Predicting Nurse Staffing Requirements From Routinely Collected Data
试验速览
- 阶段
- 不适用
- 状态
- 尚未招募
- 入组人数
- 80
- 主要终点
- Mean absolute error of prediction
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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.
排除标准
- 未提供
结局指标
主要结局
Mean absolute error of prediction
时间窗: 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.
次要结局
- enough staff for quality(for each 8- or 12-hour shift)
- mortality(within 30 days of patient admission)
- length of stay(from hospital admission until discharge)
- readmission(within 30 days of hospital admission)
- healthcare-associated conditions(from hospital admission until discharge)
- missed breaks(for each 8- or 12-hour shift)
- missed care(for each 8- or 12-hour shift)
研究者
Christina Saville
Dr.
University of Southampton