How can we optimise nurse staffing systems? Insights from a comparative document analysis of 10 widely used models...

A diverse range of formal systems have been implemented in high income countries to ensure safe nurse staffing. Evidence reviews indicate that no one best model exists and recommends optimising existing systems. As a result of the Covid-19 pandemic and a global nursing workforce crisis, healthcare systems and the nursing profession face a challenging future. Nurse staffing systems must be fit for purpose.

Identify, describe and compare the core components of nurse staffing systems, assess the conditioning effects of context on their mechanisms of action, and explore front-line implementation experiences to inform system optimisation.

Ten widely used nurse staffing systems deployed in high-income western healthcare systems.

Complex interventions thinking and Actor Network Theory.

Phase 1: Document analysis of formal published accounts of nurse staffing systems. Phase 2: Focused interpretative review of evidential fragments on implementation experiences and contextual influences from available evaluation studies.

Systems varied in their complexity, core components, and organising logics. Nurses experience a range of implementation challenges, but workforce shortages and budgetary constraints were the principal contextual influences. Prospective strategies to optimise nurse staffing systems must be tailored to system and context but include strategies and tools to augment professional authority, more granular workload measurement, improved outcome measurements, strengthened digital infrastructures, enhanced governance arrangements and increased public accountability. Benchmarking approaches should be used with caution, given the normative impulse to depress staffing levels. In the context of a global workforce shortage, consideration should also be given to the impacts of nurse staffing models on the wider healthcare system.

How can we optimise safe nurse staffing systems? Insights from a document analysis and interpretative review informed by actor network theory.