Reducing the cost of online adaptive proton therapy: automation, workflow & health economics evaluation
- Planned secondment: Erasmus MC (The Netherlands)
- PhD program: Erasmus MC (The Netherlands)
Project description
As part of the online adaptive proton therapy (OAPT) process, daily adaptation requires substantial time and resources, which may affect clinical throughput and overall cost. While OAPT has the potential to improve treatment quality by accounting for anatomical changes, its widespread implementation is currently limited by the extra daily workload and the lack of clear evidence on its economic viability and acceptability for centres, insurers, and patients.
The central aim of this PhD project is to investigate how OAPT can be organized and automated so that the additional daily adaptation effort does not reduce treatment-room throughput and results in an acceptable cost per patient. The project will explore strategies to accelerate treatment delivery—such as reducing the number of beams or spots, optimizing dose rate, hypofractionation, and triggered adaptation—while maintaining plan quality and patient safety.
A second goal is to develop a time-driven activity-based costing model and apply health-economic methods to quantify the impact of workflow automation and different clinical strategies on treatment-room time, staff utilisation, cost per patient, and cost-effectiveness. Patient and public preferences (e.g., willingness to accept longer sessions versus fewer visits or lower toxicity), together with insurer and regulator perspectives, will be integrated into a multi-criteria decision-support tool to guide cost-effective use of OAPT.
This joint project between PSI (Switzerland) and ErasmusMC (NL) combines technical work (workflow analysis, scripting, planning) with quantitative health economics and preference modelling, in close collaboration with clinical, technical, and management teams at PSI and Erasmus.
The candidate will be enrolled in the Erasmus MC (NL) PhD program. The project is a 4-year funded PhD position: the candidate will be employed at PSI (Switzerland) for the first 3 years and employed at Erasmus MC for the 4th year.
For more information concerning the research project please contact:
Francesca Albertini and Mischa Hoogeman
Candidate profile
Doctoral Candidate at Paul Scherrer Institut (PSI)
- MSc degree in physics, biomedical engineer, software engineer or related studies OR in health economics, health sciences, with quantitative and analytical orientation
- Background in health economics or physics/engineering with interest in health-economic modelling
- Experience with costing methods, decision-analytic modelling, or biostatistics is a strong asset
- Interest in workflow optimisation, automation and scripting in a radiotherapy/proton-therapy environment
- Enjoy working at the interface of technology, clinical workflows and health policy/insurer perspectives
- Experience or interest in applying AI / machine learning to healthcare workflows, prediction models or decision-support tools
- Strong quantitative and analytical skills and the ability to work independently
- High motivation to pursue research excellence in proton therapy and adaptive radiotherapy
- Good communication skills and enjoyment of interdisciplinary, international teamwork
- Fluency in English (oral and written, C1 level)
- Programming skills and interest (e.g. Python, MATLAB, C++) and experience with scientific computing or data analysis
- Fulfil the European MSCA mobility rule: you must not have resided or carried out your main activity (work, studies, etc.) in Switzerland for more than 12 months in the 36 months immediately before your recruitment date
Paul Scherrer Institut
EMPLOYING ASSOCIATED PARTNER
The Center for Proton Therapy CPT at the Paul Scherrer Institute PSI is the world leader in the development and clinical implementation of Pencil Beam Scanning (PBS) and lntensity Modulated Proton Therapy (IMPT), both of which have been pioneered at the PSI. Additionally, the first online adaptive PT CT-based workflow has also been delivered at PSI in 2023.