Project Overview

The SA Ambulance Service Callouts Dashboard is a Power BI visualisation tool that aggregates approx 185,000 pager messages sent to the South Australian Ambulance Service (SAAS) over a five-month period (August 1st, 2024 – February 28th, 2025). This dashboard integrates geographic and population data to offer a state-wide overview of emergency callout patterns, providing timely and localised insights that would not otherwise be available.

The Dashboard

Target Users & Audience

This dashboard serves a wide range of stakeholders, including:

• Governmental Agencies - for resource allocation and emergency response planning.
• Local Governments - to assess healthcare demands and infrastructure needs.
• Insurance Companies & Health Providers - for risk assessment and policy adjustments.
• General Public - for awareness of emergency response trends in their local community.

Business Value & Real-World Impact

1. Enhanced Decision-Making & Operational Efficiency

• Timely Insights: Unlike traditional health reports that rely on aggregated and delayed data, this dashboard provides near real-time visibility into emergency trends.
• Geographic & Population-Based Analysis: By mapping suburb-level callout data to local government areas (LGAs), the dashboard integrates population statistics, allowing for per capita comparisons and deeper analysis.
• Trend Identification: Health agencies can detect patterns in ambulance callouts, helping to anticipate healthcare service demands.

2. Automation & Efficiency Gains

• Automated Data Collection & Transformation: A data pipeline scrapes a live-feed of pager messages daily, cleaning and transforming them into structured tabular formats.
• Dynamic Filtering & Visualisation: Users can drill down by region, time period, or callout type, enabling quick insights without manual data manipulation.
• Elimination of Data Silos: This model centralises disparate datasets (pager messages, callout codes, station codes, geographic boundaries and population data) into a single source of truth.

3. Differentiation from Existing Solutions

• Comparative Analytics: The integration of local government and population data enables per capita comparisons, making it easier to identify areas with disproportionately high emergency response demands.
• Scalability & Long-Term Value: Unlike static reports, this dashboard continues to evolve with ongoing data collection, making historical and predictive trend analysis possible.

Technical Implementation

Core Technologies & Methods

• Text Parsing & Data Structuring: Converts unstructured pager messages into structured datasets, making them queryable for analytics.
• Snowflake Schema Data Modeling: Optimised for handling large-scale temporal and geographic data within Power BI.
• Advanced Power BI Measures: Enables time-series analysis, anomaly detection, spatial and temporal heatmaps.

Challenges & Solutions

• Data Sparsity & Early Noise Issues: Initially, limited data volume made trend detection difficult. However, after around three months (~80,000 messages), meaningful patterns had begun to stabilise.
• Handling Unstructured Text Data: Developed a custom parsing pipeline to extract priority, suburb, dispatched unit, timestamps, and incident types from pager messages.

Deployment & Scalability Considerations

• Deployed as a Power BI Dashboard – accessible via Power BI Service for secure sharing and collaboration.
• Scalable Data Model – supports ongoing data ingestion, allowing for extended historical analysis.

Future Applications & Expansions

• Long-Term Data Collection: Extending to a 12-month dataset would allow for year-over-year comparisons, helping policymakers assess seasonal trends.
• Real-Time Anomaly Detection: Increasing the data collection frequency (e.g., every 15 minutes) could enable real-time alerts for healthcare providers.
  • Example: “Anomalous trend in breathing problems reported in XYZ City Council in the past hour.”
• Integration with Predictive Analytics & AI: Incorporating machine learning models could enhance forecasting capabilities, potentially allowing hospitals to anticipate ambulance surges before they occur.