Sağlık Kaynaklarına ve Hasta Bekleme Sürelerine Dayalı Hastane Maliyetlerini ve Gelirlerini Tahmin Etmek için Bir Makine Öğrenmesi Çerçevesi

Öz

Sağlık kaynaklarının ve hasta bekleme sürelerinin etkin yönetimi, sağlık kurumlarında operasyonel verimlilik ve finansal sürdürülebilirlik açısından kritik öneme sahiptir. Bu çalışma, beş temel sağlık kaynağının (hekimler, hemşireler, memurlar, muayene odaları ve triyaj alanları) ve hasta bekleme süresinin etkisini modelleyerek toplam maliyet ve geliri birlikte tahmin etmeye yönelik makine öğrenmesi (ML) tabanlı bir çerçeve önermektedir. Simüle edilmiş bir veri seti kullanılarak üç regresyon tabanlı ML algoritması—Kısmi En Küçük Kareler (PLS), Rastgele Orman (RF) ve Gradyan Artırma (GB)—uygulanmış ve karşılaştırılmıştır. GB algoritması, maliyet ve gelir tahmininde en yüksek performansı göstermiş; sırasıyla 0,981 R² değerleri ile en düşük Ortalama Mutlak Yüzde Hata (MAPE) değerleri olan %1,869 ve %1,913’e ulaşmıştır. Bulgular, GB gibi doğrusal olmayan modellerin hastane operasyonları ile finansal sonuçlar arasındaki karmaşık ilişkileri daha iyi yakaladığını ortaya koymaktadır. Bu çalışma, veri odaklı yaklaşımların hastane yönetiminde stratejik karar alma süreçlerini destekleme potansiyelini vurgulamakta; kaynak tahsisi, maliyet kontrolü ve gelir artırma konularında öngörü doğruluğu ve operasyonel içgörü sağlamaktadır.

Anahtar Kelimeler

• Sağlık kaynakları
• bekleme süresi
• makine öğrenmesi
• maliyet
• gelir.

A Machine Learning Framework for Predicting Hospital Costs and Revenues Based on Healthcare Resources and Patient Waiting Times

Öz

Effective management of healthcare resources and patient waiting times is critical for operational efficiency and financial sustainability in healthcare institutions. This study proposes a machine learning (ML)-based framework for jointly estimating total cost and revenue by modeling the impact of five key healthcare resources (physicians, nurses, clerks, exam rooms, and triage areas) and patient waiting time. Three regression-based ML algorithms—Partial Least Squares (PLS), Random Forest (RF), and Gradient Boosting (GB)—were applied and compared using a simulated dataset. The GB algorithm demonstrated the best cost and revenue estimation performance, with R² values of 0.981 and the lowest MAPEs of 1.869% and 1.913%, respectively. The results indicate that non-linear models, such as GB, better capture the complex relationships between hospital operations and financial outcomes. This study highlights the potential of data-driven approaches to support strategic decision-making in hospital management, offering predictive accuracy and operational insight into resource allocation, cost containment, and revenue enhancement.

Anahtar Kelimeler

• Healthcare resources
• waiting time
• machine learning
• cost
• revenue.

Kaynakça

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