Turnaround Time Prediction for a Medical Laboratory Using Artificial Neural Networks

Yıl 2018, , 357 - 368, 30.10.2018

Mete Eminağaoğlu , Alper Vahaplar

https://doi.org/10.17671/gazibtd.444826

Cited By: 2

Öz

Turnaround
time (TAT) or duration between different stages in medical and healthcare
services is accepted to be one of the most significant performance measures
that can have a great impact on service quality, change management, costs, and
strategic decisions. Accurate and reliable prediction or estimation of the
turnaround times or elicitation of the underlying causes that affect TAT is
known to be a difficult problem. In this study, a heuristic prediction approach
is used by designing and implementing a special artificial neural network (ANN)
model in order to predict TAT of a specific process in a private hospital. The
prediction performance of our ANN model is comparatively analyzed with some
alternative linear and nonlinear numerical prediction algorithms. The results
show that ANN surpasses all of the other numerical prediction algorithms and
ANN might be used by the decision makers as a reliable model to estimate TAT
within acceptable error rates.

Anahtar Kelimeler

Turnaround time, Artificial neural networks, Numerical prediction, Medical laboratory, Health services

Yapay Sinir Ağları ile Tıbbi Laboratuvar için İşlem Süresi Kestirimi

Öz

Hastanelerde ve çeşitli sağlık hizmetlerinde tıbbi işlemler
/ aşamalar arasındaki işlem ya da geri dönüş süresi, hizmet kalitesi, değişim
yönetimi, maliyetlerin azaltılması ve stratejik kararlar üzerinde de etkisi olan
en önemli performans ölçütlerinden biri olarak kabul edilmektedir. Geri dönüş
sürelerinin doğru ve güvenilir tahmini ya da bu süreleri etkileyen etmenlerin
veya nedenlerin ortaya çıkarılması ise çözümü zor bir problemdir. Bu çalışmada,
özel bir hastanedeki çeşitli birimler arasındaki tıbbi iş süreçlerine ait
gerçek istatistiksel işlem süresi verileri kullanılarak iş bitirme sürelerinin
sayısal olarak tahmini için özel bir yapay sinir ağı (YSA) modeli tasarlanmış
ve kodlanmıştır. YSA modelimizin kestirim performansı, bazı alternatif doğrusal
/ doğrusal olmayan sayısal kestirim algoritmaları ile karşılaştırmalı olarak
analiz edilmiştir. YSA'nın tahmin başarısı ve hata değerleri açısından diğer
tüm sayısal kestirim algoritmalarından daha başarılı olduğu ve YSA'nın, tıbbi
iş süreçlerinde iş bitirme sürelerini kabul edilebilir hata oranlarında güvenilebilir
şekilde tahmin edebildiği ve karar destek sistemlerinde yöneticiler tarafından
alternatif bir model olarak kullanılabileceği ortaya konmuştur.

Anahtar Kelimeler

İşlem süresi, Yapay sinir ağları, Sayısal kestirim, Tıbbi laboratuvar, Sağlık hizmetleri

Kaynakça

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