Natalia Kunst

I am a Postdoctoral Research Fellow in the Department of Population Medicine at Harvard Medical School and Harvard Pilgrim Health Care Institute, and a Research Affiliate at the Yale University School of Public Health. Previously, I worked as a Senior Health Economist in a consulting company in Norway, developing and adapting decision-analytic models and preparing HTA applications.

My research focuses on applying decision-analytic and statistical methods in cancer, genetics and precision medicine to assess and identify efficient strategies that would improve patients’ health outcomes, and to design and prioritize clinical research in limited-resource settings.

My research interests

Decision-analytic modeling

I have developed several decision-analytic models to simulate clinical benefits, harms and cost-effectiveness of different interventions for various health conditions, such as breast cancer, psoriasis and peanut allergy. Software I use include R, Excel with VBA, and Amua. I am currently learning C++ and Python. When working as a Senior Health Economist, I also adapted a large number of models for various diseases to Norwegian settings.

Health economic evaluation

I have performed a number of health economic evaluations, both in the US settings as part of my research projects and in the Norwegian settings as part of my work with Health Technology Assessment(HTA)/Single Technology Assessment (STA) submissions. Disease areas I have worked with include various types of cancer, immunological disorders, blood disorders and other.

Cancer, precision medicine, genetics

I have built a portfolio of policy-relevant research concerning cancer, genomics, and precision medicine. More specifically, my research has been focusing predominantly on cancer (e.g., breast cancer, colorectal cancer). When formalizing the iterative decision-making framework, I became aware of the challenges associated with the assessment of cost-effectiveness and decision making in precision medicine. Thus, for my post-doctoral training, I moved to Harvard University to gain new skills and expertise in the area of genomics and precision medicine. More specifically, I was awarded Thomas O. Pyle Fellowship and joined the Precision Medicine Treatment (PreEMPT) Modeling team that works on simulating short- and long-term clinical benefits and estimating the cost-effectiveness of integrating different genome screening strategies into clinical care for healthy or high-risk newborns for a wide variety of heritable conditions.

Value of information analysis

An essential part of my work focuses on the use of value of information (VOI) analysis to assess and quantify decision uncertainty. I have published several application and methodological papers in this area. I have also held several short courses, seminars and workshops on VOI. Further, I co-founded and have co-led the Collaborative Network for Value of Information (ConVOI). As part of ConVOI, I work on removing the barriers to using VOI analysis in practice, providing training and guidance on the use of VOI, and develop methodological solutions to improve the application of VOI.

Evidence & uncertainty

In my PhD work, I formalized an iterative decision-making framework that highlights the central role of evidence in the process of health decision analysis. This evidence drives the iteration in the decision-making process. Consequently, the proposed framework emphasizes the importance of including a VOI analysis as a part of the decision analysis to evaluate decision uncertainty, assess whether gathering new evidence would be worthwhile, and identify the optimal designs of research.

Health disparities

My research interests also include health disparities. Some of my previous research projects examined regional variation in healthcare resource use. Further, I am part of PreEMPT II grant application at Harvard University. If funded, I will lead one of the three grant aims focusing on the evaluation of newborn screening when accounting for racial disparities. This aim will also involve the use of VOI to potentially reduce racial disparities and improve health equity by guiding decision makers on how to efficiently invest their limited resources to improve available evidence on genetic screening in underrepresented groups.


Selected publications