The Impact of Artificial Intelligence on the Health Economy, Workforce Productivity, and Administrative Efficiency: A Systematic Review
Bibliographic record
Abstract
ABSTRACT Background Healthcare systems globally are under increasing financial and operational strain due to aging populations, rising expenditures, and workforce shortages. Amid these challenges, artificial intelligence (AI) has emerged as a promising tool to enhance system-level performance, particularly in cost reduction, productivity gains, and administrative efficiency. Objective This review aims to synthesize existing evidence on the macro and system-level impact of AI implementation across three key domains: the health economy, workforce productivity, and administrative efficiency. Methods A systematic review methodology was employed, allowing for the integration of diverse data sources and study types. Literature was systematically identified through PubMed and Google, covering publications from 2020 to July 2025. Studies were included if they evaluated AI’s impact on national or regional health expenditure, labor restructuring, or process efficiency. Thematic synthesis was guided by a conceptual framework modeling AI as a system-wide catalyst. Results Twenty-four studies were included. AI implementation demonstrated potential cost savings of 5–10% in national health expenditures, driven by automation in hospital operations and administrative processes. AI-supported interventions reduced diagnostic time by up to 90% and treatment costs by over 30% in specific applications, such as cancer diagnosis and radiotherapy. Administrative tools, including AI-assisted documentation and claims processing, achieved efficiency gains of up to 40%. However, reliance on simulated models, short-term studies, and single-center data limits generalizability. Conclusions AI presents significant potential to enhance health system efficiency and reduce costs. Real-world implementation studies, standardized outcome metrics, and robust governance frameworks are essential to validate these gains and ensure equitable, sustainable adoption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".