Optimizing Budget Allocation for Digital Health Investments Using Metaheuristic Algorithms: A Cost–Impact Analysis for Public Health Systems
Yazarlar (5)
Prof. Dr. Faruk DAYI Kastamonu Üniversitesi, Türkiye
Doç. Dr. Aylin Erdoğdu İstanbul Arel Üniversitesi, Türkiye
Prof. Dr. Yusuf Esmer Bayburt Üniversitesi, Türkiye
Prof. Dr. Ferah Yıldız Kocaeli Üniversitesi, Türkiye
Farshad Ganjı İstanbul Aydın Üniversitesi
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Healthcare (Q1) | ||
| Dergi ISSN | 2227-9032 Dergi Bilgileri (2026) | ||
| Dergi Tarandığı Indeksler | SSCI | ||
| Makale Dili | Türkçe | Basım Tarihi | 06-2026 |
| Cilt / Sayı / Sayfa | 14 / 11 / 1–31 | DOI | 10.3390/healthcare14111540 |
| Makale Linki | https://doi.org/10.3390/healthcare14111540 | ||
| UAK Araştırma Alanları |
İşletme Finansı
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| 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 … |
| Anahtar Kelimeler |
| health economics | digital health investment | public health system | cost–impact optimization | fuzzy logic | genetic algorithm | AICOT |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |
| Google Scholar | 1 |