Predicting Nurse Staffing Requirements From Routinely Collected Data (PREDICT-NURSE)

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Nursing Workload

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 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.

• Study Type: Observational
• Enrollment (Estimated): 80

Contacts and Locations

Study Locations

• United Kingdom
  • Southampton, United Kingdom
    • University of Southampton

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• Sampling Method: Non-Probability Sample

Study Population

English NHS acute hospital Trusts

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
National survey; We will survey staffing matrons and Chief Nursing Information Officers in England to find out how staffing tools are used and the availability/quality of patient data in IT systems.
Workshops; In workshops we will 1) ask nurses and managers what problems they have with current staffing systems and what would help, 2) discuss with nurses, NHS IT managers and IT system providers ideas for building our prediction algorithms into software products.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Mean absolute error of prediction	measured in whole-time-equivalents per patient. This is a measure of predictive accuracy, i.e. how well the algorithm's predictions match the target value for required nurse staffing on average across wards and shifts.	For each 12-hour shift

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
mortality	used to test validity of the prediction algorithm for estimating nurse staffing requirements	within 30 days of patient admission
length of stay	used to test validity of the prediction algorithm for estimating nurse staffing requirements	from hospital admission until discharge
readmission	to test validity of the prediction algorithm for estimating nurse staffing requirements	within 30 days of hospital admission
healthcare-associated conditions	infections that patients get while receiving healthcare. Used to test validity of the prediction algorithm for estimating nurse staffing requirements	from hospital admission until discharge
were the nursing staff on duty appropriate to meet patient care needs	as assessed by the nurse in charge of the ward. Used to assess validity of the prediction of nurse staffing requirements.	for each 8- or 12-hour shift

Collaborators and Investigators

• Sponsor: University of Southampton
• Collaborators: Imperial College London; Guy's and St Thomas' NHS Foundation Trust; Portsmouth Hospitals NHS Trust; Imperial College Healthcare NHS Trust; NHS England

Study record dates

Study Major Dates

• Study Start (Actual): November 1, 2025
• Primary Completion (Estimated): July 31, 2026
• Study Completion (Estimated): July 31, 2026

Study Registration Dates

• First Submitted: March 27, 2025
• First Submitted That Met QC Criteria: April 4, 2025
• First Posted (Actual): April 11, 2025

Study Record Updates

• Last Update Posted (Actual): February 27, 2026
• Last Update Submitted That Met QC Criteria: February 24, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: safety; prediction; workload; workforce; nurse staffing
• Other Study ID Numbers: 100780; NIHR166784 (Other Grant/Funding Number: National Institute for Health and Care Research); 346148 (Other Identifier: Integrated Research Application System)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No