Social Learning in Lung Transplant Decision

TL;DR

This paper investigates how social learning influences lung transplant decisions, showing that information sharing affects organ acceptance, allocation efficiency, and patient utility, with policy implications for optimizing transplant outcomes.

Contribution

It introduces a structural model to quantify social learning effects in lung transplant allocation and evaluates policy impacts on efficiency and utility.

Findings

Blinding patients to their sequence position increases organ allocation but lowers utility.

Prioritizing by acceptance likelihood intensifies social learning, reducing allocations but increasing utility per organ.

Social learning significantly influences decision-making and policy outcomes in lung transplants.

Abstract

We study the allocation of deceased-donor lungs to patients in need of a transplant. Patients make sequential decisions in an order dictated by a priority policy. Using data from a prominent Organ Procurement Organization in the United States, we provide reduced-form evidence of social learning: because patients accept or reject organs in sequence, their decisions exhibit herding behavior, often rejecting an organ that would otherwise be accepted. We develop and estimate a structural model to quantify the impact of various policy proposals and informational regimes. Our results show that blinding patients to their position in the sequence\textemdash thereby eliminating social learning\textemdash boosts organ allocation but reduces average utility per patient. In contrast, prioritizing patients by their likelihood of acceptance exacerbates social learning, leading to fewer organ allocations. Nevertheless, it raises utility per accepted organ and expedites the allocation process.