Ignite Creation Date:
2024-05-06 @ 7:09 PM
Last Modification Date:
2024-10-26 @ 3:01 PM
Study NCT ID:
NCT05917574
Status:
RECRUITING
Last Update Posted:
2023-12-11
First Post:
2023-05-04
Brief Title:
A Study to Evaluate the Introduction of New Staffing Models in Intensive Care a Realist Evaluation SEISMIC-R
Sponsor:
University of Hertfordshire
Organization:
University of Hertfordshire
Study Overview
Official Title:
A Study to Evaluate the Introduction of New Staffing Models in Intensive Care a Realist Evaluation SEISMIC-R
Status:
RECRUITING
Status Verified Date:
2023-12
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
SEISMIC-R
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 modelsResearch 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 n294 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
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 n294 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
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 11 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
ObjectivesMethods Guided by a realist framework the investigators propose to interlink workstreams WS over 2 years to allow cross-fertilisation of ideashypotheses 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 EnglandWalesNorthern Ireland NIScotland that will identify staffing models emergingretained 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 nursessenior nurses organisational leads critical care network managerscommissioners familiespatients to test emerging programme theories Rapid ethnographies n30 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 patientstaff outcomes vary with differing staff models between unitscase 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
Aim To identify the key components of an optimal nurse staffing model for deployment in ICU
ObjectivesMethods Guided by a realist framework the investigators propose to interlink workstreams WS over 2 years to allow cross-fertilisation of ideashypotheses 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 EnglandWalesNorthern Ireland NIScotland that will identify staffing models emergingretained 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 nursessenior nurses organisational leads critical care network managerscommissioners familiespatients to test emerging programme theories Rapid ethnographies n30 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 patientstaff outcomes vary with differing staff models between unitscase 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None