Nursing dependency tools

This offers assurance methods to set nursing establishments are effective. A mobile app allowing direct data input on the ward now being developed nationally has reduced time spent on data collection for each ward by 45 minutes a day. The intensive care unit already collects such data daily. As the tool does not take into consideration the turnaround of patients in the emergency department, so the trust no longer uses the tool in these areas.

The trust gained the cooperation of staff by involving nursing staff from the outset, and encouraging open discussion of the advantages and disadvantages of using the tool. The senior sisters lead use of the tool, and data for each ward is collected by three people, either the senior sisters or a nominated deputy, One of the biggest challenges has been validating the data and ensuring consistency. Once a week when data is being collected, the trust allocates two senior nurses, one external and one from the ward, to validate and peer review the collection.

Senior nurses also receive training three times a year prior to data collection, using case studies to agree the level of care.

The tool lets the trust decide nurse staffing on the levels of care needed. The director of nursing is now proposing to use the data for budget review. Trusts involved in the development and testing of the SCNT have offered a number of tips based on their experience:.

The SNCT is being developed and validated for other care settings. The tool now includes staff multipliers for acute assessment units as well as acute inpatient wards. Sign in or Register a new account to join the discussion. You are here: Patient safety. A tool to calculate safe nurse staffing levels.

Abstract A guideline from the National Institute for Health and Care Excellence and a NICE-endorsed tool on safer nursing care allow decisions on safe staffing levels to be made at a local level. This article has been double-blind peer reviewed Scroll down to read the article or download a print-friendly PDF here.

The Northwick Park Nursing Dependency tool provides an assessment of patient care needs. It is an ordinal scale incorporating activities of daily living, safety awareness, behavioural management and communication.

The Northwick Park Care Needs Assessment is derived from the NPDS using a computerised algorithm based on a set of validated 'rules' or assumptions, together with a small additional set of 5 questions about the community setting.

The computer outputs include an overall nursing dependency score, an estimation of care hours for each carer and a suggested care package required to meet the care needs.

End of Life Care. Infection Intelligence Platform. Community Health Activity Data. Child Health. Dental Care. Emergency Care. General Practice. Health Conditions. Heart Disease. Hospital Care. Mental Health. Public Health. Quality Indicators. However, it was also recognised that there was a need to understand the experience of staff using the tool. This was achieved through a mix of semi-structured one-to-one interviews lasting approximately an hour with six staff, plus a focus group with a cross section of 12 staff from the nursing teams.

To facilitate meaningful discussion within the focus group 25 real patient scenarios were used. The purpose was to determine consistency in thinking between individuals and teams. The information gathered through the focus group was analysed through two processes - firstly by comparing dependency scores across the teams, then by extending the thematic analysis that was already being developed through the one-to-one interview process.

Of particular relevance was the outcome of discussions relating to dependency decisions, which identified two of specific themes, namely:. Although there is a general framework to support the decision-making process, the dependency level is affected by patient-specific detail. For example, if a patient requires a dressing change for a sacral pressure ulcer and the nurse is required to hoist and position the patient before and after the dressing change, the allocation would potentially need to be longer than if the patient was relatively mobile and able to position themselves.

It is therefore not possible or appropriate to exclude professional judgement. This is reflective of the literature, which indicates that, despite the risk Goldstone et al highlighted in terms of professional judgement potentially manipulating a tool, it has not been possible to develop a community-based procedure for managing patient demand that excludes elements of professional judgement.

Patients having multiple nursing needs results in different dependency levels, subject to which nursing tasks need to be undertaken at any specific visit. Patients, therefore, potentially need more than one dependency score. For example, a patient with diabetes requiring daily visits for routine insulin administration allocated as dependency minutes , who also requires a dressing change three times a week for a foot ulcer, would also have this care activity allocated also dependency 1.

On the visits when the patient requires insulin and a dressing change, the dependency for the nurse visit would be identified as category 2 30 minutes. Overall the focus group confirmed that the teams were comfortable that, although there were occasional discrepancies in the allocation of dependency levels, this was more reflective of patient-specific information that informs professional judgement, rather than inaccuracy with the application of the dependency tool itself.

While the development of the processes using the data capture system were taking place, parallel work was being undertaken within the organisation to develop the use of the clinical recording system SystemOne. The intention was to facilitate electronic allocation of nursing visits, using individual patient care plans set up within the system.

All community nursing in Solihull is based on allocations to meet specified need within individual care plans, such as wound care and the administration of intravenous antibiotics. However, feedback from staff highlighted that work needed to be duplicated to populate information for the dependency and capacity tool as well as the electronic allocations. It was understood from the limited evidence base in the literature that the use of dependency and capacity tools is unlikely to be effective if the associated additional workload is considered disproportionate to the positive effects of using the tool.

A commitment was therefore made to develop a capacity and dependency element within the clinical recording system used for electronic allocations. Combining the two processes is now complete and real-time information is used on a day-to-day basis to support the management of demand and capacity in the service. From the data collated during the initial dependency and capacity tool roll-out, there was evidence of sufficient staffing hours available to meet patient demand.

However, the profiling of staff hours did not align with the anticipated level of patient need. As the work progressed, the alignment of capacity and demand improved.