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

MakaleAçık ErişimScopus SJRQ1
  • SDG 3 · Sağlık ve Kaliteli Yaşam
  • SDG 9 · Sanayi, Yenilikçilik ve Altyapı

Özet

Background: 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).

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https://rdlopen.rawdatalibrary.net/r/bayburt/11561Resmi atıflarda kullanılan kalıcı adres
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https://doi.org/10.3390/healthcare14111540Yayıncı resmi DOI adresi
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Tam Kayıt
AlanDeğerDil
dc.contributor.authorDayi, Faruk
dc.contributor.authorErdogdu, Aylin
dc.contributor.authorEsmer, Yusuf
dc.contributor.authorYildiz, Ferah
dc.contributor.authorGanji, Farshad
dc.date.accessioned2026-10-29T19:08:32.123183965Z
dc.date.available2026-10-29T19:08:32.123183965Z
dc.date.issued2026
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.articlenumber1540
dc.identifier.doi10.3390/healthcare14111540
dc.identifier.eissn2227-9032
dc.identifier.issn2227-9032
dc.identifier.issue11
dc.identifier.pmcidPMC13256940
dc.identifier.pmid42278795
dc.identifier.scopus2-s2.0-105041409944
dc.identifier.urihttps://rdlopen.rawdatalibrary.net/r/bayburt/11561
dc.identifier.volume14
dc.identifier.wosWOS:001789963000001
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofHealthcare (Switzerland)
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectAICOT
dc.subjectcost–impact optimization
dc.subjectdigital health investment
dc.subjectfuzzy logic
dc.subjectgenetic algorithm
dc.subjecthealth economics
dc.subjectpublic health system
dc.subject.classificationHealth Care Sciences & Services
dc.subject.classificationHealth Policy & Services
dc.subject.otheranalysis
dc.subject.otherArticle
dc.subject.otherartificial intelligence
dc.subject.otherclinical decision support system
dc.subject.othercost impact analysis
dc.subject.otherdigital health
dc.subject.otherelectronic health record
dc.subject.otherfuzzy system
dc.subject.othergenetic algorithm
dc.subject.otherhealth economics
dc.subject.otherhospital admission
dc.subject.otherhuman
dc.subject.othermetaheuristics
dc.subject.otherparticle swarm optimization
dc.subject.otherpublic health
dc.subject.otherquality adjusted life year
dc.subject.otherquality control
dc.subject.othersensitivity analysis
dc.titleOptimizing Budget Allocation for Digital Health Investments Using Metaheuristic Algorithms: A Cost–Impact Analysis for Public Health Systems
dc.typeArticle
rdl.citation.openalex1
rdl.citation.scopus1
rdl.citation.wos1
rdl.departmentFaculty of Economics and Administrative Sciences, Kastamonu University, 37160 Kastamonu, Türkiye
rdl.departmentFaculty of Economics and Administrative Sciences, İstanbul Arel University, 34295 İstanbul, Türkiye
rdl.departmentFaculty of Applied Sciences, Bayburt University, 69300 Bayburt, Türkiye
rdl.departmentFaculty of Management, Kocaeli University, 41350 Kocaeli, Türkiye
rdl.departmentSoftware Development Department, Istanbul Aydin University, 34295 İstanbul, Türkiye
rdl.oa.statusgold
rdl.oa.statusgreen
rdl.oa.urlhttps://doi.org/10.3390/healthcare14111540
rdl.scopus.quartileQ1
rdl.sdgSDG09
rdl.sdgSDG03
rdl.source.indexSCOPUS
rdl.source.indexWOS
rdl.source.indexPUBMED
rdl.wos.indexScience Citation Index Expanded (SCI-EXPANDED)
rdl.wos.indexSocial Science Citation Index (SSCI)