Information, learning, and drug diffusion
File(s)
Author(s)
Zhu, Zhengnan
Type
Thesis
Abstract
Advancements in technology and pharmaceutical innovations enhance medical care access, improve patient outcomes, and reduce healthcare costs. However, the diffusion of these innovations is often slow and uneven, leading to disparities that can adversely affect public health. This thesis examines the determinants of individual adoption decisions in the context of new pharmaceutical developments.
Chapter 2 examines the impact of a patient-sharing physician network on the prescription behaviours of generic versus branded drugs, using the universe of statin prescriptions in Finland from 2000 to 2008. The results show that the patient-sharing network positively affects the adoption of generic drugs. Higher connection intensity, measured by the number of patients exchanged between physicians, further increases physicians’ likelihood of prescribing generic drugs.
In Chapter 3, I develop and estimate a structural Bayesian learning model where physicians update their beliefs about the quality of generic drugs based on patient information signals. The estimation results show that physicians initially hold negative perceptions of the quality of generic drugs. Both the volume and composition of information signals influence generic adoptions. New patient signals from other physicians, despite bringing noises, eventually increase long-term drug diffusion by boosting physicians’ expectations about generic drug quality.
Chapter 4 evaluates the informational effect of generic substitution (GS) policy on the adoption of generic drugs. The analysis suggests that the effective communication of GS outcomes from pharmacies to physicians could more than double the generic adoption rates compared to relying solely on learning from prescription histories. The results highlight that successful policy implementation should focus on proactive early-stage promotion through the information channel.
Overall, this thesis highlights the role of information and learning in shaping adoption decisions, providing insights for policymakers to promote the diffusion of pharmaceutical innovations.
Chapter 2 examines the impact of a patient-sharing physician network on the prescription behaviours of generic versus branded drugs, using the universe of statin prescriptions in Finland from 2000 to 2008. The results show that the patient-sharing network positively affects the adoption of generic drugs. Higher connection intensity, measured by the number of patients exchanged between physicians, further increases physicians’ likelihood of prescribing generic drugs.
In Chapter 3, I develop and estimate a structural Bayesian learning model where physicians update their beliefs about the quality of generic drugs based on patient information signals. The estimation results show that physicians initially hold negative perceptions of the quality of generic drugs. Both the volume and composition of information signals influence generic adoptions. New patient signals from other physicians, despite bringing noises, eventually increase long-term drug diffusion by boosting physicians’ expectations about generic drug quality.
Chapter 4 evaluates the informational effect of generic substitution (GS) policy on the adoption of generic drugs. The analysis suggests that the effective communication of GS outcomes from pharmacies to physicians could more than double the generic adoption rates compared to relying solely on learning from prescription histories. The results highlight that successful policy implementation should focus on proactive early-stage promotion through the information channel.
Overall, this thesis highlights the role of information and learning in shaping adoption decisions, providing insights for policymakers to promote the diffusion of pharmaceutical innovations.
Version
Open Access
Date Issued
2024-08-15
Date Awarded
01/02/2025
License URL
Advisor
Miraldo, Marisa
Kosova, Renata
Publisher Department
Business School
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)