Nursing WorkloadPREDICT-NURSENCT06923943
Predicting Nurse Staffing Requirements From Routinely Collected Data
An observational study of Nursing Workload, run by University of Southampton at 1 site in United Kingdom.
Trial 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?… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Nursing Workload
- How many people
- 80 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Nov 2025to 31 Jul 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Child, Adult, Older adult
Study population: English NHS acute hospital Trusts
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.
Trial locations and contacts
1 site in 1 country.
About this study
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.
Read the 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.
- Conditions
- Nursing Workload
- Keywords
- nurse staffing, workload, prediction, workforce, safety
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: For each 12-hour shift
Secondary outcomes (5)
mortality
used to test validity of the prediction algorithm for estimating nurse staffing requirements
Measured: within 30 days of patient admission
length of stay
used to test validity of the prediction algorithm for estimating nurse staffing requirements
Measured: from hospital admission until discharge
readmission
to test validity of the prediction algorithm for estimating nurse staffing requirements
Measured: 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
Measured: 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.
Measured: for each 8- or 12-hour shift
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Apr 2025
- Study start1 Nov 2025
- Primary completion31 Jul 2026 (estimated)
- Study completion31 Jul 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Nursing WorkloadStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 80 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Southampton
- Sponsor type
- University, hospital or other
- Collaborators
- Imperial College London, Imperial College Healthcare NHS Trust, Portsmouth Hospitals NHS Trust, NHS England, Guy's and St Thomas' NHS Foundation Trust
- Responsible party
- Principal investigator, Christina Saville, University of Southampton
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06923943
- Sponsor's protocol ID
- 100780
- Other IDs
- NIHR166784, 346148
- Acronym
- PREDICT-NURSE
- Status last verified
- Feb 2026
- First submitted
- 27 Mar 2025
- Last updated
- 27 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 27 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Predicting Nurse Staffing Requirements From Routinely Collected Data
- ClinicalTrials.gov ID
- NCT06923943
- Acronym / sponsor ID
- PREDICT-NURSE / 100780
- Conditions
- Nursing Workload
- Keywords
- nurse staffing, workload, prediction, workforce, safety
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 80 participants (estimated)
- Lead sponsor
- University of SouthamptonUniversity, hospital or other
- Collaborators
- Imperial College London, Imperial College Healthcare NHS Trust, Portsmouth Hospitals NHS Trust, NHS England, Guy's and St Thomas' NHS Foundation Trust
- Study start
- 1 Nov 2025 (actual)
- Primary completion
- 31 Jul 2026 (estimated)
- Study completion
- 31 Jul 2026 (estimated)
- First posted
- 11 Apr 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Sex
- All
- Age groups
- Child, Adult, Older adult
- Study population
- English NHS acute hospital Trusts
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Mean absolute error of prediction
Time frame: For each 12-hour shift
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.
5 secondary outcomes
mortality
Time frame: within 30 days of patient admission
length of stay
Time frame: from hospital admission until discharge
readmission
Time frame: within 30 days of hospital admission
healthcare-associated conditions
Time frame: from hospital admission until discharge
were the nursing staff on duty appropriate to meet patient care needs
Time frame: for each 8- or 12-hour shift
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 27 Feb 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Nursing Workload, run by University of Southampton at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 27 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Mean absolute error of prediction (time frame: For each 12-hour shift). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 27 Feb 2026.