Staff Burnout Doesn't Come Out of Nowhere: Proof of Concept for an Early Warning Dashboard

The problem: Few sectors have higher sickness rates than health and social care: shift work, emotional strain, chronic understaffing. Managers sense that a team is tipping, but they only see it in the numbers once the resignation is on the table or the long-term absence has begun. Satisfaction scores, sick days, and turnover live in separate systems and are never looked at together.

My approach: An interactive Power BI dashboard as a proof of concept, bringing together publicly available data and making the link visible while there is still time to act:

1. Data integration: Job satisfaction and sickness rate statistics at regional (state) and sector level, with a year filter, brought together in an interactive dashboard.
2. Correlation analysis: The connection becomes visible instead of felt: declining satisfaction measurably goes hand in hand with rising sick days.
3. Early warning instead of post-mortem: Sector benchmarks show where teams or departments deviate from the norm, anonymized and GDPR-compliant, without monitoring individual employees.

The benefit: The proof of concept shows that the link between satisfaction and sickness rates can be made visible with reasonable effort. For an individual facility, the next step would be feeding in its own (anonymized) data to turn this into an objective basis for staffing decisions and health measures, instead of guessing where the fire is. For the anonymized collection of this data, I already bring the technical foundation: with Ertoba Analytics, I built my own platform for encrypted psychological assessments (AES-256-GCM encryption, row-level security). Building on that, I develop a tailored survey solution for each facility that genuinely and anonymously asks staff about their satisfaction, instead of relying only on public sector data. As a psychologist (B.Sc.), I deliver not just the technology but also the interpretation: which metrics actually indicate overload, and which measures follow from them. One thing matters to me here: care through data only works with strict anonymization and staff acceptance. Otherwise prevention becomes surveillance.