Kamu Hastanelerinin Performansının Karar Ağacı Algoritmaları İle Değerlendirilmesi

Yıl 2025, Cilt: 59 Sayı: 1, 133 - 142, 22.01.2025

Keziban Avcı

https://doi.org/10.51551/verimlilik.1494277

Öz

Amaç: Bu çalışma, Türk sağlık sektöründeki kamu hastaneleri için yapısal, operasyonel ve HVI ölçümleri yoluyla hesaplanan bir dizi finansal performans göstergesinin çeşitli karar ağacı algoritmaları kullanılarak değerlendirilmesini amaçlamaktadır.
Yöntem: Çalışma üç aşamadan oluşmaktadır. İlk aşamada hastanelerin mali tablolarından oran analizi yöntemi kullanılarak finansal rasyolar hesaplandı. İkinci aşamada HVI'nın hesaplanmasında bu oranlar kullanıldı. Üçüncü aşamada seçilen operasyonel ve finansal göstergeler karar ağacı algoritmaları ile analiz edildi. Operasyonel ve finansal göstergelerin tahmin edilmesi ve karar ağaçlarının performans değerlendirmesi için ID3, C4.5 ve CART karar ağacı algoritmaları ve AUC kullanıldı.
Bulgular: ID3 algoritması kullanılarak oluşturulan karar ağaçlarının diğer algoritmalara göre daha yüksek performans sergilediği görülmüştür (AUC=0,93). Araştırma sonuçlarına göre yatak sayısı kamu hastanelerinin operasyonel ve finansal performansını anlamlı düzeyde yordamakta olup hastane ölçeği ile açıklanabilmektedir. Ayrıca operasyonel ve finansal performans göstergeleri ile eğitim durumu arasında da güçlü bir ilişki bulunmuştur.
Özgünlük: Çalışma, ID3 karar ağacı algoritmasının kamu hastanelerinin performansını tahmin etmedeki etkinliğini göstermesi açısından orijinaldir.

Anahtar Kelimeler

Finansal performans, Operasyonel performans, Kamu hastaneleri, Karar ağaçları, K-ortalamalar

Evaluation of Public Hospitals' Performance with Decision Tree Algorithms

Öz

Purpose: The study aims to evaluate a range of financial performance indicators calculated through structural, operational, and HVI measures for public hospitals in the Turkish healthcare sector using various decision tree algorithms.
Methodology: The study comprises threa phases. In the first phase, financial ratios were calculated from the hospitals' financial statements using the ratio analysis method. In the second phase, these ratios were used to calculate the HVI. In the third phase, the selected operational and financial indicators were analyzed with decision tree algorithms. The ID3, C4.5 and CART decision tree algorithms and AUC were used for predicting operational and financial indicators and performance assessment of decision trees.
Findings: It has been observed that decision trees created using the ID3 algorithm exhibit higher performance compared to other algorithms (AUC = 0.93). According to the results of the study, the number of beds significantly predicts the operational and financial performance of public hospitals and can be explained by the hospital scale. In addition, a strong relationship was found between operational and financial performance indicators with training status.
Originality: The study is original in demonstrating the effectiveness of the ID3 decision tree algorithm in predicting the performance of public hospitals.

Anahtar Kelimeler

Financial performance, Operational performance, Public hospitals, Decision trees, K-means

Kaynakça

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