Clinical trial NCT06923943: Predicting Nurse Staffing Requirements From Routinely Collected Data
Official title: Predicting Nurse Staffing Requirements From Routinely Collected Data
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Nursing Workload
Keywords: nurse staffing
workload
prediction
workforce
safety
Lead sponsor: University of Southampton
Sponsor class: OTHER
Collaborators: name: Imperial College London
name: Imperial College Healthcare NHS Trust
name: Portsmouth Hospitals NHS Trust
name: NHS England
First posted: 2025-04-11
Enrollment: 80
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: English NHS acute hospital Trusts

Brief summary: The goal of this observational study is to find out if the researchers can predict the number of nurses needed on hospital wards (units) from patient hospital data. The main question it aims to answer is:

Is it possible to predict nurse staffing requirements from routinely recorded data in hospital systems?

Researchers will ask nurses about their views of nurse staffing tools and what support they need for staffing decisions. They will analyse data from hospital IT systems.

Detailed description: Background: Having enough nurses on hospital wards is vital for patient safety but planning for varying numbers and needs of patients is hard. Almost all acute NHS Trusts in England use the NICE-endorsed Safer Nursing Care Tool (SNCT) to guide staffing decisions. However, this approach is labour-intensive and necessitates the collection of data specifically to measure staffing requirements, not informed by data gathered for administration or care management.

Aim: Develop a method to measure demand for nursing staff on hospital wards using routine data to help plan establishments (number of ward employees), monitor staffing adequacy in real-time, and inform safe and efficient deployment of staff.

Design: A retrospective observational study across wards providing acute adult somatic (i.e. not mental health) inpatient care in 5 general hospital Trusts, predicting nurse staffing requirements from routinely collected data and validating these predictions against patient and staffing adequacy outcomes. Algorithms will be developed according to user-centred design and by engaging with patients to understand experiences of hospital nurse staffing and implications for developing algorithms.

Workstream (WS) 1 Objective: understand what does/does not work for nurses and managers when using staffing tools, and incorporate this into algorithm design. Method: User-centred design approach comprising i) a national survey of staffing matrons and Chief Nursing Information Officers to find out how staffing tools are used and patient data availability/quality, ii) workshops with nurses and nursing managers to understand staffing decision support needs at different timepoints, iii) workshops with this group plus NHS IT managers and roster companies to discuss algorithm design considerations.

WS2 Objective: develop statistical/machine learning algorithms to estimate nurse staffing requirements from routinely available patient data. Method: Since there is no "gold standard" for measuring nurse staffing requirements, researchers will first replicate measurements from the SNCT, a patient acuity/dependency classification tool. They will develop alternative algorithms including replicating individual patient acuity/dependency classifications and replicating the staffing requirements for a whole ward. They will consider staffing decisions at different timepoints. Predictor variables will come from administrative and care plan data.

WS3 Objective: assess the validity of algorithms. Method: Researchers will fit regression models to investigate the associations between actual under/over-staffing relative to each candidate measure of staffing requirements and multiple outcomes. For this, they will use routine data extracted from hospital IT systems and a micro-survey of nurses to understand perceptions of staffing adequacy. They will test whether as staffing increases relative to a measure of staffing requirements, the risk of poor patient outcomes and perceptions that staffing is inadequate decreases. They will compare model fit against models with staffing requirements measured by the SNCT.

Eligibility criteria: National survey Inclusion Criteria:

* safe staffing lead/nurse with responsibility for safe staffing or CNIO/nurse with responsibility for IT/electronic records

Workshops Inclusion Criteria:

* nursing manager with safe staffing remit/IT remit. OR
* clinical nurse with experience of completing Safer Nursing Care Tool ratings. OR
* NHS IT manager with familiarity of hospital Trust's systems for storing patient data. OR
* representative of company who provide rostering or patient information system services to hospitals.

Primary outcomes:
measure: Mean absolute error of prediction
timeFrame: For each 12-hour shift
Secondary outcomes:
measure: mortality
timeFrame: within 30 days of patient admission
measure: length of stay
timeFrame: from hospital admission until discharge
measure: readmission
timeFrame: within 30 days of hospital admission
measure: healthcare-associated conditions
timeFrame: from hospital admission until discharge
measure: enough staff for quality
timeFrame: for each 8- or 12-hour shift
measure: missed breaks
timeFrame: for each 8- or 12-hour shift
measure: missed care
timeFrame: for each 8- or 12-hour shift
Other outcomes:
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