  Occupational stress among health workers is a pervasive issue that affects
individual well-being, patient care quality, and healthcare systems'
sustainability. Current time-tracking solutions are mostly employer-driven,
neglecting the unique requirements of health workers. In turn, we propose an
open and decentralized worker-centered solution that leverages machine
intelligence for occupational health and safety monitoring. Its robust
technological stack, including blockchain technology and machine learning,
ensures compliance with legal frameworks for data protection and working time
regulations, while a decentralized autonomous organization bolsters distributed
governance. To tackle implementation challenges, we employ a scalable,
interoperable, and modular architecture while engaging diverse stakeholders
through open beta testing and pilot programs. By bridging an unaddressed
technological gap in healthcare, this approach offers a unique opportunity to
incentivize user adoption and align stakeholders' interests. We aim to empower
health workers to take control of their time, valorize their work, and
safeguard their health while enhancing the care of their patients.
