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

NCT05917574 | Completed

• 1 other identifier: UH:02990
• Study Type: observational
• Target: 87
• Locations: 1 country
• Sites: 1

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:

1. 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. 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. 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.

Conditions

Critical Illness; Staff Attitude; Organisation; Intensive Care

Keywords

Staffing; Patient outcomes; intensive care; critical care; healthcare organisation; nurse; realist evaluation

Outcome Measures

Primary Outcomes (1)

• Death from all causes within 30 days of ICU admission

  Mortality

  2019-2023

Secondary Outcomes (6)

• Discounted Quality-adjusted Life Year (QALYs)

  2019-2023

• Composite death/discharge to long term-care (LTC)

  2019-2023

• ICU-acquired infection

  2019-2023

• Days of organ support in ICU

  2019-2023

• Cost of ICU stay

  2019-2023

Interventions

n/a (non-interventional) OTHER

Non-interventional (Realist Evaluation study)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Staff population: Workstream 1 and Workstream 2 sample is expected to be 294 (assuming 100% ICUs respond) plus 30 interviews and 30 ethnographic observation participants Critical care patients population (de-identified data, no individual consent/sampling needed, using routine aggregated dataset): Workstream 3/4 the data set is likely to exceed 29300 Intensive care admissions

You may qualify if:

• Organisational leads who have been working in their role and in the ICU field for at least one year.
• 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.

Sponsors & Collaborators

• University of Hertfordshire: lead
• University of Southampton: collaborator
• Imperial College Healthcare NHS Trust: collaborator
• Intensive Care National Audit & Research Centre: collaborator
• University of Exeter: collaborator
• University of Plymouth: collaborator
• London South Bank University: collaborator

Study Sites (1)

East and North Hertfordshire NHS Trust, Lister Hospital

Stevenage, United Kingdom

Location

Related Publications (1)

• Hadley R, Dogan B, Wood N, Bohnacker N, Mouncey PR, Pattison N; SEISMIC-R investigator group. National survey evaluating the introduction of new and alternative staffing models in intensive care (SEISMIC-R) in the UK. BMJ Open. 2025 Apr 10;15(4):e088233. doi: 10.1136/bmjopen-2024-088233.

  PMID: 40216433 DERIVED

MeSH Terms

Conditions

Critical Illness

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Officials

• Natalie A Pattison

  University of Hertfordshire

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 4, 2023
• First Posted: June 26, 2023
• Study Start: June 14, 2023
• Primary Completion: September 30, 2025
• Study Completion: September 30, 2025
• Last Updated: May 4, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)