The purpose of this tool was to yield a more accurate understanding of the distribution of people with risk factors for Type 2 Diabetes (ie Existing prevalence of Type 2 Diabetes, Depression, Obesity, Smoking) and Health Services General Practice which has bulk billing, Fee only, after hours service etc.).
This is a exploratory tool that allows the user to test different risk factors and health service locations. For example areas with a SEIFA score of less than 4 and a Diabetes prevalence less than 5 people per 100 and walking distance to a fee paying GP with an after hour hours services. To achieve this the interface was developed first as a prototype tested with users and then developed further into an online interactive tool. To assist users the tool includes a series of slider bars and tick boxes which enable the user to select parameters. Once the selection is finalised the “run” button allows the user to run the analysis.
Broader model considerations
The system has been designed to enable users to interact with the data and test a variety of exploratory options. The tool also enables the overlay of the input data so that users are able to visualise each of the input parameters
Figure 2: Example output areas based on input parameters
Figure 3. Example exploration of SEIFA data (one of the inputs along with GP locations (red dots).
Outputs include: a visual representation of the areas which have the higher risk factors (based on the input parameters) along with an overlay of the input layers which enable the users to question and explore the risk factors and service locations spatially.
Because of the open-source nature of the current data, theoretically the health demonstrator can be used across most built environments internationally.
A online access to the health demonstrator is available here: http://18.104.22.168:9999/health-demonstrator/ It is also referenced via the AURIN Blog post here: https://docs.aurin.org.au/the-north-west-melbourne-project
Who are the Users and the Testers?
The tool was initially developed and tested with a working groups comprising people from academia, government as well as The Inner North West Primary Care Partnership, Inner North West Medicare Local, Australian Bureau of Statistics, Department of Health, North East Primary Care Partnership and the Hume Whittlesea Primary Care Partnership. (Refer to previous Blog Posts in the health series).
Testing Methods and Findings
As per the remit of the overall project, a stakeholder working group and industry- and research-based project champions were established to guide the development and testing of the tool through the development phases. The project champions for the tool Ms Sandy Austin and Professor Jane Gunn were both involved in reviewing the initial prototype.
At the conclusion of the project a workshop was held to scenario test the tool. This process involved providing end users exercise which involved using the health demonstrator tool to locate a new GP clinic in the area along with a co-located specialist diabetes outreach and additional mental health and social support service. The users were then asked to provide feedback on the performance of the tool. This exercise enabled the users to become familiar with the tool and to test how it might be translated into a real life example. Feedback from the users was also collected and used to improve the tool (for example extending the tool to include spatial overlays of the input data).
This testing process also enabled the users to test the tool with multiple concurrent users (over 8 computers accessing the tool at one time) to which is performance was relatively fast. Based on early testing a processing bar has been added to the tool to enable users to understand the potential time required to run each different exploratory excercise. Figure 4 is an image taken at the testing workshop and illustrates the level of interaction users are able to have with the tool
Findings and Lessons Learned
This tool was designed to enable people without a spatial background to access spatial data in a very fast and intuitive way. However at the same time the resolution of the data was highly aggregated to SLA and LGA boundaries which made the process of detailed spatial analysis difficult. The other main difficulty was the dependence of the tool on accurate data to GP locations, whilst the medicare locals supplied the data, there is a concern that if this data is not maintained it may become dated.