This course is about the European and global regulatory framework for clinical and reimbursement decisions on new medical technologies, and the use of quantitative methodologies for analysing and support of decisions to improve value-based personalized (cancer) care. The emphasis is on the use of methods for evidence development, the use of Real-World Data to analyse actual use of new medical treatments, simulation modelling to improve access to precision oncology and the use of value-frameworks for personalized treatment decisions.|
Healthcare is one of the great challenges of modern times, with health care spending going up (close to 15% GDP in some European, and 18% GDP in the US) and with significant technological progress and opportunities to make healthcare delivery more efficient, safer and better. There obviously is a global trend that healthcare should become more patient-centered or personalized, particularly in oncology which is largely based on genomics discoveries. Personalization is advocated not only to achieve better health outcomes at lower burden and financial cost (e.g. precision or stratified medicine), but also to put those who undergo health services in the centre so they can have a say in what is important to them. Within this challenging domain, it is essential to systematically analyse the effects of any new technology on clinical outcomes, resources required and the added value to society. Health Technology Assessment is known for its systematic and transparent methods for analysing these outcomes, and to support decision makers to do what is best for patients. As an introduction, the course will therefore start with an outline of the regulatory framework in Europe (i.e. the European Medicines Agency and the European Network for HTA agencies, EUNetHTA), as well as the differences between hospital-based (procurement) and government decisions (listing in benefits package). We will then specifically talk about challenges in precision oncology, with multiple treatments available based on genomic aberrations.|
The main part of this course is an introduction to some of the key-methodologies used to inform decisions as mentioned above. We will mainly introduce the methods, illustrate the applications using real-world data and also discuss the limitations. Yet, it should be noted that this course is not about a specific modelling approach, as these are covered elsewhere.
First, we present an overview of different clinical trial strategies, and the complexity of evidence development in an era of personalized medicine with molecular biomarkers and high-cost (targeted) drugs. We will introduce new adaptive designs, such as SMART designs, and discuss how simulation modelling might be used to inform the approval and reimbursement of high-cost (cancer) medicines.
Second, we will introduce specific modelling methods for supporting decision making in cancer care. This part of the course will introduce simulation modelling for healthcare delivery research, such as discrete-event simulation and demonstrates how such models can be used to optimize a cancer survivors’ follow-up program. We will also explore dynamic simulation modelling methods for planning of efficient allocation of resources in order to maximize health outcomes (and not just the efficiency of the healthcare process) for the individual patient.
The third and final part of this course deals with the different initiatives to support personalized value-based clinical decisions. Such decisions usually combine the best available clinical evidence on therapeutic alternatives, and weigh the evidence either implicitly or explicitly. This chapter introduces global initiatives for such value-frameworks, such as the ESMO (European Society for Medical Oncology) benefits scale and the ASCO (American Society for Clinical Oncology) framework, both based on Multi-Criteria Decision Analysis.
The course is delivered face-to-face and as interactive as possible and takes the real-world problems as a starting point. We will start with a course outline and an introduction of a real-world data set of patients with different cancers to explore complexity of treatment pathways. Students are challenged to work on these problems and find creative solutions based on their experience with other courses.