Nursing Workload
Conditions
Keywords
nurse staffing, workload, prediction, workforce, safety
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 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.
Interventions
None listed
Sponsors
University of Southampton
Imperial College London
Imperial College Healthcare NHS Trust
Portsmouth Hospitals NHS Trust
NHS England
Guy's and St Thomas' NHS Foundation Trust
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Inclusion 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Mean absolute error of prediction | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| mortality | within 30 days of patient admission | used to test validity of the prediction algorithm for estimating nurse staffing requirements |
| length of stay | from hospital admission until discharge | used to test validity of the prediction algorithm for estimating nurse staffing requirements |
| readmission | within 30 days of hospital admission | to test validity of the prediction algorithm for estimating nurse staffing requirements |
| healthcare-associated conditions | from hospital admission until discharge | infections that patients get while receiving healthcare. Used to test validity of the prediction algorithm for estimating nurse staffing requirements |
| were the nursing staff on duty appropriate to meet patient care needs | for each 8- or 12-hour shift | as assessed by the nurse in charge of the ward. Used to assess validity of the prediction of nurse staffing requirements. |
Countries
United Kingdom
Outcome results
None listed