Type of Presentation
Presentation on demand only
Location
Virtual
Description of Program
This research examines rising U.S. healthcare costs through economic theory and public administration, analyzing market failures, government interventions, and equity challenges. It evaluates how policies like the ACA, Medicare, and Medicaid address inefficiencies and explores how artificial intelligence can improve cost control, efficiency, and health outcomes while requiring responsible governance.
Abstract
This research paper analyzes rising healthcare costs in the United States through core economic concepts in public administration, including market failure, public goods theory, externalities, cost-benefit analysis, and price elasticity. These frameworks explain persistent inefficiencies such as information asymmetry between providers and patients, high administrative expenses, limited price transparency, and restricted competition among healthcare systems and pharmaceutical companies. The paper integrates the growing role of Artificial Intelligence (AI) as both a response to and driver of economic change in healthcare. AI technologies—including predictive analytics, automated claims processing, and clinical decision-support systems—have the potential to reduce administrative waste, improve diagnostic accuracy, and strengthen resource allocation. By lowering transaction costs and enhancing data transparency, AI may partially address information asymmetry and improve cost-benefit outcomes across public programs. However, AI also introduces new economic and governance challenges. High development costs, data concentration, and network effects may reinforce market consolidation among dominant firms. Additionally, algorithmic bias risks exacerbating health disparities, creating negative externalities that disproportionately affect marginalized populations. These concerns highlight the need for regulatory oversight and equity-centered implementation. The paper evaluates major government interventions such as the Affordable Care Act, Medicare, and Medicaid, assessing how these policies address structural market failures and how AI integration within public programs may enhance efficiency and fraud prevention. Drawing on lessons from the COVID-19 pandemic and prescription drug pricing debates, the analysis concludes that while AI offers cost-containment potential, meaningful reform requires coordinated economic policy, competition safeguards, and ethical governance to achieve a more equitable and sustainable healthcare system.
Faculty / Staff Sponsor
Dr. Natalia Ermasova
Presentation File
wf_yes
Included in
Economic Analysis on Healthcare Costs with AI
Virtual
This research paper analyzes rising healthcare costs in the United States through core economic concepts in public administration, including market failure, public goods theory, externalities, cost-benefit analysis, and price elasticity. These frameworks explain persistent inefficiencies such as information asymmetry between providers and patients, high administrative expenses, limited price transparency, and restricted competition among healthcare systems and pharmaceutical companies. The paper integrates the growing role of Artificial Intelligence (AI) as both a response to and driver of economic change in healthcare. AI technologies—including predictive analytics, automated claims processing, and clinical decision-support systems—have the potential to reduce administrative waste, improve diagnostic accuracy, and strengthen resource allocation. By lowering transaction costs and enhancing data transparency, AI may partially address information asymmetry and improve cost-benefit outcomes across public programs. However, AI also introduces new economic and governance challenges. High development costs, data concentration, and network effects may reinforce market consolidation among dominant firms. Additionally, algorithmic bias risks exacerbating health disparities, creating negative externalities that disproportionately affect marginalized populations. These concerns highlight the need for regulatory oversight and equity-centered implementation. The paper evaluates major government interventions such as the Affordable Care Act, Medicare, and Medicaid, assessing how these policies address structural market failures and how AI integration within public programs may enhance efficiency and fraud prevention. Drawing on lessons from the COVID-19 pandemic and prescription drug pricing debates, the analysis concludes that while AI offers cost-containment potential, meaningful reform requires coordinated economic policy, competition safeguards, and ethical governance to achieve a more equitable and sustainable healthcare system.