AI, Digital Welfare, and Equity in Healthcare Access

• In Event: Social Equity in Health Care: Policy, Access, and Digital Systems

Abstract

The integration of artificial intelligence (AI) into healthcare delivery is increasingly concentrated at key access points, particularly in triage and telehealth systems. While these technologies are intended to improve efficiency and patient flow, their implications for equitable access to care remain insufficiently understood. As healthcare systems increasingly rely on digital entry points, understanding how these technologies shape access has become critical for both policy design and service delivery, particularly in systems facing growing demand and resource constraints. This paper examines how AI-enabled triage and telehealth systems shape access to healthcare within digitally mediated service environments.The study assesses how these systems influence early-stage decision-making processes, including patient prioritization, intake, and remote consultation, and evaluates whether their implementation produces differential access across populations. Using a mixed-methods design, the analysis combines administrative healthcare data (e.g., hospital- and system-level data) with system-level evaluation and case-based evidence from digital health deployments. Quantitative analyses examine patterns in access and utilization across demographic and socioeconomic groups, while qualitative evidence assesses system design, implementation practices, and user interaction with digital interfaces.Preliminary findings indicate that although AI-enabled systems can improve efficiency at the point of entry, their benefits are not evenly distributed. Populations with limited digital access, lower system navigation capacity, or constrained resources experience diminished gains and, in some cases, additional barriers to care. These disparities highlight the risk that digital innovation, if not carefully designed and implemented, may reinforce existing inequities rather than reduce them.Preliminary findings further suggest that AI-driven access points should be governed as part of a broader healthcare access infrastructure. This includes attention to how systems are designed, implemented, and monitored over time, as well as how they interact with existing institutional constraints and population-level differences. These findings have direct implications for policymakers and healthcare organizations seeking to align digital transformation efforts with equity goals, particularly in the design, implementation, and oversight of AI-enabled systems.

Author

• Imen Ameur, Hult International Business School
  Presenting Author