Ciyfr is building an AI-powered workforce intelligence platform designed to help hospitals predict and prevent nurse turnover before it happens.
Over the past several months, we’ve conducted extensive customer discovery with nursing leaders, hospital operators, and healthcare researchers to understand the operational signals that precede nurse departures. We’ve interviewed leaders and experts across major healthcare systems and universities to validate both the problem and the data sources required to solve it.
Through these conversations, we identified that hospitals already collect most of the workforce data needed to detect early instability signals such as scheduling patterns, overtime trends, staffing ratios, and operational workload metrics but that this data is rarely analyzed in a predictive way.
Ciyfr aggregates these workforce signals and uses machine learning models to detect patterns associated with nurse attrition risk and unit-level staffing instability.
We have already completed an alpha pilot validating the availability and structure of the workforce data required to power the platform, confirming that hospitals can export the necessary operational datasets without requiring complex system integrations.
The long-term vision is to create a workforce digital twin for hospitals, allowing leaders to simulate staffing decisions, forecast attrition risk, and proactively stabilize their nursing workforce.