A Study to Evaluate the Introduction of New Staffing Models in Intensive Care: a Realist Evaluation (SEISMIC-R)

Background: Optimising deployment of the scarce nursing workforce in the intensive care unit (ICU) is paramount for patient safety, and staff wellbeing. ICU staffing models are determined by National Health Service (NHS) service specification, with 1:1 patient to registered nurse (RN) ratios for the highest acuity patients. A rapid expansion of ICU capacity during COVID19 led to adoption of alternative models, using more support staff, non-ICU qualified nurses and other professionals, reaching up to 70% at surge. The strengths, weaknesses, costs and effects of these models, and benefits of retaining them, remain uncertain. Lower nurse-staffing levels, and high workload, have been associated with adverse outcomes for patients, staff and organisations although ICU-specific evidence is limited. Studies focus on levels of RNs, contributing little to understanding consequences of changes retained post-COVID, or to guiding adoption of alternative staffing models. It is unclear how changes in staffing or specific models affect various outcomes.

Aim: To identify the key components of an optimal nurse staffing model for deployment in ICU.

Objectives/Methods: Guided by a realist framework, the investigators propose to interlink workstreams (WS) over 2 years to allow cross-fertilisation of ideas/hypotheses and inform emerging programme theories.

• To identify and describe organisation of models, exploring intended mechanisms and outcomes for how different models work, the investigators will conduct:

• a UK survey (WS 1) of all 294 ICUs in England/Wales/Northern Ireland (NI)/Scotland that will identify staffing models emerging/retained since COVID19, compared with United Kingdom (UK) service specifications.
• a realist evaluation (WS 2, cross-cutting workstream) and detailed case studies involving six sites, and 30-40 interviews with: nurses/senior nurses; organisational leads; critical care network managers/commissioners; families/patients, to test emerging programme theories. Rapid ethnographies (n=30), will elucidate how staffing decisions are made.
• To provide estimates of variability in demand for nursing staff and estimate associations between staffing patterns and patient outcomes, the investigators will:

• use administrative e-roster (nurse staffing roster) data and patient data (WS 3) from the Intensive Care National Audit and Research Centre Case Mix Programme (2019-2023) to assess whether and how patient/staff outcomes vary with differing staff models between units/case study sites.
• To develop simulation models to show the impact of models on capacity, cost and patient flow, the investigators will use simulation modelling (WS 4) to explore scenarios for different staffing policies given case mixes of case study units, swiftly and with no patient impact.

Analysis: Data integration occurs across all workstreams in WS 5. Theories developed from WS2 case studies will be further tested against WS 3 observational data and inform WS 4 mathematical simulation models of ICU capacity, patient outcomes and patient flow, to inform emerging propositions for the realist evaluation programme theories as context-mechanism-outcome configurations.

Inclusion criteria

Interviews with nursing staff

• Registered Nurses who have worked in ICU for at least six months. Inclusion criteria: Interviews with nurse managers
• Clinically-based nurse managers who have worked in ICU for at least one year. Inclusion criteria: Interviews with organisational leads
• Organisational leads who have been working in their role and in the ICU field for at least one year.

Inclusion criteria

Interviews with regional managers/commissioners • Regional managers/commissioners who have been working in their role and in the ICU field for at least one year.

Inclusion criteria

Interviews with patients/families

• Patient or family member over 18 years old.
• Patients who have been in General ICU for at least 48 hours in the last 6 months.
• Family members who have visited ICU for at least 20 mins on two days in the preceding 6 months.
• Patient discharged from hospital at least 2 weeks prior to the interview.
• Patient expected to be well enough, after hospital discharge, to attend the interview and to have capacity to consent.

Exclusion criteria

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