Medecipher offers a web-based software, FLO, that utilizes predictive analytics and optimization algorithms to enhance nurse scheduling by accurately forecasting workloads and intelligently prescribing shift plans. This solution addresses the inefficiencies in manual scheduling processes, resulting in improved patient safety, reduced staffing costs, and significant time savings in schedule management.
Funding
$49.8K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Manual nurse scheduling processes are often inefficient, leading to suboptimal staffing levels that can compromise patient safety, increase staffing costs, and consume significant administrative time. Existing methods struggle to accurately predict fluctuating workloads and adapt to changing labor market conditions, resulting in inflexible and reactive staffing models.
Solution
Medecipher's FLO is a web-based software solution that leverages predictive analytics and optimization algorithms to enhance nurse scheduling. FLO accurately forecasts nursing workloads using historical census data and intelligently prescribes shift plans to maintain appropriate staffing levels at every hour. The system adapts future schedules by incorporating trends and preferences from prior forecasts and shift plans, enabling hospitals to design, evaluate, and implement customized and flexible staffing models. By automating and improving the scheduling process, FLO increases patient safety, boosts schedule flexibility for nursing staff, reduces staffing costs, and ensures compliance with department and organizational rules.
Target Audience
The primary target audience includes hospital administrators, nurse managers, and staffing coordinators responsible for optimizing nurse schedules and managing staffing costs.
Features
- Predictive analytics using hospital's historical census data to forecast nursing workloads.
- Optimization algorithms to prescribe shift plans that maintain appropriate staffing levels.
- Adaptive scheduling incorporates trends and preferences from prior forecasts and shift plans.
- Customizable staffing models that adapt to changing labor landscape.
- Decision support tools to design and evaluate staffing models.
- Automation of manual nurse staff scheduling processes.
- Reporting and analytics to track key performance indicators related to staffing effectiveness.