2026· International Journal of Artificial Intelligence, Data Science, and Machine Learning· Vol 7, pp. 1-11· 0 citations
TL;DR
It is argued that AI can generate substantial national value when deployed in high-volume, high-cost, and prevention-oriented services and offers policymakers a practical basis for prioritizing responsible AI investments that improve both healthcare efficiency and long-term patient outcomes.
Abstract
Artificial intelligence (AI) is increasingly being adopted across healthcare systems to improve diagnostic accuracy, streamline administrative processes, strengthen clinical decision-making, and support preventive care. However, its national economic implications remain insufficiently understood, particularly regarding healthcare cost savings, workforce productivity, and long-term population-health outcomes. This study develops an evidence-informed national economic modelling framework to estimate the potential impact of AI adoption across key healthcare functions, including diagnostic imaging, automated screening, clinical decision support, predictive risk management, telehealth, and administrative automation. The framework compares current healthcare delivery with low-, moderate-, and high-adoption AI scenarios over a ten-year period. Economic outcomes include direct medical cost savings, reduced avoidable hospitalizations, improved diagnostic efficiency, clinician and administrative time savings, increased service capacity, and long-term health benefits measured through avoided complications and quality-adjusted life years. The analysis also incorporates implementation, maintenance, workforce-training, data-infrastructure, and governance costs to estimate net economic value. The study argues that AI can generate substantial national value when deployed in high-volume, high-cost, and prevention-oriented services. Nevertheless, financial benefits depend on interoperable health-data systems, clinical integration, algorithmic accuracy, human oversight, equity safeguards, and continuous performance monitoring. The proposed framework offers policymakers a practical basis for prioritizing responsible AI investments that improve both healthcare efficiency and long-term patient outcomes.
An AI Productivity Index is proposed to complement existing safety, efficacy and economic assessments by evaluating operational impact, implementation burden, opportunity costs and post-deployment consequences.
Y. Al-Ajlouni, Basile Njei· Clinical medicine (London)· 0 citations
Predictive analytics can reduce pressure on high-cost healthcare segments when care teams connect risk estimates with concrete intervention pathways. The paper analyzes prospective risk stratification, skilled nursing facility transition management, readmission surveillance, and automated quality reporting as linked mechanisms of cost control. The aim is to define approaches for moving from retrospective expenditure review to prospective care-routing decisions. The materials include nine peer-reviewed studies and one official CMS measurement report published during the last five years. Comparative source analysis, conceptual synthesis, typologization, and analytical generalization guide the review. The paper argues that the high-cost burden declines when payers and providers use claims, electronic health record (EHR), registry, facility, and quality-measure data to identify high-risk patients, select safer alternatives to costly settings, and monitor outcomes after intervention. The proposed logic suits payer, provider, and value-based care environments where skilled nursing facility (SNF) placement, readmission exposure, and reporting workload shape financial risk.
Sachin Bajpai· Universal Library of Innovat...· 0 citations
Abstract Objectives Diagnostic error is common, harmful, and costly, yet most health systems lack active interventions to improve diagnostic safety. This study aimed to identify scalable care models that advance diagnostic excellence while reducing costs for healthcare systems across diverse delivery and reimbursement environments. Methods We employed a multi-method care model development framework that integrated: (1) a literature review of 1,632 sources, (2) 19 semi-structured expert interviews, (3) in-depth analysis of four exemplar programs, and (4) four iterative refinement cycles with a 12-member cross-institutional expert panel. Candidate interventions were evaluated by diagnostic failure points, potential net cost savings, operational feasibility, stakeholder value alignment, and payment-model fit. Results We identified three highest value care model archetypes. (1) Diagnostic safety nets identify patients with abnormal findings lacking appropriate follow-up and re-engage them before harm escalates. (2) Optimized navigation routes patients to the right level of care through risk stratification and selective specialist input. (3) Decision support strengthens diagnostic reasoning at the point of care through evidence-based diagnostic tools. These archetypes differed in infrastructure requirements and financial attractiveness across payment models, with diagnostic safety nets most broadly attractive across both fee-for-service and risk-bearing environments. Conclusions This framework introduces three diagnostic improvement archetypes that health systems can use to select and sequence interventions based on local failure points, operational feasibility, and reimbursement context. By linking intervention choice to real-world implementation conditions, the framework offers a pragmatic approach for advancing diagnostic excellence across diverse care delivery settings.
