Hastane İç Lojistiğinde Tıbbi Malzeme Akışlarının Operasyonel Yük ve Talep-Karşılama Süresi Açısından Analizi

Öz

Bu çalışma, bir üniversite hastanesinin ana tıbbi malzeme deposundan klinik servislere yönelen malzeme akışlarını operasyonel yük ve talep-karşılama süresi açısından incelemektedir. Hastane bilgi yönetim sisteminden elde edilen 57.542 işlem kaydı, veri temizleme ve istek-servis düzeyinde yapılandırma sonrasında 55.985 analiz kaydı üzerinden değerlendirilmiştir. Çalışmada ürün çeşitliliği, toplam miktar, hasta yükü, servis grubu, talep-karşılama süresi ve uzun talep-karşılama riski birlikte ele alınmıştır. Talep-karşılama süresi sağa çarpık dağılım göstermiş; ortalama süre 194,81 dakika, medyan 93 dakika, P90 eşiği 493 dakika ve P95 eşiği 803 dakika olarak hesaplanmıştır. P90 riski en yüksek yoğun bakım grubunda (%21,06), P95 uç gecikme riski ise hemato-onkoloji/kök hücre grubunda (%9,75) görülmüştür. Lojistik regresyon modellerinde ROC-AUC değerleri P90 için 0,8308, P95 için 0,8336 bulunmuştur. Bulgular, hastane iç lojistiğinde servis bazlı, veri temelli karar desteğinin depo iş gücü planlaması ve talep önceliklendirme açısından önemli olduğunu göstermektedir.

Anahtar Kelimeler

• Hastane iç lojistiği
• tıbbi malzeme yönetimi
• talep-karşılama süresi
• operasyonel yük
• lojistik regresyon.

Analysis of Medical Supply Flows in Hospital Internal Logistics in Terms of Operational Workload and Request Fulfillment Time

Öz

This study examines medical supply flows from the central medical supply warehouse of a university hospital to clinical services in terms of operational workload and request-fulfillment time. A total of 57,542 transaction records obtained from the hospital information management system were evaluated through 55,985 analysis records after data cleaning and restructuring at the request-service level. Product variety, total quantity, patient load, service group, request-fulfillment time and long request-fulfillment risk were considered together. Request-fulfillment time showed a right-skewed distribution; the mean time was 194.81 minutes, the median was 93 minutes, the P90 threshold was 493 minutes and the P95 threshold was 803 minutes. The highest P90 risk was observed in the intensive care group (21.06%), whereas the highest P95 extreme delay risk was identified in the hemato-oncology/stem cell group (9.75%). In the logistic regression models, ROC-AUC values were 0.8308 for P90 and 0.8336 for P95. The findings indicate that service-based, data-driven decision support is important for warehouse workforce planning and request prioritization in hospital internal logistics.

Anahtar Kelimeler

• Hospital internal logistics
• medical supply management
• request fulfillment time
• operational workload
• logistic regression

Kaynakça

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