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Ciyfr, Inc.

AI platform predicting nurse turnover using hospital workforce data to help leaders stabilize turnover.
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https://ciyfr.io
About this idea
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.
Impact
Hospitals across the United States are facing a severe workforce stability crisis. Nurse turnover alone can cost hospitals more than $10 million annually across all nursing roles, and it often takes up to 90 days to backfill a single nurse position. During that time, hospitals frequently rely on agency and travel nurses to maintain safe staffing levels. These temporary staff can cost up to three times more than full-time nurses, placing enormous financial pressure on already strained healthcare systems. As a result, roughly 73% of hospitals are actively looking for ways to reduce their reliance on agency and travel nurses while stabilizing their permanent workforce. Ciyfr aims to address this challenge by helping hospitals move from reactive staffing decisions to proactive workforce intelligence. By analyzing operational workforce data such as scheduling patterns, overtime trends, staffing ratios, and workload signals Ciyfr can detect early indicators of workforce instability and identify nursing units that may be at risk of losing staff. This gives hospital leaders the ability to intervene earlier with retention strategies, staffing adjustments, or operational changes before turnover occurs. Even small improvements can have a meaningful financial impact. Reducing nurse turnover by just a few percentage points can save hospitals hundreds of thousands of dollars each year while strengthening care teams and improving workforce stability. Ultimately, Ciyfr aims to help hospitals build more resilient nursing teams, reduce costly staffing disruptions, and maintain high-quality patient care in an industry that depends on a stable and supported workforce.
What I'll do with $5,000
Ask $5K To Launch Ciyfr’s First Hospital Pilot Funding Unlocks Pilot-Ready Workforce Data Engine Executive ROI Simulation Dashboard Deployment Preparation for First Hospital Pilot
Quick Bio
Madala Mathurin is the Founder & CEO of Ciyfr, an AI platform helping hospitals predict and reduce nurse turnover. He was the second iOS engineer at Rocket Homes and previously worked at IFTTT in SF.
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