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

Sponsor

University of Hertfordshire (Other)

Overall Status

Not yet recruiting

CT.gov ID

NCT05917574

Collaborator

University of Southampton (Other), Imperial College Healthcare NHS Trust (Other), Intensive Care National Audit & Research Centre (Other), University of Exeter (Other), University of Plymouth (Other), London South Bank University (Other)

Enrollment

22.5

Anticipated Duration (Months)

Study Details

Study Description

Brief 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:

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)
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.
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.

Condition or Disease	Intervention/Treatment	Phase
Critical Illness Staff Attitude Organisation Intensive Care	Other: n/a (non-interventional)

Detailed 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.

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.

Study Design

Study Type:

Observational

Anticipated Enrollment :

80 participants

Observational Model:

Other

Time Perspective:

Other

Official Title:

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

Anticipated Study Start Date :

Jun 14, 2023

Anticipated Primary Completion Date :

Jan 30, 2025

Anticipated Study Completion Date :

Apr 30, 2025

Outcome Measures

Primary Outcome Measures

Death from all causes within 30 days of ICU admission [2019-2023]
Mortality

Secondary Outcome Measures

Discounted Quality-adjusted Life Year (QALYs) [2019-2023]
Quality-adjusted Life Years (QALYs)
Composite death/discharge to long term-care (LTC) [2019-2023]
Death/discharge to LTC
ICU-acquired infection [2019-2023]
ICU-acquired infection
Days of organ support in ICU [2019-2023]
Days of organ support in ICU (per organ)
Cost of ICU stay [2019-2023]
Cost of ICU and post-ICU stay (in hospital)
Staff absence [2019-2023]
Sickness and absence (and associated costs)

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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.