Optimizing Budget Allocation for Digital Health Investments Using Metaheuristic Algorithms: A Cost-Impact Analysis for Public Health Systems

Basit Öğe Kaydı
dc.contributor.authorDayi, Faruk
dc.contributor.authorErdogdu, Aylin
dc.contributor.authorEsmer, Yusuf
dc.contributor.authorYildiz, Ferah
dc.contributor.authorGanji, Farshad
dc.date.accessioned2026-09-01T15:52:34Z
dc.date.available2026-09-01T15:52:34Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractBackground: In the era of digital transformation, public health systems increasingly rely on digital technologies to improve accessibility, efficiency, and patient outcomes. However, policymakers face significant challenges in allocating limited resources across competing digital health investments characterized by uncertainty and dynamic impacts. Methods: This study introduces the Adaptive Impact-Cost Optimization Theory (AICOT), a hybrid framework integrating fuzzy logic and genetic algorithms to optimize digital health investment portfolios. The model defines the Investment Priority Score (IPS) as a function of cost, expected impact, and implementation feasibility, enabling structured evaluation under uncertainty. A fuzzy inference system with centroid-based defuzzification is used to convert qualitative assessments into quantitative scores, while optimization techniques identify optimal portfolios across different fiscal scenarios. The empirical analysis covers 15 OECD countries (2018-2024) using publicly available datasets. Sensitivity analyses assess robustness under inflation, cost shocks, and changing system priorities. Results: The findings show that blended investment strategies combining routine digital health tools with pandemic-oriented infrastructures yield the highest resilience-adjusted efficiency. Results remain stable across sensitivity scenarios, with pandemic surveillance consistently ranking as a top priority even under increased cost conditions. The model effectively captures cross-country heterogeneity, demonstrating adaptability to different levels of digital maturity. Conclusions: AICOT provides a transparent and policy-relevant decision-support framework that improves resource allocation efficiency and reduces unnecessary expenditures. These contributions support long-term financial sustainability and align with global health objectives, including Universal Health Coverage and Sustainable Development Goal 3 (Good Health and Well-being).
dc.identifier.doi10.3390/healthcare14111540
dc.identifier.issn2227-9032
dc.identifier.issue11
dc.identifier.pmid42278795
dc.identifier.scopus2-s2.0-105041409944
dc.identifier.scopusqualityQ2
dc.identifier.urihttp://dx.doi.org/10.3390/healthcare14111540
dc.identifier.urihttps://hdl.handle.net/20.500.12403/8445
dc.identifier.volume14
dc.identifier.wosWOS:001789963000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofHealthcare
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260820
dc.subjectHealth Economics
dc.subjectDigital Health Investment
dc.subjectPublic Health System
dc.subjectCost-Impact Optimization
dc.subjectFuzzy Logic
dc.subjectGenetic Algorithm
dc.subjectAicot
dc.titleOptimizing Budget Allocation for Digital Health Investments Using Metaheuristic Algorithms: A Cost-Impact Analysis for Public Health Systems
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu