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Cited by 4Healthcare Operations and Scheduling OptimizationArtificial Intelligence in HealthcareClinical Laboratory Practices and Quality Control

Turnaround Time Prediction for a Medical Laboratory Using Artificial Neural Networks

Open access
Eminağaoğlu, Mete, Vahaplar, Alper·2018·Bilişim Teknolojileri Dergisi
DOI 10.17671/gazibtd.444826
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Abstract

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.

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  • There are very few studies on machine learning to predict TAT or investigate factors affecting TAT in medical diagnostic laboratories.

    “However, there are very few studies on ML to predict TAT or investigate factors affecting TAT in medical diagnostic laboratories [5,6].”

    From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [6]

  • Only one study has predicted TAT for a specific sample, namely the time in post-analysis between result generation and result reporting.

    “However, we found only one study on predicting TAT for a specific sample, namely the time in post-analysis between result generation and result reporting [6].”

    From Turnaround time prediction for clinical chemistry samples using machine learning (2022) · cites this paper as [6]

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Authors
Eminağaoğlu, M., & Vahaplar, A.
Affiliations
Dokuz Eylül University (TR)
ORCID
Venue
Bilişim Teknolojileri Dergisi
Publisher
International Journal of Informatics Technologies
Published
2018-10-30
Volume
11
Issue
4
Pages
357-368
ISSN
1307-9697 (ISSN-L)
DOI
10.17671/gazibtd.444826
OpenAlex
W2899164330
Cited by
4
References
0
Citations by year
2026: 1 · 2022: 1 · 2020: 2
FWCI
0.77
Type
journal article
Language
en
Open access
Locations
  • Bilişim Teknolojileri Dergisi (publishedVersion)
  • DergiPark (Istanbul University) (submittedVersion)
Topics
  • Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
Keywords
medical laboratories, artificial neural networks, hospital service quality

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