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

Critical Illness Clinical Trial

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

Status Recruiting

Clinical Trial Summary

Background: Staffing in intensive care units (ICU) has been in the spotlight since the pandemic. Having enough nurses to deliver safe, quality care in ICU is important. However, what the skill mix should be (how many should be qualified nurses or have an ICU qualification) is unclear. Very little research has been done to look at which nursing staff combinations and mix of skills works best in ICU to support patients (described as 'staffing models').Research shows that there is a link between the quality of nurse staffing and poor patient outcomes, including deaths.

Aim: Our research plans to look at different staffing models across the UK. This study aims to examine new staffing models in ICU across six very different Trusts. This study will use a research technique called Realist Evaluation that examines what works best in different situations and help to understand why some things work for some people and not others. The design of this approach will help to better understand the use of different staff ratios across different ICU settings.

This study will examine what combinations of staff numbers and skills result in better patient care and improved survival rates. The aim is to produce a template that every ICU unit can use. To do this, this study will compare staffing levels with how well patients recover, and seek to understand the decisions behind staffing combinations.

Methods: This study will:

1. carry out a national survey to understand the different staff models being used, comparing this against the current national standard (n=294 ICUs in the UK including Scotland)

2. observe how people at work in 6 hospitals (called ethnography), watching how they make decisions around staffing and the effect on patients. The investigators will also conduct interviews (30 interviews plus 30 ethnographic observations) to understand staffing decisions.

3. look at ICU staffing patterns and models, and linked patient outcomes (such as whether people survive ICU) over 3 years (2019-2023) in those hospitals, including with a very different combination of staffing). The investigators will then carry out some mathematical calculations to understand the best possible staffing combinations, and how this varies.

Clinical Trial Description

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.

1. 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.

2. 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.

3. 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. ;

Related Conditions & MeSH terms

• Critical Illness
• Intensive Care
• Staff Attitude

• NCT number: NCT05917574
• Study type: Observational
• Source: University of Hertfordshire
• Contact: Natalie A Pattison
• Phone: 07543220056
• Email: n.pattison@herts.ac.uk
• Status: Recruiting
• Start date: June 14, 2023
• Completion date: April 30, 2025