Background and Analysis

Background

Workload in German hospitals regularly exceeds legal limits in many areas.[1] Medical and nursing staff systematically work overtime, often without complete documentation.[2][3]

The consequences are well documented:

• Elevated burnout rates and attrition of skilled workers[4][5]
• Increased risk of medical errors – including preventable ones[6]
• Longer wait times and reduced quality of care for patients[7]

These conditions are widely known. Yet structural change remains absent.

Analysis

Hospitals operate under considerable economic pressure.[8][9] A common response is to reduce staffing costs, leading to chronic understaffing.[8][10]

In functioning systems, corrective mechanisms exist for such failures:

• Market mechanisms (where choices exist): Patients and skilled workers choose other institutions, creating economic pressure for change.
• Legal mechanisms: Violations of working time regulations[1][11] or liability cases for treatment errors[6] lead to sanctions that alter decision-makers' calculations.

However, both mechanisms require that the underlying problems are visible and demonstrable.

Here lies the central bottleneck: Actual workload is often not fully, independently, and comparably documented.[2][3][11]

• Without reliable data, there is no basis for evidence.
• Without evidence, no pressure for action.
• Without pressure, no correction.

Approach

Open Working Hours addresses this bottleneck directly: creating systematic transparency without putting individuals at risk.

Workers can record their working hours on their own device and contribute anonymously to aggregated statistics. No personal data is published – only aggregated metrics at the institutional level, released only when a minimum number of contributors is reached.

The principle:

• Protect individuals: Workers should not face additional disadvantages or risks for documenting overwork.
• Make institutional responsibility visible: Responsibility for working conditions should rest with decision-makers who determine staffing and organizational structures.

The resulting data can serve as evidence:

• In collective bargaining
• In health policy debates
• Where necessary: in legal proceedings