Product Overview
MHN’s analytics platform turns clinical and operational data into actionable intelligence via dashboards, automated reports, and predictive models. It supports real-time monitoring, program evaluation, and strategic planning.
Analytics Layers & Capabilities
Data ingestion & ETL: Connectors from HMIS, lab systems, pharmacy systems, mobile apps, and external datasets (demographics, weather).
Data warehouse / Lakehouse: Cleaned, standardized clinical and event data with conformed dimensions.
Master data management (MDM): Ensures consistent facility, clinician, and product hierarchies.
Prebuilt dashboards: Clinical KPIs (MCH, immunization coverage), operational dashboards (bed occupancy, supply chain), finance dashboards (revenue, claims), program dashboards (outreach, screening).
Ad-hoc reporting & self-service BI: Drag-and-drop report builders, data exports, scheduled deliveries.
Predictive analytics & ML: Risk stratification (readmission, default from treatment), demand forecasting, anomaly detection for surveillance.
Alerts & workflows: Threshold-based alerts (e.g., rising stockouts, vaccination drop), automated case management tasks.
UX & Visualization
Role-based dashboards tuned for clinicians, facility managers, program managers, and policy makers.
Multi-level drill-down: national → provincial → district → facility → patient cohort.
Mobile-first summary dashboards for field supervisors.
Technical Stack
Ingestion: Kafka/streaming or batch pipelines (Airflow-like orchestration).
Storage: Columnar data warehouse (for OLAP) + object storage for raw data.
Processing: SQL-based transformation layer; Python/R for advanced analytics.
Visualization: Web dashboards with interactive filtering, export to CSV/PDF.
APIs: Programmatic access for external analytics and dashboards.
Data Governance & Quality
Built-in data quality scoring and drift detection.
Lineage tracking to trace KPI values back to source records.
Role-based data access, PII masking for analyst roles.
Use Cases & Benefits
Public health surveillance: Early detection of outbreaks via anomaly detection.
Program optimization: Identify low-coverage areas for immunization outreach.
Clinical quality improvement: Monitor treatment adherence, outcome measures, and clinician performance.
Resource planning: Forecast medicine demand and bed capacity.
KPIs to Monitor Analytics Impact
Time-to-report (hours) for critical indicators.
% of decisions influenced by analytics (qualitative surveys).
Reduction in stockout incidents after forecasting implementation.
Increase in timely reporting rates.