Slovenia Healthcare Authority
Healthcare decision-makers must balance medicine availability, consumption, and cost efficiency. This project analyzed national-level medicine pricing and consumption data in Slovenia to support transparency and data-driven planning.
Challenge
Required clear visibility into medicine consumption trends over time, identification of high-cost medicines with disproportionate impact, and a way to communicate insights effectively to non-technical stakeholders. Existing reporting was fragmented and time-consuming.
The client required clear visibility into medicine consumption trends over time and needed to identify high-cost medicines with disproportionate impact on healthcare budgets. They also needed a way to communicate complex insights effectively to non-technical stakeholders including policymakers and procurement teams. Existing reporting was fragmented across multiple systems and required significant manual effort, making it time-consuming and prone to errors.
Solution
Combined spreadsheet analysis, Python-based data processing, and Power BI dashboards for comprehensive medicine analytics
We combined spreadsheet analysis, Python-based data processing, and Power BI dashboards to create a comprehensive analytics solution. The approach involved cleaning and structuring multi-year medicine data, analyzing pricing, consumption volume, and defined daily dose (DDD) metrics. We identified cost drivers and category-level patterns, then designed interactive dashboards that made complex health data accessible to decision-makers without requiring technical expertise.
Key Features
- Multi-year trend analysis
- Cost driver identification
- Category-level pattern detection
- Interactive Power BI dashboards
- DDD (Defined Daily Dose) metrics
- Seasonal trend detection
Implementation Approach
- Cleaned and structured multi-year medicine data from national sources.
- Analyzed pricing, consumption volume, and defined daily dose (DDD) metrics.
- Identified cost drivers and category-level patterns across medicine types.
- Designed interactive Power BI dashboards for exploration and reporting.
- Enabled non-technical stakeholders to access insights independently.
Impact
- Identified high-consumption medicines with outsized cost impact, highlighting opportunities for cost optimization.
- Revealed steady growth in total prescription value, despite relatively stable consumption patterns.
- Detected category-specific and seasonal usage trends, supporting better forecasting and planning.
Timeline
- Data Collection1 week
- Data Processing3 weeks
- Analysis & Modeling3 weeks
- Dashboard Development2 weeks