Challenge
From raw consultation events to accurate, timely business intelligence.
Billing-grade accuracy
Institutional clients require precise records of every consultation, FACEM involved, time spent, and outcome. Reporting errors create both revenue leakage and client trust issues.
SLA reporting visibility
Healthcare partners contract MED on specific response time and service level commitments. Reporting needs to be near-real-time, not month-end.
Workforce planning intelligence
Matching FACEM availability to demand patterns across multiple healthcare institutions, time-of-day load, and seasonal variation requires consultation data that aggregates cleanly across dimensions.
Audit-ready data
Healthcare data carries regulatory and clinical quality reporting requirements that demand clear data lineage and the ability to reconstruct any reported number from source events.
From raw consultation events to accurate, timely business intelligence.
Billing-grade accuracy
Institutional clients require precise records of every consultation, FACEM involved, time spent, and outcome. Reporting errors create both revenue leakage and client trust issues.
SLA reporting visibility
Healthcare partners contract MED on specific response time and service level commitments. Reporting needs to be near-real-time, not month-end.
Workforce planning intelligence
Matching FACEM availability to demand patterns across multiple healthcare institutions, time-of-day load, and seasonal variation requires consultation data that aggregates cleanly across dimensions.
Audit-ready data
Healthcare data carries regulatory and clinical quality reporting requirements that demand clear data lineage and the ability to reconstruct any reported number from source events.
Solution
Engineering a healthcare data pipeline built for accuracy, not speed.
1. Azure-based architecture for healthcare-grade reliability
We built the pipeline on Microsoft's Azure data platform, using SQL Server as the operational data store, Azure Data Factory for orchestration, and Azure Functions for event-driven processing. This combination gives MED the reliability characteristics required for healthcare operations data: managed services with high availability, region-locked data residency for Australian healthcare compliance, and audit logging built into the platform.
2. Pipeline design for billing-grade accuracy
Each consultation event flows through validation, enrichment, aggregation, and reporting stages. We designed reconciliation checkpoints at each stage so that any discrepancy between source events and reported figures can be traced quickly. For billing data specifically, every reported amount can be traced back to specific consultation events, FACEM allocations, and time records, which protects MED in client audits and dispute scenarios.
3. Power BI as the business intelligence layer
We built reporting and SLA dashboards in Power BI, integrated directly with the data pipeline. Different stakeholders see different views: MED's operations team sees workforce planning intelligence, finance sees billing-ready reports, account managers see client-specific SLA dashboards, and leadership sees aggregate service health. Each view pulls from the same underlying data, ensuring consistency across roles.
Engineering a healthcare data pipeline built for accuracy, not speed.
1. Azure-based architecture for healthcare-grade reliability
We built the pipeline on Microsoft's Azure data platform, using SQL Server as the operational data store, Azure Data Factory for orchestration, and Azure Functions for event-driven processing. This combination gives MED the reliability characteristics required for healthcare operations data: managed services with high availability, region-locked data residency for Australian healthcare compliance, and audit logging built into the platform.
2. Pipeline design for billing-grade accuracy
Each consultation event flows through validation, enrichment, aggregation, and reporting stages. We designed reconciliation checkpoints at each stage so that any discrepancy between source events and reported figures can be traced quickly. For billing data specifically, every reported amount can be traced back to specific consultation events, FACEM allocations, and time records, which protects MED in client audits and dispute scenarios.
3. Power BI as the business intelligence layer
We built reporting and SLA dashboards in Power BI, integrated directly with the data pipeline. Different stakeholders see different views: MED's operations team sees workforce planning intelligence, finance sees billing-ready reports, account managers see client-specific SLA dashboards, and leadership sees aggregate service health. Each view pulls from the same underlying data, ensuring consistency across roles.
Results
The data pipeline replaces what would otherwise be manual reporting, scattered spreadsheets, and reconciliation work that scales poorly with consultation volume. With the pipeline in place, MED's operations team works from a single source of truth for billing, SLA performance, and workforce intelligence. Billing-grade accuracy means institutional clients receive reports they can audit and pay against. Near-real-time SLA visibility means service issues surface quickly rather than at month-end. And operational analytics give MED's leadership the data foundation to plan FACEM capacity as the service continues to grow.
MED operates at significant scale and continues to expand its service across Australian healthcare. The organization has fielded more than 250,000 consultations since 2016, partners with hospitals, ambulance services, aged care facilities, and primary health networks across the country, and has been recognized as one of Australia's most innovative health companies multiple years running, including a Top 3 placement on the AFR BOSS Most Innovative Health Companies list. The data pipeline Adamo built is part of the operational infrastructure supporting this growth, focused specifically on the back-office layer where consultation events become billing records, SLA evidence, and workforce intelligence.
The data pipeline replaces what would otherwise be manual reporting, scattered spreadsheets, and reconciliation work that scales poorly with consultation volume. With the pipeline in place, MED's operations team works from a single source of truth for billing, SLA performance, and workforce intelligence. Billing-grade accuracy means institutional clients receive reports they can audit and pay against. Near-real-time SLA visibility means service issues surface quickly rather than at month-end. And operational analytics give MED's leadership the data foundation to plan FACEM capacity as the service continues to grow.
MED operates at significant scale and continues to expand its service across Australian healthcare. The organization has fielded more than 250,000 consultations since 2016, partners with hospitals, ambulance services, aged care facilities, and primary health networks across the country, and has been recognized as one of Australia's most innovative health companies multiple years running, including a Top 3 placement on the AFR BOSS Most Innovative Health Companies list. The data pipeline Adamo built is part of the operational infrastructure supporting this growth, focused specifically on the back-office layer where consultation events become billing records, SLA evidence, and workforce intelligence.