Algorithmic Decision-Making In Healthcare Systems: Equity, Transparency, And Policy Implications

Abstract

Predictive algorithms are already influencing resource allocation, clinical decision-making, and health policy-making across the globe, with high potential for computational technologies to promote equity, efficiency, and evidence-based governance in healthcare organizations. In the absence of algorithmic bias controls‚ structural transparency‚ and accountability‚ these systems can perpetuate historic forms of discrimination and diminish public trust in data-enabled governance of health systems․ This forum article reviews how predictive analytics can be used as a tool to promote more equitable, accountable, and fairer governance of health systems in three domains: resource allocation/geographic equity; cost control/quality; and transparency, accountability, and trust to build long-term institutional legitimacy. Together, the health economics, public health ethics and computational science evidence suggests that algorithmic systems can improve access, lower avoidable costs, and support better policy choices if policy makers commit to the principles of equity monitoring‚ multi-stakeholder governance and continuous evaluation of the distributional impacts of algorithmic systems․ Any institution that implements algorithmic technologies should implement measurable equity commitments‚ technical and process-level transparency‚ participatory design methodologies‚ and institutional accountability mechanisms as a standard to ensure the use of these technologies to support social progress rather than social inequalities.