Deceased donor organs in the United States are absolutely scarce - demand for transplantation greatly exceeds the supply of donors, and thousands of patients die on the waitlist each year. Deceased donor organ allocation policies take on the complicated task of distributing the limited supply of organs to the large pool of waiting candidates. Our Healthcare Allocation (HCA) Lab applies advanced empirical methods to evaluate and design organ allocation systems according to the underlying ethical principles.

To learn more, visit our website: https://voices.uchicago.edu/healthallocate/

The HCA Lab has multiple ongoing projects across major organs. Below are the specific aims of a few examples:

1. Heart: Design a novel medical urgency score for candidates on the heart waiting list. The current heart allocation system relies on subjective treatment choices to rank order candidates on the waitlist, sorting them into categorical statuses. Our lab is working to develop an objective continuous score that is resistant to manipulation and better discriminates between levels of patient mortality risk.

2. Liver: Determine potential disparities in access to deceased donor liver transplant based on citizenship status (Non-citizen Non-residents who travel to the US for the express purpose of transplant vs. all other candidates). This project aims to determine whether these patients experience significantly higher transplant rates and time to transplant, and whether they are more likely to receive exceptions that boost their priority for transplant.

3. Kidney: Compare the net benefit curves of a donor kidney survival model that includes recipient characteristics to the existing model, the Kidney Donor Profile Index (KDPI). This method introduces decision curve analysis to the space of organ transplant as a novel way to evaluate the clinical utility of different allocation models.

All projects will use the Scientific Registry of Transplant Recipients, a complete national registry of all US candidates, donors, and recipients. There are three principal empirical methodologies the lab employs.

1. Policy evaluation with advanced causal inference techniques from health services research.

2. Machine learning to design novel allocation scores.

3. Simulation modeling to evaluate the potential impact of novel allocation policies.

In parallel to these empirical techniques, the lab relies on a deep philosophical understanding of the ethical principles that guide the allocation of scarce healthcare resources.

Most projects use the open-source version of R with the RStudio IDE. Machine learning projects are typically in python. We also occasionally use Stata to estimate specific models. All software is either free or provided by the mentor.

International Society for Heart and Lung Transplantation

American Society of Transplantation conferences (World Transplant Congress, American Transplant Congress)

American Society for Bioethics and Humanities

MacLean Conference on Clinical Medical Ethics

American Thoracic Society International Conference