Leveraging Artificial Intelligence and Machine Learning for Healthcare Cost-Effectiveness

Healthcare systems worldwide face immense pressure from rising costs and operational inefficiencies. This Research Topic focuses on the transformative potential of artificial intelligence (AI), including machine learning (ML), deep learning (DL), generative AI, natural language processing, and statistical learning, to drive data-driven decision-making for enhanced cost-effectiveness. While AI holds great promise, turning theoretical breakthroughs into practical, scalable solutions that clearly lower costs and improve operational efficiency continues to be a major challenge. We explore how specific AI/ML methodologies can be applied to measurably improve cost-effectiveness in healthcare. Submissions should provide actionable insights for healthcare providers, administrators, insurers, and policymakers, with a strong emphasis on real-world applicability and scalability.

This Research Topic aims to curate and showcase high-quality research that bridges the gap between theoretical AI/ML advancements and practical, cost-saving healthcare applications. We seek submissions demonstrating validated, measurable improvements in efficiency, cost reduction, or value generation while maintaining or enhancing the quality and safety of patient care.

Submissions may employ a wide range of AI/ML methodologies to address healthcare cost-effectiveness. We are particularly interested in, but not limited to, the following research areas:

1. Predictive Modeling, Statistical and Machine Learning

• Patient volume forecasting (time-series analysis, regression) for dynamic staffing.

• Risk stratification (XGBoost, logistic regression) to reduce preventable readmissions.

• Staff turnover/absenteeism prediction (survival analysis, ensemble models).

• Predicting patient churn or disengagement (classification, survival analysis) to enable targeted retention strategies and reduce associated costs.

• Modeling patient engagement and adherence patterns (clustering, regression) to optimize outreach and support programs, improving outcomes and long-term value.

2. Generative AI

• Automating administrative tasks (LLMs for billing, claims, clinical documentation).

• Synthetic data generation (GANs, diffusion models) for rare-event cost modeling.

3. Deep Learning & Anomaly Detection

• Fraud/waste detection (graph neural networks, autoencoders) in insurance claims.

• Equipment/utilization optimization (CNNs for OR/imaging suite efficiency).

4. Natural Language Processing (NLP)

• Patient experience analysis (sentiment analysis on feedback to reduce churn costs and improve patient satisfaction).

• Prior authorization automation (NLP to cut approval delays)

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: healthcare systems, health economics, cost-effectiveness, artificial intelligence, machine learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.