Clinical Trial Details
— Status: Recruiting
Administrative data
NCT number |
NCT05917574 |
Other study ID # |
UH:02990 |
Secondary ID |
|
Status |
Recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
June 14, 2023 |
Est. completion date |
April 30, 2025 |
Study information
Verified date |
December 2023 |
Source |
University of Hertfordshire |
Contact |
Natalie A Pattison |
Phone |
07543220056 |
Email |
n.pattison[@]herts.ac.uk |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
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.
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.