Aim: The aim of this study was to investigate the impact of changes to bed configuration and patient mix on nurses’ workload in a single ward. Design: Multi-method case study. Method: The study was undertaken in an acute 28-bed ward in a tertiary referral public hospital in Queensland, Australia. Ward-level administrative data were obtained for a 2-year period, 12 months before bed configuration changes in October 2015 and 12 months after. These data included patient activity (bed occupancy, transfers, length of stay and casemix) and nurse staffing (budgeted and actual staffing levels, employment status and skillmix). Semi-structured interviews were conducted with ward nurses (N = 17) to explore the impact of the bed configuration changes on their workload. Results: Administrative data showed that the bed configuration changes resulted in more complex and dependent patients, increased patient transfers and greater variability in casemix. The interview data found these changes to patient complexity and activity intensified workloads, which were further increased by staffing decisions that resulted in greater reliance on temporary staff. Conclusion: Hospitals already possess the data and expert knowledge needed to improve staffing and bed management decisions without the need for additional, costly workload systems. Impact: Determining appropriate nurse staffing in light of the complexities and variation of patient needs at the ward level remains a challenge. This study identified increases in patient complexity, dependency, variability and churn that increased workload. Staffing grew but hidden factors associated with temporary staffing and skillmix further intensified nurses’ workload. Harnessing existing data and the expertise and experience of nursing unit managers (NUMs) would help staff wards more efficiently and effectively, providing reasonable workloads and appropriate skillmix that can enhance the safety and quality of patient care. To facilitate this, NUMs need access to accurate, timely, data and authority in staffing and bed management decisions.