Sierra Stingl Sauter, R. Jarral, Yan Wang et al.· Diagnosis· 1 citation
The analysis considered the savings potential from full national implementation of three specific AI technologies - machine learning (ML), natural language processing (NLP), and generative AI (genAI) - across administrative and medical expense categories for payers and providers (but excluded onetime implementation costs).
Nikhil R Sahni, Kate O'Gorman, Rahul Agarwal et al.· NEJM catalyst innovations in...· 0 citations
Background AI is being introduced into clinical workforces during a critical transition toward integrated, value-based models of care, where its greatest promise lies in augmenting clinician judgment and expanding the reach of already strained healthcare teams. Yet clinical adoption remains limited because most AI systems lack reimbursement pathways, impose substantial implementation costs, and lack standardized mechanisms for integration into electronic health records (EHRs). These gaps create misalignment between technological capability and clinical usability. This paper identifies financial, regulatory, and workflow structures required for AI to operate safely, predictably, and sustainably across key domains of healthcare. Methods This narrative synthesis reviews clinical, economic, regulatory, and implementation-science literature from 2022 to 2025. Four domains were analyzed: (1) AI augmentation of clinical workflows; (2) reimbursement structures and CPT coding pathways; (3) EHR-based AI deployment and governance; and (4) economic and equity considerations for large-scale implementation. Sources included peer-reviewed reviews, white papers, consensus statements, and health policy analyses. Results AI tools demonstrated benefits in diagnostic accuracy, decision support, and documentation efficiency, particularly in radiology, cardiology, and EHR-integrated workflows. Adoption was hindered by absent reimbursement for clinician-reviewed AI outputs. Implementation and monitoring costs fell heavily on health systems, risking widened disparities. Additional concerns included accuracy, bias, generalizability, and limited oversight. Enabling conditions included clinician-in-the-loop review, auditable outputs, equity-centered validation, and alignment with evolving payment models. Conclusions AI can strengthen healthcare teams and advance value-based care, but scaling requires aligned incentives, rigorous evaluation standards, equity safeguards, and coordinated governance—echoing lessons from national EHR implementation.
Michael C. Changaris, Franca V. Niameh· Frontiers in Digital Health· 0 citations
Background Preventable adverse drug events (ADEs) remain a major source of hospital morbidity, mortality, and healthcare costs worldwide. Clinical decision support systems (CDSS) integrated into electronic health records (EHRs) were developed to reduce unsafe prescribing, yet evidence of their real-world effectiveness remains mixed. The emergence of artificial intelligence (AI) and machine learning (ML) offers new opportunities to enhance medication safety but also introduces risks such as algorithmic bias, technology-induced error, and reduced clinician vigilance. Objectives This narrative review critically examines: (1) evidence for the effectiveness of CDSS in reducing preventable ADEs; (2) human factors influencing interactions between clinicians and AI-enabled safety tools; and (3) conceptual, methodological, and governance challenges affecting the safe implementation of digital health technologies. Methods A structured narrative review was conducted using the SANRA framework and reported in accordance with PRISMA-ScR guidance where applicable. Searches of PubMed/MEDLINE, CINAHL, Embase, Scopus, and IEEE Xplore covered literature published between January 2015 and March 2024, supplemented by seminal earlier studies. Following eligibility screening, 75 studies were included in a thematic synthesis and quality appraisal using established risk-of-bias tools. Results Five themes emerged: the transition from passive to adaptive decision support; AI's dual role as both a safety enhancer and a source of new risks; persistent alert fatigue; the often-overlooked contribution of nursing vigilance; and gaps in equity, governance, and technology-induced error research. From these findings, we propose the Clinical Safety Intelligence Loop (CSIL), a conceptual framework that positions AI within a sociotechnical system while embedding equity, governance, and continuous feedback as core components. Conclusion Achieving medication safety improvements requires moving beyond technology-focused solutions toward systems-level approaches integrating AI, clinician cognition, organizational culture, and governance. The CSIL offers a useful framework for guiding this transformation, although further empirical validation is needed.
M. Alruwaili, U. Paul-Chima, Ugwu Chinyere Nneoma· Frontiers in Digital Health· 0 citations