Cost-effectiveness of a quality improvement project, including simulation-based training, on reducing door-to-needle times in stroke thrombolysis.

Abstract

Rapid revascularisation in acute ischaemic stroke is crucial to reduce its total burden including societal costs. A quality improvement (QI) project that included streamlining the stroke care pathway and simulation-based training was followed by a significant reduction in median door-to-needle time (27 to 13 min) and improved patient outcomes after stroke thrombolysis at our centre. Here, we present a retrospective cost-effectiveness analysis of the QI project. Costs for implementing and sustaining QI were assessed using recognised frameworks for economic evaluations. Effectiveness was calculated from previously published outcome measures. Cost-effectiveness was presented as incremental cost-effectiveness ratios including costs per minute door-to-needle time reduction per patient, and costs per averted death in the 13-month post-intervention period. We also estimated incremental cost-effectiveness ratios for a projected 5-year post-intervention period and for varying numbers of patients treated with thrombolysis. Furthermore, we performed a sensitivity analysis including and excluding costs of unpaid time. All costs including fixed costs for implementing the QI project totalled US$44 802, while monthly costs were US$2141. We calculated a mean reduction in door-to-needle time of 13.1 min per patient and 6.36 annual averted deaths. Across different scenarios, the estimated costs per minute reduction in door-to-needle time per patient ranged from US$13 to US$29, and the estimated costs per averted death ranged from US$4679 to US$10 543. We have shown that a QI project aiming to improve stroke thrombolysis treatment at our centre can be implemented and sustained at a relatively low cost with increasing cost-effectiveness over time. Our work builds on the emerging theory and practice for economic evaluations in QI projects and simulation-based training. The presented cost-effectiveness data might help guide healthcare leaders planning similar interventions.