Big Data, better hospitals
Years: |
|
Strands: |
|
Proficiencies: |
|
Overcrowding in hospitals is one of the biggest challenges facing our healthcare systems. In order to reduce hospital waiting times, the Patient Admission Prediction Tool (PAPT) uses historical data to predict how many patients are expected to arrive at the Emergency Department every day of the year.
The PAPT is formulated by collecting and using vast data-sets about patient admissions and discharges. It is used by hospitals to manage
their staff and physical resources. How accurate is it? Should all hospitals be using the PAPT?
Teacher notes
|
The teacher notes contain: an overview of each of the activities; curriculum links and suggested year levels; background information; prompting questions and key mathematical points; practical suggestions for running the activity; a list of resources needed; and further ideas. |
|
Activity 1: Errors and the powers of percentages
|
Students use real data to compare the daily forecast with the actual number of people who came to the Emergency Department of a hospital, to evaluate the effectiveness of the PAPT. They will gain insight into the way in which a prediction tool can be evaluated. Students calculate absolute, relative and percentage errors, and compare the usefulness of the measures. |
|
Activity 2: What Happened?
|
Students arrange themselves in height order to establish a firm understanding of the median and interquartile range, in preparation for constructing boxplots. They analyse the number of competition points scored by each of the 18 teams in the 2016 Australian Football League regular season. Students then analyse and compare four data sets from the PAPT. They attempt to explain apparent anomalies, and are led to the correct conclusion through a series of hints. |
|
Data set |
|
|
The data come from the Emergency Department in a Gold Coast hospital showing admissions over a single year. |
|
Entry forms |
|
|
You can turn the answer into a guessing competition with these entry forms. |
|
Number lines 60-120 |
|
|
Number lines with appropriate scales. |
|
Number lines blank |
|
|
Number lines with tick-marks but no scale. |
|
Activity 3: Difficult to easy
|
Students are exposed to a variety of contemporary graphical representations of data. They examine representations of the more complex data from the PAPT to assist in their interpretation. Using a simple but relevant context, students construct their own heat-maps and 3D graphs, using Excel. |
|
Which graph is best? |
|
|
The first slides of the PowerPoint ‘Which graph is best?’ show six different ways of using Excel to graphically represent the data. |
|
Heat map data set |
|
|
The Heat-map spreadsheet contains data on the number of admissions for a large Queensland hospital in the month of March. The admissions are recorded hourly using 24-hour clock time. |
|
How to create a heat map in Excel |
|
|
A heat-map only has colours, highlighting the patterns which are related to the underlying data. This download gives further instructions on how to create a heat-map in Excel. |
|
Activity 4: Waiting, waiting
|
Students identify common queueing situations and the factors that cause a queue to occur. They simulate simple queueing situations using concrete materials and then use spreadsheets for more complex modelling. Students change the factors to explore their effects. They then apply their knowledge to a hospital queue. |
|
Hourly admissions in July |
|
|
This file contains the data on the number of admissions, by hour, in the month of July. It is organised so that each student can receive one full day of data. |
|
Exploring queues |
|
|
A table template to assist in recording the behaviour in queues. |
|
Spinners |
|
|
Four-sided spinner templates.. |
|