Nursing is a trust’s most expensive resource. Workforce planning methods help managers estimate how many nurses they need and the skills they should have
Keith Hurst, PhD, is an independent analyst, Nottinghamshire.
Hurst K (2010) Evaluating the strengths and weaknesses of NHS workforce planning methods. Nursing Times; 106: 40, early online publication.
This article examines the different methods used in NHS workforce planning and development.
It is a summary and update of the Department of Health commissioned nurse staffing study (Hurst, 2003) and describes different methods used for NHS workforce planning and development as well as their individual strengths and weaknesses. The article is designed to help nurse managers select and apply methods for evaluating or estimating their staffing needs and looks at the future for workforce planning and development.
Key words: Workforce planning, Staffing levels, Skill mix, Quality
- This article has been double-blind peer reviewed
Daily nursing costs for one patient in an elderly care ward is estimated to be £116 and this rises to £525 for patients in critical care units (Skills for Health Workforce Planning Team, 2010a).
Nurses and healthcare support workers are the NHS’s largest and therefore most expensive workforce and it is important that nurses’ time is used efficiently and effectively. There are six methods used for estimating the size of ward and department teams and skill mix and these fall into two broad approaches: one top-down method and five bottom-up. Each has strengths and weaknesses, so it is wise to plan staffing recommendations using at least two methods. The commonly used methods for workforce planning and development are outlined in Box 1. Examples are provided in this article to show how all six methods are used in practice.
Box 1. Six commonly used workforce planning and development methods
Macro, top-down, population-based methods
- Benchmarking databases (SfH WPT, 2010b)
Micro, bottom-up or workload-driven methods: from simple to sophisticated
- Professional judgment or consensus methods (Telford, 1979)
- Staff to bed ratios, for example Leeds University Nursing Database (SfH WPT, 2010a)
- Workload-quality, for example Safer Nursing Care (Smith et al, 2009)
- Timed task, for example GRASP (Anderson, 1997)
- Regression (Kaplan, 1975)
What is NHS workforce planning and development?
Effective workforce planning and development supports service quality by ensuring sufficient workers with the right skills are in the right place at the right time at the right price. Health workforce planning and development is usually presented as scales illustrated in Fig 1. These emphasise how ward-based workforce planning and development cannot be viewed in isolation. It is likely that staffing reviews will involve clinical staff and managers as well as personnel, commissioning and education colleagues.
All four UK countries maintain data warehouses from where NHS staffing and related data can be obtained. These are listed in Box 2.
Box 2. UK health service data warehouses
There are currently around 1,350 workforce-related datasets and these are being aggregated into dedicated sites (SfH WPT, 2010b), which makes benchmarking workforce easier.
The SfH WPT (2010b) Benchmarking Database, for example, show top-rated trusts (based on Care Quality Commission ratings) employ 4.3 registered nurses (RN) and 2.5 healthcare support workers (HCSW) for each bed compared with bottom-rated trusts who employ 0.9 and 1.9 respectively.
This involves expert, multi-disciplinary groups armed with “local intelligence”, such as moving to new buildings, agree a ward or department team’s size and mix by consensus. This was first used in the 1970s and is one of the oldest methods for workforce planning and development (Telford, 1979). Box 3 highlights its strengths and weakness. (See PDF.)
It is the quickest and simplest method because precise information such as patient dependency data is not needed. Workforce planning teams often begin with this method but may run into difficulties if clinicians and finance managers cannot agree – the former are often concerned with patient safety, the latter with affordability. An example of how it can be used is outlined in Box 4. (See PDF.)
The major criticism of the professional judgement method is that it is too subjective, especially if nurses alone decide the number of nursing staff needed.
It is interesting to note research updating the Department of Health Expert Working Group (2003) neonatal service report showed that the original professional judgement based staffing multipliers (the staffing value multiplied by occupied beds) were close to ones determined later by fieldwork using cot-occupancy, neonatal dependency and staff activity data. For example, the expert group using professional judgement] decided a level 1 special care baby unit cot required 0.33 FTE staff, whereas fieldwork indicated a 0.32 was appropriate. Fieldwork-determined multipliers are considerably more expensive and time-consuming to generate compare to professional judgement calculations.
Staff to bed ratios
The staff per occupied bed method uses staff (by grade) to occupied bed ratios from “best-practice” wards that pass a service-quality test. The UK has staff to bed ratios for all services (SfH WPT, 2010a), but these have to be used with caution and the strengths and weaknesses of this method are outlined in Box 5. (See PDF.)
Unlike the professional judgement method, the staff per bed ratio is evidence-based, which makes it more expensive to establish and maintain. Although the ratios provide excellent benchmarks and are easy to use, it is important that the person calculating them uses occupied beds rather than total beds and full-time staff, not headcount otherwise benchmarks are useless. Occupied beds (the numerator) is divided by the number of full time staff (the denominator). An example is provided in Box 6. (See PDF.)
This method assumes that patients generate the same workload; namely that all patients have similar dependency. Another failing is that throughput (how many times a bed empties and fills each shift) is not recognised, which makes it inappropriate for admission and assessment wards. Free software (SfH WPT, 2010a) can help convert occupied beds into a ward establishment. However it is important to use the right care group – for example, it would not be appropriate to use elderly care ward staffing ratios in children’s wards.
Finally, ratios from different ward designs are being developed as, for example, single-rooms require different staffing configurations to Nightingale wards (Hurst, 2008).
The workload-quality method
The workload (or acuity) quality method is popular in the UK. It is a sophisticated algorithm that uses occupancy, throughput, patient dependency, direct patient care times and ward overhead data from best-practice wards. The strengths and weakness are outlined in Box 7. (See PDF).
