
        "util_fig_simple.update_layout(yaxis_tickformat = '.0%')\n",
        "util_fig_simple.update_yaxes(title_text='Resource Utilisation (%)',\n",
        "                         range=[-0.05, 1.1])\n",
        "# util_fig_simple.data = util_fig_simple.data[::-1]\n",
        "util_fig_simple.update_xaxes(labelalias={\n",
        "\"01b_triage_util\": \"Triage<br>Bays\",\n",
        "\"02b_registration_util\": \"Registration<br>Cubicles\",\n",
        "\"03b_examination_util\": \"Examination<br>Bays\",\n",
        "\"04b_treatment_util(non_trauma)\": \"Treatment<br>Bays<br>(non-trauma)\",\n",
        "\"06b_trauma_util\": \"Stabilisation<br>Bays\",\n",
        "\"07b_treatment_util(trauma)\": \"Treatment<br>Bays<br>(trauma)\"\n",
        "}, tickangle=0)\n",
        "\n",
        "util_fig_simple"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y7Rb400bz9TE"
      },
      "outputs": [],
      "source": [
        "# @title Waits In Range {display-mode: \"form\"}\n",
        "md(\"**Wait Metrics in Ideal Range**: {} of {}\".format(in_range_wait, len(results.mean().filter(like=\"wait\"))))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VixJQCMkz9rQ"
      },
      "source": [
        "The emergency department wants to ensure people wait no longer than 2 hours (120 minutes) at any point in the process.\n",
        "                    \n",
        "This needs to be balanced with the utilisation graphs on the left.\n",
        "\n",
        "The green box shows waits of less than two hours. If the bars fall within this range, the number of resources does not need to be changed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Spiuc7tpz99m",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Wait Graph\n",
        "\n",
        "wait_fig_simple = go.Figure()\n",
        "wait_fig_simple.add_hrect(y0=0, y1=60*2, fillcolor=\"#5DFDA0\",\n",
        "                          opacity=0.3, line_width=0)\n",
        "\n",
        "wait_fig_simple.add_bar(x=results.mean().filter(like=\"wait\").index.tolist(),\n",
        "                        y=results.mean().filter(like=\"wait\").tolist())\n",
        "\n",
        "wait_fig_simple.update_xaxes(labelalias={\n",
        "    \"01a_triage_wait\": \"Triage\",\n",
        "    \"02a_registration_wait\": \"Registration\",\n",
        "    \"03a_examination_wait\": \"Examination\",\n",
        "    \"04a_treatment_wait(non_trauma)\": \"Treatment<br>(non-trauma)\",\n",
        "    \"06a_trauma_wait\": \"Stabilisation\",\n",
        "    \"07a_treatment_wait(trauma)\": \"Treatment<br>(trauma)\"\n",
        "}, tickangle=0)\n",
        "# wait_fig_simple.data = wait_fig_simple.data[::-1]\n",
        "wait_fig_simple.update_yaxes(title_text='Wait for Treatment Stage (Minutes)')\n",
        "\n",
        "wait_fig_simple"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kbY11K1d0luJ"
      },
      "source": [
        "# Advanced Graphs\n",
        "\n",
        "We can use box plots to explore the effect of the random variation within each model run.\n",
        "                        \n",
        "This can give us a better idea of how robust the system is.\n",
        "\n",
        "Each dot indicates a single model run. The number of runs can be increased under the advanced options."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wr-5rycV0jKY"
      },
      "source": [
        "## Average utilisation\n",
        "\n",
        "The emergency department wants to aim for an average of 65% to 85% utilisation across all resources in the emergency department.\n",
        "                    \n",
        "The green box shows this ideal range. If the bars overlap with the green box, utilisation is ideal.\n",
        "\n",
        "If utilisation is below this, you might want to **reduce** the number of those resources available.\n",
        "\n",
        "If utilisation is above this point, you may want to **increase** the number of that type of resource available."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iWlGO0rc0jRd",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Utilisation Boxplot\n",
