Open Access

ARTICLE

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

Uzma Afzal¹, Tariq Mahmood², Ali Mustafa Qamar^3,*, Ayaz H. Khan^4,5

1 Department of Computer Science, Federal Urdu University of Arts Science and Technology, Karachi, Pakistan
2 Department of Computer Science, Institute of Business Administration, Karachi, Pakistan
3 Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi Arabia
4 Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
5 SDAIA-KFUPM Joint Research Center for Artificial Intelligence, Dhahran, 31261, Saudi Arabia

* Corresponding Author: Ali Mustafa Qamar. Email:

Computers, Materials & Continua 2023, 75(3), 6251-6267. https://doi.org/10.32604/cmc.2023.037653

Received 12 November 2022; Accepted 13 February 2023; Issue published 29 April 2023

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.

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Keywords

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