Cost and effectiveness of autologous haematopoietic stem cell transplantation and high-efficacy disease-modifying therapies in relapsing-remitting multiple sclerosis

BACKGROUND

Autologous haematopoietic stem cell transplantation (AHSCT) is a highly effective one-off treatment for relapsing-remitting multiple sclerosis (RR-MS), potentially representing an optimal front-loading strategy for costs.

OBJECTIVE

Exploring cost/effectiveness of AHSCT and high-efficacy disease-modifying treatments (HE-DMTs) in RR-MS, estimating costs at our centre in Italy, where National Health Service (NHS) provides universal health coverage.

METHODS

Costs (including drugs, inpatient/outpatient management) for treatment with AHSCT and HE-DMTs were calculated as NHS expenditures over 2- and 5-year periods. Cost-effectiveness for each treatment was estimated as "cost needed to treat" (CNT), i.e. expense to prevent relapses, progression, or disease activity (NEDA) in one patient over n-years, retrieving outcomes from published studies.

RESULTS

Costs of AHSCT and HE-DMTs were similar over 2 years, whereas AHSCT was cheaper than most HE-DMTs over 5 years (€46 600 vs €93 800, respectively). When estimating cost-effectiveness of treatments, over 2 years, mean CNT of HE-DMTs for NEDA was twofold that of AHSCT, whereas it was similar for relapses and disability. Differences in CNT were remarkable over 5 years, especially for NEDA, being mean CNT of HE-DMTs €382 800 vs €74 900 for AHSCT.

CONCLUSIONS

AHSCT may be highly cost-effective in selected aggressive RR-MS. Besides priceless benefits for treated individuals, cost-savings generated by AHSCT may contribute to improving healthcare assistance at a population level.

Precision Workforce Management for Radiographers: Monitoring and Managing Competences with an Automatic Tool

Optimizing work shifts in healthcare is crucial for maintaining high standards of service delivery and fostering professional development. This study delves into the emerging field of skill-oriented work shift optimization, focusing specifically on radiographers within the healthcare sector. Through the development of Skills Retention Monitoring (SRH), this research aims to enhance skill monitoring, workload management, and organizational performance. In this study, several key highlights emerged: (a) Introduction of the SRH tool: The SRH tool represents a resource-efficient solution that harnesses existing software infrastructure. A preliminary version, focusing on the radiographers' professional profile, was released, and after several months of use, it demonstrated effectiveness in optimizing work based on competency monitoring. (b) The SRH tool has thus demonstrated the capacity to generate actionable insights in the organizational context of radiographers. By generating weekly reports, the SRH tool streamlines activity management and optimizes resource allocation within healthcare settings. (c) Application of a Computer-Assisted Web Interviewing (CAWI) tool for pre-release feedback during a training event. (d) Strategic importance of a maintenance and monitoring plan: This plan, rooted in a continuous quality improvement approach and key performance indicators, ensures the sustained effectiveness of the SRH tool. (e) Strategic importance of a transfer plan: Involving professional associations and employing targeted questionnaires, this plan ensures the customization of the tool from the perspective of each profession involved. This is a crucial point, as it will enable the release of tool versions tailored to various professions operating within the hospital sector. As a side result, the tool could allow for a more tailored and personalized medicine both by connecting the insights gathered through the SRH tool with the right competencies for healthcare professionals and with individual patient data. This integration could lead to better-informed decision making, optimizing treatment strategies based on both patient needs and the specific expertise of the healthcare provider. Future directions include deploying the SRH tool within the Pisa hospital network and exploring integration with AI algorithms for further optimization. Overall, this research contributes to advancing work shift optimization strategies and promoting excellence in healthcare service delivery.