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01
Predictive Insights
AI algorithm predicts shift-level staffing requirements 1-3 weeks in the future.
In-House’s proprietary Workload Score factors in patient-specific work requirements and adjusts unit predictions based on average patient status per-shift.
Workload prediction further adjusted for nurse capacity, such as average tenure.
02
Recommendation Engine
In-House combines predictive census and current staffing in summary dashboard.
Option to make changes manually or automatically suggest updates via In-House’s AI engine
All recommendations provided with easy override: user is final authority on team well-being & patient safety
03
Team Management
Nurse preferences, experience by unit, training and certifications tracked and automatically reflected in schedule recommendations.
Push out swaps, adds and other requests via mobile app or text message.
Why In-House Health?
AI-driven Scheduling and Management Platform for Modern Nursing Teams
Make each shift more efficiently tailored to clinical need. In-House takes less than an hour to learn, after which nurse managers start saving hours a week through our automations.
In-House demystifies scheduling processes and decision-making, which are often opaque to direct care nurses, and supports nurse managers to build fair, reliable schedules. Grounding scheduling decisions in patient need is important.
The #1 objection to static nurse-to-patient ratios is that they oversimplify both patient needs and nurse experience- and we agree. We trained our algorithm using machine learning on 100,000s of historical cases from triage through discharge.
Turning to internal staff first to fill incremental shifts, but knowing how much to pay in a more flexible staffing environment is still largely guesswork. In-House’s platform knows how difficult each shift is to fill, as well as the criticality based on clinical workload.