This method is more expensive to set-up, maintain and use than the two bottom-up methods covered so far because seven important datasets, such as patient dependency, are required, to ensure that staffing estimations are workload-based. Wards with sicker patients, are therefore allocated more staff.
These more sophisticated methods mean that busy ward staff have to collect data, which does not influence patient care directly and they may resent this especially if staffing does not change as a result. The alternative is importing data from other wards, but borrowed data may not be from quality-assured wards.
Although this is a flexible method and there is excellent, freely available software for calculating your workforce (SfH WPT, 2010a), the staffing formula does not suit small wards (ten occupied beds or less) with low dependency patients because recommended establishments may be insufficient to place at least one RN on each shift. In this case, the professional judgement method is preferred.
Perhaps the most significant workload-quality method development in the last few years is the Safer Nursing Care (SNC) multipliers (Harrison 2004; Hurst 2008; Smith et al 2009).
Safer Nursing Care’s strength is combining patient dependency definitions outlined with empirically determined staffing requirements resulting in simple ward staffing formulas (Box 8, see PDF). Patient dependency and staff activity used to generate the definitions were collected alongside ward quality data, ensuring that substandard wards are excluded from the main database. More detailed care level definitions (for example, of Level 0) can be found in Hurst (2008).
It is clear that workload-quality methods are more sensitive to nursing workload than other methods and placing patients into the correct “care level” is crucial. It is important that ward managers check nurses are accurately assessing patient dependency (Hurst, 2009). Fig 2. outlines how this is used in practice. (See PDF.)
In the future it is likely that patient dependency will be replaced by healthcare resource groups so that ward staffing levels are connected to payment by results (RCN, 2009). Supporting data and algorithms are emerging slowly but none are ready for general use.
A recent and important development has incorporated patient turnover in busy wards to obtain more accurate workload indicators. Also under development are ward-design sensitive staffing formulas. Eventually separate algorithms for busy admission/assessment single-room wards will be available.
Ward staff rostering software, such as Care Ware (caresystemsinc.com), which combines electronic duty rostering with workload-based staffing, are likely to become commonplace.
Nursing care plans are commonplace in wards, so it makes sense to attach care times to nursing interventions so that ward staffing can be estimated. The GRASP approach (Anderson, 1997) attaches care times to interventions in patients care plans before adding ward overheads to cover indirect patient care activities. This can be used to estimate nursing hours per patient day. Box 9 outlines the strengths and weaknesses of this method. (See PDF.)
These systems are commercial and have licensing costs. They also add considerably to ward overheads since detailed, individual care plans are essential although computerising them reduces setting-up and maintenance time significantly. Evidence-based standard care plans (care pathways) and time-task methods go hand-in-glove and like HRGs, care pathways are an exciting development.
The regression formula, uses one main ward element such as: theatre sessions (in surgery wards); complex nursing procedures (in critical care units) and escorts (in diagnostic wards) to predict how many staff is needed. The strengths and weaknesses of this method are outlined in Box 10. (See PDF.)
Although weaknesses outnumber strengths in Box 10 this method is recognised to be the best forecaster of staffing in areas with predictable workloads- for example, planned waiting-list theatre cases next week. It is simple and cheap to use - when software-based - but the licence can be costly. The main weakness is that regression statistics and related language are off-putting. Invariably, a statistician’s help is needed to explain, for example “predicting beyond the range” and its dangers. Finally, some nurses are unhappy about importing data from wards and that “ownership” is missing and imported data may not come from best-practice wards. An example of the regression method is outlined in Box 11. (See PDF.)
Nursing is a trust’s most expensive resource. We know from the SfH WPT (2010b) database that nursing costs per bed vary significantly, which is not always related to service quality. It is therefore important that we systematically evaluate our ward and department staffing.
Nurse managers have a choice ranging from methods that are quick, simple and easily used, such as professional judgement to robust and more expensive time-task approaches. Six common methods, broadly classified as bottom-up and top-down can be combined so that results can be triangulated. Experienced managers do not aim for quick fixes - they argue for staffing changes using reports incorporating results from at least two methods described. Consequently, they should capitalise on the centrally held, free to use nursing data at their disposal. However, if fieldwork is commissioned in your hospital ensure that data you collect support all six workforce planning and development bottom-up approaches.
Anderson L (1997) The role and resources required for the introduction of ward assistants using the GRASP systems workload method: a quantitative study. Journal of Nursing Management; 5: 11-17.
Department of Health Expert Working Group (2003) Neonatal intensive care services - report of the Department of Health Expert Working Group. London: DH.
Harrison J (2004) Addressing increasing patient acuity and workload. Nursing Management; 11: 4, 20-25.
Hurst K (2003) Selecting and Applying Methods for Estimating the Size and Mix of Nursing Teams. Leeds University: Nuffield Institute for Health.
Hurst K (2008) UK ward design, patient dependency, nursing workload, staffing and quality – an observational study. International Journal of Nursing Studies; 45: 370-381.
Hurst K (2009) Gaming and up-coding. Nursing Management; 15: 9, 19-23.
Kaplan RS (1975) Approaches and techniques. Analysis and control of nurse staffing. Health Services Research; Fall: 278-296.
Royal College of Nursing (2009) Nursing and Payment by Results: understanding the cost of care. London: Royal College of Nursing.
Skills for Health Workforce Projects Team (SfH WPT) (2010a) The Nursing Workforce Planning Tool.
Skills for Health Workforce Projects Team (SfH WPT) (2010b) The NHS Benchmarking Database.
Smith et al (2009) Developing, testing and applying instruments for measuring rising dependency-acuity’s impact on ward staffing and quality. International Journal of Healthcare Quality Assurance; 22: 1, 30-39.
Telford WA (1979) A method of determining nursing establishments. Hospital Health Services Review; 5: 4, 11-17.