“Patients Aren’t Datasets”: Generating Return on Investment via Automation, Responsibly

Abstract

Healthcare spending in the United States continues to rise as patient demand shows no signs of slowing. In response, health system executives have invested billions in technology modernization to boost efficiency and reduce costs through automation, such as machine learning models, generative AI models, deep learning models, and others. These efforts have improved access, enhanced outcomes, and standardized patient data collection, fueling advances in research and the development of future treatments. Now, the industry stands at a pivotal moment: how to responsibly leverage years of patient and outcomes data to train artificial intelligence (AI) to generate return on investment (ROI) through data monetization, accelerate care delivery, and lower costs while managing the risks and unintended consequences of IT adoption. This article explores how AI and other emerging technologies in healthcare demand critical evaluation of responsible data use, careful consideration of data privacy and compliance, and clear strategies for measuring the positive or negative unforeseen consequences of AI across different stakeholders.

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