Health systems review of AI costs and human factors
A Sociotechnical Review of Algorithms in Health Systems: Technical, Cost, and Human-Centered Considerations
Human-Computer Interaction
Summary
AI is becoming common in healthcare to help with tasks like diagnosing diseases and advising doctors. The authors studied 114 papers to understand how these AI tools consider costs and the impact on people and organizations. They found that while AI aims to save money and improve efficiency, many studies don’t fully examine all related costs or human factors. The paper highlights where AI in healthcare needs better design that balances technical performance with real-world social and organizational effects.
What this means in practice
- •For health system administrators: Assess AI tool choices by their financial, computational, organizational, and social cost impacts for better investment decisions.
- •For healthcare quality teams: Incorporate human-centered and cost-aware considerations in evaluating AI tools to improve sustainable clinical workflows.
A survey. It maps existing work.
Authors
Victoria Chui, Kelly McConvey, Shion Guha
Abstract
Artificial intelligence (AI) applications in healthcare are becoming increasingly prevalent, to assist health systems, providers, and patients with tasks such as decision-making, risk prediction, and diagnosis. This increasing computational potential brings AI applications to the forefront of workplace decision making, often without full consideration of subsequent computational, organizational, and social costs. These applications are leveraged to reduce healthcare costs and increase efficiency of daily tasks, with model-related costs being considered at varying levels of granularity. To understand these trends, we critically analyze 114 papers to examine how cost-aware AI models have been developed for health systems. We explore the data, method, and outcome choices of these models, as well as their intersection with cost and human-centered concerns, highlighting the gaps in rigorous sociotechnical model design. From these trends, we define model costs and subsequent dimensions, presenting insight into those studies reporting financial, computational, organizational and/or social measures. Further, we critique the benefits and challenges of evaluating model-related costs and sustainability concerns when developing AI models for health systems.