Nurse-built workforce scheduling platform for health systems
M7 Health operates a scheduling and staffing platform built by nurses for frontline healthcare teams. The tech stack (React, TypeScript, Node.js, PostgreSQL, AWS) is lean and modern, optimized for real-time visibility and automation. Current project work centers on AI-enhanced scheduling intelligence and hospital operations features, while internal challenges reflect the sector's core pressures: nurse shortages, labor cost control, and overtime reduction. The hiring mix—weighted toward senior engineering and support roles—suggests focus on product stabilization and customer success rather than rapid headcount scaling.
M7 Health builds a dynamic workforce management platform for large health systems and hospitals. The product integrates scheduling automation, real-time visibility, and staff preference data to improve fairness and reduce administrative overhead. Organizations using M7 report 60%+ reductions in scheduling administrative time, up to 30% lower premium labor spend, and staff fairness scores above 94%. For large health systems, these operational gains translate into more than $20 million in annual labor savings while improving retention and satisfaction. The platform complements existing timekeeping and HR systems and delivers measurable results within 90 days. M7 is based in New York and operates as a privately held company with 11–50 employees.
M7 Health builds on React, TypeScript, Node.js, NestJS, PostgreSQL, and AWS. Frontend tooling includes Vite; backend uses TypeORM for data access. Sales and customer operations run on HubSpot.
M7 Health has 2 active engineering roles posted, part of an 8-role hiring slate across engineering, ops, support, product, and sales. Hiring velocity is decelerating. All roles are based in the United States.
Current projects include AI-enhanced scheduling intelligence, hospital operations features, design system expansion, and sales automation. Internal priorities center on reducing overtime, lowering contract labor costs, and operationalizing an AI-first operating model.
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