Artificial Intelligence in Healthcare Finance: Economic Evaluation Insights, Cost Savings, and Evidence Gaps

The Impact of Artificial Intelligence on Financial Systems in Healthcare: A Systematic Review of Economic Evaluation Studies

Abstract

Artificial intelligence (AI) is changing healthcare by addressing rising costs and inefficiencies. Despite its promise, evidence on AI's financial benefits remains scattered due to varying study methods and inconsistent reporting. This review consolidates economic evaluations of AI in healthcare, focusing on cost savings, efficiency improvements, and cost-effectiveness, while highlighting gaps in current research.

Following PRISMA 2020 guidelines, six relevant studies out of 341 records were analyzed. These studies demonstrated notable cost savings—for example, eliminating over 45,000 unnecessary diagnostic tests in 45 days and reducing Medicaid spending by up to USD 12.9 million annually. AI also improved cost-effectiveness, though some clinical trade-offs appeared.

Methodological weaknesses were common, including unclear evaluation perspectives, lack of sensitivity analyses, and minimal discussion of ethical concerns. Half of the studies showed moderate risk of bias. AI holds potential to improve financial sustainability in healthcare, but standardized economic evaluation frameworks and long-term studies are needed to support reliable and equitable AI adoption.

Introduction & Background

Healthcare costs continue to rise globally, prompting interest in AI solutions to improve resource use and reduce waste. AI tools—ranging from predictive models for hospital readmissions to diagnostic aids—offer promising ways to enhance efficiency and cost-effectiveness. However, evidence about their true financial impact is inconsistent and fragmented.

Most existing studies focus on AI’s clinical accuracy rather than its economic effects. The diversity of evaluation methods and rapidly evolving AI technologies contribute to this fragmented evidence. This review synthesizes peer-reviewed economic evaluations from various regions, examining AI’s influence on cost savings and efficiency in healthcare.

Understanding AI’s financial impact is crucial as health systems face budget constraints and value-based care demands. This review consolidates current knowledge, identifies methodological gaps, and guides future economic assessments, aiming to help healthcare leaders make informed decisions about AI investments.

Review

Methodology

This review followed PRISMA 2020 guidelines to ensure transparency. It targeted economic evaluations of AI in healthcare, including cost savings, cost-effectiveness, and return on investment.

Eligibility criteria included studies focusing on AI applied to clinical or administrative healthcare tasks, with comparisons to standard care or alternative methods. Studies had to report financial outcomes and be peer-reviewed, English-language publications.

The search spanned five databases: PubMed/MEDLINE, Embase, Scopus, Web of Science, and EconLit, using tailored search strings combining AI, healthcare, and economic evaluation terms. Gray literature was excluded to focus on peer-reviewed research.

Two independent reviewers screened and selected studies, resolving conflicts through discussion. Data extraction covered study details, economic methods, outcomes, and risk of bias. Due to diverse study designs and outcomes, a narrative synthesis was applied instead of meta-analysis.

Methodological quality was assessed with the Quality of Health Economic Studies (QHES) tool, categorizing studies by risk of bias. Although this review used published data only, it considered ethical implications of AI-driven cost changes.

Results

The initial search identified 341 records. After removing duplicates and applying eligibility criteria, six studies were included. These studies covered AI applications in different countries (USA, Germany, Netherlands, Turkey, Zambia) and healthcare settings such as hospitals, outpatient clinics, and Medicaid services.

• Cost Savings and Efficiency: AI reduced unnecessary diagnostic tests by over 45,000 in 45 days, saving thousands of dollars in a hospital lab. Preventive dental care guided by AI decreased Medicaid expenditures by up to USD 12.9 million annually. AI models also improved hospital readmission predictions, potentially lowering related costs.
• Cost-Effectiveness and Clinical Outcomes: Some AI interventions reduced costs while slightly compromising clinical outcomes, such as personalized depression treatment that cut costs by 5.4% but had a 2% decline in effectiveness. Other studies showed AI boosted both financial and clinical results, like optimizing audit processes in Zambia and improving readmission risk prediction.
• Methodological Limitations: Diverse methodologies and inconsistent reporting limited generalizability. Some studies lacked clear economic perspectives or sensitivity analyses. No study adequately addressed ethical or equity issues related to AI’s financial impact.
• Risk of Bias: Half the studies had low risk of bias, while others had moderate concerns due to unclear methods or missing analyses.

Summary of Included Studies

• Risk Prediction (USA): Deep learning models outperformed traditional methods, showing potential to reduce readmissions and costs within 30 days post-discharge.
• Preventive Dental Care (USA): Machine learning clustered utilization patterns, revealing significant Medicaid savings over seven years from preventive treatments.
• Performance Verification (Zambia): Random Forest algorithms enhanced cost-effectiveness in auditing health clinics.
• Depression Treatment (Germany, Netherlands): Personalized AI recommendations offered cost savings with minor clinical outcome trade-offs.
• Diagnostic Test Optimization (Turkey): AI software cut unnecessary tests significantly, yielding substantial short- and projected long-term cost savings.
• Readmission Risk Prediction (USA, China): Ensemble machine learning improved cost-effectiveness in managing readmission risks within 90 days.

Discussion

The evidence confirms AI’s potential to reduce healthcare costs and improve efficiency, but benefits vary by context and application. For example, AI-driven diagnostic test optimization eliminated tens of thousands of unnecessary tests quickly, while AI-guided preventive dental care saved millions annually. These savings align with other reports showing AI reducing redundant procedures across various specialties.

Cost-effectiveness findings highlight a trade-off between clinical outcomes and cost savings in some cases. Small declines in health outcomes may be acceptable when paired with meaningful cost reductions, but this balance requires careful consideration. Other AI applications improved both financial and clinical results, demonstrating that AI can support better resource allocation.

Methodological inconsistencies remain a barrier to drawing firm conclusions. Many studies lacked transparency about their economic evaluation perspective, sensitivity analyses, and did not address ethical concerns such as equity or bias. The omission of ethical discussion is especially concerning given AI’s potential to impact vulnerable populations disproportionately.

The heterogeneity of AI applications and healthcare settings complicates comparisons across studies. Standardized economic evaluation frameworks are needed to improve consistency and support better decision-making.

Future research should focus on long-term evaluations, include stakeholder perspectives, and ensure transparent reporting. This will help healthcare leaders weigh AI investments carefully and align innovation with fiscal responsibility.

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