        "\n",
        "utilisation_boxplot = px.box(\n",
        "                    results.reset_index().melt(id_vars=\"rep\").set_index('variable').filter(like=\"util\", axis=0).reset_index(),\n",
        "                    y=\"variable\",\n",
        "                    x=\"value\",\n",
        "                    points=\"all\",\n",
        "                    range_x=[0, 1])\n",
        "\n",
        "utilisation_boxplot.add_vrect(x0=0.65, x1=0.85,\n",
        "                          fillcolor=\"#5DFDA0\", opacity=0.25,  line_width=0)\n",
        "# Add extreme range (above)\n",
        "utilisation_boxplot.add_vrect(x0=0.85, x1=1,\n",
        "                          fillcolor=\"#D45E5E\", opacity=0.25, line_width=0)\n",
        "# Add suboptimum range (below)\n",
        "utilisation_boxplot.add_vrect(x0=0.4, x1=0.65,\n",
        "                          fillcolor=\"#FDD049\", opacity=0.25, line_width=0)\n",
        "# Add extreme range (below)\n",
        "utilisation_boxplot.add_vrect(x0=0, x1=0.4,\n",
        "                          fillcolor=\"#D45E5E\", opacity=0.25, line_width=0)\n",
        "\n",
        "utilisation_boxplot.update_yaxes(labelalias={\n",
        "    \"01b_triage_util\": \"Triage<br>Bays\",\n",
        "    \"02b_registration_util\": \"Registration<br>Cubicles\",\n",
        "    \"03b_examination_util\": \"Examination<br>Bays\",\n",
        "    \"04b_treatment_util(non_trauma)\": \"Treatment<br>Bays<br>(non-trauma)\",\n",
        "    \"06b_trauma_util\": \"Stabilisation<br>Bays\",\n",
        "    \"07b_treatment_util(trauma)\": \"Treatment<br>Bays<br>(trauma)\"\n",
        "}, tickangle=0, title_text='')\n",
        "\n",
        "utilisation_boxplot.update_xaxes(title_text='Resource Utilisation (%)',\n",
        "                range=[-0.05, 1.1])\n",
        "\n",
        "utilisation_boxplot.update_layout(xaxis_tickformat = '.0%')\n",
        "\n",
        "utilisation_boxplot"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZQ2JH9i0_cZ"
      },
      "source": [
        "## Average Waits\n",
        "\n",
        "The emergency department wants to ensure people wait no longer than 2 hours (120 minutes) at any point in the process.\n",
        "                    \n",
        "This needs to be balanced with the utilisation graphs on the left.\n",
        "\n",
        "The green box shows waits of less than two hours. If the bars fall within this range, the number of resources does not need to be changed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4Arwemg_1F5d",
        "cellView": "form"
      },
      "outputs": [],
      "source": [
        "# @title Wait Boxplot\n",
        "\n",
        "wait_boxplot = px.box(\n",
        "                    results.reset_index().melt(id_vars=\"rep\").set_index('variable')\n",
        "                    .filter(like=\"wait\", axis=0).reset_index(),\n",
        "                    y=\"variable\",\n",
        "                    x=\"value\",\n",
        "                    points=\"all\")\n",
        "\n",
        "wait_boxplot.update_yaxes(labelalias={\n",
        "    \"01a_triage_wait\": \"Triage\",\n",
        "    \"02a_registration_wait\": \"Registration\",\n",
        "    \"03a_examination_wait\": \"Examination\",\n",
        "    \"04a_treatment_wait(non_trauma)\": \"Treatment<br>(non-trauma)\",\n",
        "    \"06a_trauma_wait\": \"Stabilisation\",\n",
        "    \"07a_treatment_wait(trauma)\": \"Treatment<br>(trauma)\"\n",
        "}, tickangle=0, title_text='')\n",
        "\n",
        "wait_boxplot.add_vrect(x0=0, x1=60*2, fillcolor=\"#5DFDA0\",\n",
        "                          opacity=0.3, line_width=0)\n",
        "\n",
        "wait_boxplot.update_xaxes(title_text='Wait for Treatment Stage (Minutes)')\n",
        "\n",
        "wait_boxplot"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}