Cohort: 2 

Locations: Iki City, Japan & Kanagawa, Japan 

Application Title: AI agent systems for hospital operations, bed management and patient flow optimization 

Team Members: Yuki Doi, Ito Momoka, Takamichi Mitsutake 

The challenge 

Japan’s healthcare system faces structural pressure. A majority of hospitals operate at a financial deficit, while staff shortages continue to grow due to demographic change. These pressures are especially acute in regional and rural areas, where hospitals must maintain high occupancy to remain viable. 

Hospital management in Japan is also shaped by complex regulatory rules. Medical fee reimbursements depend on detailed documentation and strict compliance with government standards. Errors or omissions can lead to fee refunds, creating financial risk. At the same time, bed allocation requires constant adjustment to balance patient needs with operational constraints. Much of this work is handled manually, relying on experienced staff and disconnected systems. 

These conditions make it difficult to improve efficiency without increasing workload. 

The solution 

AI agents embedded into hospital management workflows 

Social Medical Corporation Genshukai partnered with Fujitsu to apply AI to hospital operations where complexity is highest. 

Rather than deploying isolated tools, Fujitsu introduced a set of AI agents designed to work within existing hospital processes. Each agent supports a specific function, such as checking compliance with medical fee rules, assisting with document preparation, or supporting bed allocation decisions. The agents operate together, covering the patient journey from pre-admission through discharge and coordination with referral facilities. 

Genshukai integrated this approach into its operating model, allowing staff to use AI support as part of daily work rather than as a separate system. 

How it works 

Supporting decisions that staff already make 

Medical billing rules in Japan are detailed and frequently updated. One of the AI agents continuously references official fee schedules and notifications when records are created. It highlights missing requirements and suggests corrective actions, helping reduce compliance-related errors. 

Another agent supports bed allocation. Using mathematical optimisation, it evaluates placement options based on capacity and patient needs. This assists staff in maintaining occupancy without manual recalculation. Additional agents support discharge planning and coordination with external facilities by displaying relevant conditions and availability. 

Each agent focuses on a defined task. An orchestration layer combines their outputs so staff receive clear guidance rather than fragmented recommendations. 

How it differs from conventional approaches 

Reducing reliance on manual coordination 

Traditional hospital management systems in Japan address billing, bed control and referrals separately. Staff must reconcile information across tools and apply complex rules from memory or paper guidance. 

The Fujitsu–Genshukai approach embeds rule interpretation and optimisation directly into workflows. AI agents support decisions as they are made, rather than reviewing outcomes after the fact. This reduces dependence on individual expertise and lowers the risk of error without removing human oversight. 

Because Japan’s healthcare system operates under a common national framework, the same agent-based approach can be applied across institutions without redesigning core systems. 

Impact 

• Approximately 10% in annual revenue uplift from improved operations 
• Monthly medical fee refunds reduced from $7,000 to zero through compliance support 
• Around 400 staff hours per month freed from manual regulatory and bed-control tasks 
• Active use by hundreds of nurses, administrators and care staff 



Why it matters 

Supporting sustainable hospital operations 

The collaboration between Fujitsu and Genshukai shows how AI agents can support hospital management by reducing administrative burden and improving consistency in complex processes. 

By embedding AI into everyday workflows, the approach helps hospitals operate more reliably under financial and staffing constraints. This becomes increasingly important as healthcare systems seek ways to sustain services without adding pressure to frontline staff.