Managing Health Treatment by Optimizing Complex Lab-Developed Test Configurations: A Health Informatics Perspective

Uzma Afzal, Tariq Mahmood, Ali Mustafa Qamar*, Ayaz H. Khan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

A complex Laboratory Developed Test (LDT) is a clinical test developed within a single laboratory. It is typically configured from many feature constraints from clinical repositories, which are part of the existing Laboratory Information Management System (LIMS). Although these clinical repositories are automated, support for managing patient information with test results of an LDT is also integrated within the existing LIMS. Still, the support to configure LDTs design needs to be made available even in standard LIMS packages. The manual configuration of LDTs is a complex process and can generate configuration inconsistencies because many constraints between features can remain unsatisfied. It is a risky process and can lead patients to undergo unnecessary treatments. We proposed an optimized solution (opt-LDT) based on Genetic Algorithms to automate the configuration and resolve the inconsistencies in LDTs. Opt-LDT encodes LDT configuration as an optimization problem and generates a consistent configuration that satisfies the constraints of the features. We tested and validated opt-LDT for a local secondary care hospital in a real healthcare environment. Our results, averaged over ten runs, show that opt-LDT resolves 90% of inconsistencies while taking between 6 and 6.5 s for each configuration. Moreover, positive feedback based on a subjective questionnaire from clinicians regarding the performance, acceptability, and efficiency of opt-LDT motivates us to present our results for regulatory approval.

Original languageEnglish
Pages (from-to)6251-6267
Number of pages17
JournalComputers, Materials and Continua
Volume75
Issue number3
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 Tech Science Press. All rights reserved.

Keywords

  • Artificial intelligence
  • evolutionary algorithms
  • feature selection
  • genetic algorithms
  • health informatics
  • laboratory developed test

ASJC Scopus subject areas

  • Biomaterials
  • Modeling and Simulation
  • Mechanics of Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering

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