Simulating nursing unit performance with OrgAhead: strengths and challenges.

Area of Science:

• Health Services Research
• Computational Modeling
• Nursing Quality and Safety

Background:

• Computational modeling offers a novel approach to simulating healthcare environments.
• Previous work has established the OrgAhead tool for detailed system simulations.

Purpose of the Study:

• To utilize OrgAhead for simulating nursing units to predict improvements in patient quality and safety.
• To detail the challenges and solutions encountered when applying computational modeling in a real-world healthcare setting.

Main Methods:

• Simulated 39 nursing units across 13 Arizona hospitals using the OrgAhead tool.
• Collected data from diverse sources including managers, staff, patients, and quality services.
• Mapped specific nursing unit data elements (e.g., workload, patient complexity) to OrgAhead parameters and validated virtual units against actual performance metrics (e.g., medication errors, fall rates).

Main Results:

• Achieved acceptable correspondence between simulated and actual nursing units.
• Generated actionable, unit-specific strategies to improve virtual unit performance for patient quality and safety.
• Nurse managers responded positively to the tailored recommendations provided by the model.

Conclusions:

• Computational modeling, specifically with OrgAhead, is a viable method for identifying strategies to enhance patient safety and quality in nursing units.
• Overcoming modeling challenges has improved the functionality and usability of the OrgAhead tool.
• This approach provides unique, unit-specific recommendations that traditional methods cannot offer.