Purpose
This paper presents work in progress from a two year mixed methods study in the UK to evaluate the impact of a community nursing workload activity tool as a mechanism for modelling optimum caseloads to underpin decisions about safe staffing levels. Current methods of measuring workload and output in the community context are not robust enough to capture the complexity of care differences in rural and urban populations. Many teams have heavy caseloads, poor/inappropriate referrals, and an inability to state when capacity has been reached. .
Research Aim
To develop and evaluate a robust model to predict and plan for optimum community nursing caseload activity within a whole system.
Research Objectives
1. To develop a taxonomy and associated database that provides a consistent language for describing community nursing interventions that can be used to provide reliable and comparable metrics.
2. To determine the utility of the Cassandra tool in capturing community nursing interventions.
3 To use the data collected to build an inter-relational model of community nursing practice that can be used to determine, case-load, activity and develop a predictive model.
4. To evaluate the usability of the model in assisting managers and local decision-makers in workforce planning.
5. To assess the effectiveness of the model in capturing community nursing care left undone or missed
6. To explore how the model interrelates community nursing caseload activity with other care provision in a whole system.
Methods
Informed by critical realism, which attempts to understand real world issues, the design is guided by optimum caseload modelling, and given the multivariate nature of the environment in which workload activity takes place, a multiple case study evaluation across six NHS Pilot sites in England. Full ethical approval is in place.
Results
Results from case study sites demonstrated we have created a robust tool that captures an accurate picture of the multidimensional complexity of community nursing intervention, context of care, users of care and care left undone and are beginning to mine the data to identify patterns and relationships to build and test more accurate predictive optimum caseload activity tools to support workforce planning around patient acuity and skill mix, and provide an economic analysis of the cost of care left undone. Application in international contexts will be considered.
Conclusions
The tool can accurately capture a representative picture of how community and district nurses spend their time by generating both individual and organisational level reports. This reporting is speedy and enables workforce planners to work with robust evidence to make decisions about commissioning education for nurses, identifying skills shortages to target recruitment and retention activities, and to underpin decision making about commissioning services and the workforce required to provide high quality care
