Ox-POS – Using economic methods to optimise cancer care in the NHS: a case study in sarcoma

Ox-POS – Using economic methods to optimise cancer care in the NHS: a case study in sarcoma

Supervisors:

• Associate Prof. Apostolos Tsiachristas
• Dr Lisa Hinton
• Professor Bass Hassan, Oxford Molecular Pathology Institute

BACKGROUND

Cancer imposes a significant burden on individuals, families, and societies worldwide. Addressing the burden of cancer requires optimizing cancer care pathways that involves streamlining processes to ensure timely and effective diagnosis, treatment, and follow-up care for cancer patients. From a health services point of view, there are several strategies to optimize cancer care pathways including multidisciplinary teams, patient navigation programs, and early detection and screening. The increasing use of genome sequencing and other genomic technologies as we as imaging and digital pathology is raising the expectations for these strategies to improve outcomes, enhance patient experiences, and optimize resource utilization in the fight against cancer. Towards that direction, the University of Oxford is conducting the Oxford Precision Oncology for Sarcoma (OxPOS) study to explore whether the integration of new technologies that offer personalised or precision medicine can result in measurable improvements in care for patients with this rare cancer.

AIM AND METHODS

As part of this study, a DPhil project is funded to assess the cost-effectiveness of the OxPOS interventions, characterise their underlying value drivers, and develop scenarios on how to use them in order to optimize the sarcoma care pathway in the English NHS. Quasi-experimental methods (or causal inference) may be applied to assess the cost-effectiveness of precision oncology in the NHS as implemented in the OxPOS study. Causal inference in healthcare refers to the process of determining cause-and-effect relationships between healthcare interventions or exposures and outcomes of interest and some of the most common methods include propensity score matching, instrumental variable analysis, and various forms of regression analysis. Optimization modelling may also be applied to optimise cancer precision oncology (by obtaining the right diagnosis and giving the right treatment at the right time) using sarcoma as a case study. Optimization in this context is defined as finding the best possible solution for a given problem given the complexity of the system inputs, outputs/outcomes, and constraints (budget limits, staffing capacity, etc.). Optimizing a care pathway would require to use optimization methods to identify the optimal allocation of resources across interventions alongside the pathway, subject to various types of constraints. Such methods may include a range of techniques such as linear programming, constraint optimization, or dynamic simulation modelling

INTENDED OUTCOMES

The main outcome of this DPhil topic is to assess the cost-effectiveness if precision oncology as implemented in the OxPOS study and to apply a methodological approach that can optimize cancer care processes, improve patient care, and achieve better outcomes while balancing cost and resource constraints

Potential candidates should have a strong quantitative background in (health) economics, statistics, epidemiology, data science, maths, or operations research. 

FUNDING

Funding linked to this project will cover Home/Overseas level university fees and a stipend of at least £20,775 pa for three years. 

APPLICATION DEADLINE

The application deadline is 01 May 2024. Please contact [email protected] if you wish to discuss this opportunity.