case study

Optimizing Nurse Scheduling with Predictive AI

Healthcare

Business Impacts

Enhanced visibility into staff workload

Improved operational efficiency and patient care

Reduced nurse burnout, dissatisfaction, and turnover

Data-driven, cost-effective staffing optimization

Customer Key Facts

  • Country : United States
  • Size : 36000
  • Industry : Hospitals and Health Care
  • About : The client is a large nonprofit healthcare system in the Midwest, operating multiple hospitals.

Alleviating nurse workload by streamlining nurse assignments workflow

The client previously utilized an Excel-based tool for workload distribution, requiring manual checking of workload data from dashboards for hospital units, resulting in a slow and cumbersome process.

Challenges

  • Nurse dissatisfaction and stress: High turnover is caused by work-related stress and dissatisfaction among nurses.
  • Inefficient staffing model: Traditional staffing methods lead to inefficiency and uneven workload distribution.
  • Poor workload visibility: Lack of measurement and visualization of workload hinders workflow efficiency.
  • Skill mismatch: Limited data usage prevents effective skill matching for resource allocation.

Technologies

BigQuery

BigQuery

Cloud SQL

Cloud SQL

Kubernetes Engine

Kubernetes Engine

Azure AD

Azure AD

Cloud storage

Cloud storage

Redis

Redis

Solution

  • Quantiphi enhanced the client's nurse assignment workflow by replacing their slow, manual process with a modern, data-driven, AI-powered nurse scheduling tool. This tool aids charge nurses in distributing patients more fairly and efficiently.
  • The tool performs ingestion and harmonization of source data from four distict source systems including standardization of over 10 million HL7v2 messages.
  • The Web UI of the tool aggregates all necessary data, creates visualizations, and facilitates informed scheduling decisions, matching patient workload with nurse demand.

Results

  • Optimized resource allocation for peak efficiency is provided by our AI-powered nurse scheduling tool.
  • Improved agility in staffing is achieved through a data-driven and dynamic approach that enhances speed and accuracy over time according to demand.
  • Real-time data empowers frontline workers with informed decisions, skill-aligned resource placement, and consolidated inputs on common screens for streamlined operations.
  • Elevated patient care through fair workload distribution, staffing alignment, and enhanced nurse satisfaction.
  • Greater value realization is achieved through accurate information centralized in one source, easing labor challenges and enhancing operational efficiency.

"A new nurse on a hospital unit was overwhelmed due to a significantly higher patient load compared to her colleagues. The implementation of the AI-powered nurse scheduling tool identified this disparity during her shift, allowing for adjustments to be made. The nurse's subsequent statement, "It made my voice heard, and my shift manageable," underscores the value of the tool in addressing workload inequities and improving nurse well-being."

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