Developing a workload benchmarking tool for community (public health) nursing

Objectives: To outline the development of a safer staffing tool for use in community (public health) nursing. In 2013, funding was secured to convert selected variables and outputs from the UK Nursing Database to: (i) create community staffing formulae; and (ii) generate organisational profiles. A workload-quality driven workforce planning and development model was built and tested to enable community nursing teams to calculate their workloads and benchmark staffing levels and skill mix against high performing teams. This complements similar models built for inpatient areas (the Hurst Tools). Population: 430 community teams have entered data into the English Safer Staffing community workforce model since 2013 (Scotland and Northern Ireland have similar databases, appropriate to their context). Data are derived from more than 13,000 patient care days during which 3,700 community staff contacted, on average, eight patients a day. Methods: Staff (all grades) keep a diary, spanning 24hrs, Monday to Sunday, and record nursing interventions and patient dependency/acuity. An empirically derived workload index shows whether each full time equivalent staff member (FTE) is under- or over-occupied. This index is calculated from patient numbers, case mix, direct care time and an ‘overhead’ (e.g., desk time). Only data from teams achieving acceptable service quality is admitted to the model. Benchmarking information for overall staffing levels, skill mix and activity (contact time) is available Findings: Although clinic time is logged, patients are counted in the daily contact rate. Typical patient dependency is 13% Level 1 (simple case); 36% Level 2 (requiring intermediate interventions); 37% Level 3 (requiring complex interventions) and 14% Level 4 (most complex cases); suggesting highly dependent/acute patients aren’t being hospitalised, thereby relieving pressure on hospital beds. Sixty-seven percent of the workforce are registered practitioners, generally higher than inpatient specialties. Sixty-three percent of all activity is patient focussed; i.e., direct (face-to-face) care or indirect (patient-related) care. This is markedly higher than inpatient contact time. Twenty-nine% of staff time is spent on activities not directly connected with patient care (e.g. general meetings and administration). Unproductive time is negligible (less than 1%) in comparison with inpatient areas (10%). The concurrent service quality audit (166,000 questions answered by patients, carers and staff) indicates that patients and carers highly value the service they receive from community staff, but community staff report stressful workloads, which require significant unpaid overtime (i.e., completed at home outside work time). Conclusion: This community staffing model complements inpatient staffing models and enables comparisons across teams and localities. The tool has proved popular with senior staff as it provides valuable best-practice data to support service delivery. Results highlight that community care is efficient and effective, and is likely to reduce inpatient workload and costs significantly. However, community care appears to be the Cinderella service, which desperately needs additional resources. Front-line staff report challenges with objective assessment of patient dependency and have, at times, been resistant to data capture. The approach has been rolled out for community mental health and learning disability workforces and results will be shared at the conferenc