PhD position in Pharmaceutical Reimbursement Policies (1.0 FTE)

Do you wish to help inform the pharmaceutical reimbursement policies of the future? This PhD position offers a challenging project meant for an excellent candidate, who is interested in bridging pharmaceutical policy research with data science.

Pharmaceutical reimbursement policies are informed by insights from actual and rigorous policy studies. In this PhD project, you will provide decision makers within the field of health technology assessment (HTA) with insights to formulate effective international pharmaceutical reimbursement policies.

The most important task is to develop research activities that focus on advancing the understanding of how HTA organizations can deal with uncertainty. In a short period of time, you will learn many new techniques in the fields of health policy research and data science and apply them within multiple national and international research projects. You will work closely with national and international public organizations such as the Dutch National Health Care Institute (Zorginstituut Nederland, ZIN). Therefore, proficiency in Dutch is an inflexible requirement to be eligible for this position.

Key tasks and responsibilities:

• perform ground-breaking and internationally relevant research;
• work closely with public organizations and academic partners;
• apply data science methodologies within the domain of health policy research;
• teach health technology assessment courses within the Pharmacy and Drug Innovation curricula (10% of appointment).

Background
HTA informs decisions on drug reimbursement and therefore is a big determinant of patient access to innovative therapies. HTA practices vary between countries and is becoming even more divergent with the introduction of specific HTA pathways, for example for drugs that were granted a marketing authorization through expedited approval pathways. To make sure that HTA organizations can make well-informed decisions, HTA policies and practices are constantly analyzed and compared internationally. Situations of particular current interest are those in which relatively greater uncertainties about comparative benefits and/or risks are present. By internationally analyzing and comparing assessments of HTA organizations, decision-makers get novel insights on how to handle uncertainties associated with a lack of evidence on long-term comparative effects. The primary objective of this research project is to provide HTA decision-makers with suggestions on how to manage uncertainties regarding long-term comparative effects of medicines.

HTA policy research aims to ensure that current and novel HTA policies and practices are data-driven and informed by relevant evidence. Evidence-based policy making is only possible if policy studies are undertaken timely and performed rigorously. Health policy studies use (publicly available) regulatory and reimbursement organizations’ reports from which data is usually extracted manually. Because of the labor-intensiveness of this process, it is hard to provide continuous and timely answers to current health policy questions. Applied data science techniques may benefit HTA policy research by automating large parts of the data retrieval and validation processes. The second objective of the project is to improve the effectiveness of HTA policy research through the development and application of data science methodologies within the health policy domain.