Literature DB >> 30585134

Clinical biochemistry test eliminator providing cost-effectiveness with five algorithms.

Ataman Gönel1.   

Abstract

Objectives: The purpose of this study is to investigate the elimination ratios of requested unnecessary tests and the cost-effectiveness to be achieved by means of 5 different algorithms with clinical validity defined in an artificial intelligence program.
Methods: The clinician orders received from the hospital information management system were adapted to eliminate AST, direct bilirubin, chlorine, fPSA and fT3 tests using five different algorithms defined in the ALIN IQ software.
Results: In this study, 18387 AST, 9500 direct bilirubin, 61 free PSA, 1127 FT3 and 11172 chlorine tests that were ordered within 45 days were eliminated using 5 different algorithms defined in the ALIN IQ software in the Laboratory of Harran University Faculty of Medicine. USD 5592.76 was saved in 45 days. The annual saving is expected to be 363710 tests and USD 45363.49.
Conclusion: Five different tests were successfully eliminated with this study. Open-code smart softwares, which can create indefinite algorithms may be utilized as test eliminators in diagnostic clinical laboratories. Millions of dollars may be saved by means of such artificial intelligence softwares that can be adapted to any analyzer across the world.

Entities:  

Keywords:  AST; FT3; chlorine; direct bilirubin; test rejection

Year:  2018        PMID: 30585134     DOI: 10.1080/17843286.2018.1563324

Source DB:  PubMed          Journal:  Acta Clin Belg        ISSN: 1784-3286            Impact factor:   1.264


  2 in total

1.  Cost-effectiveness of Artificial Intelligence as a Decision-Support System Applied to the Detection and Grading of Melanoma, Dental Caries, and Diabetic Retinopathy.

Authors:  Jesus Gomez Rossi; Natalia Rojas-Perilla; Joachim Krois; Falk Schwendicke
Journal:  JAMA Netw Open       Date:  2022-03-01

Review 2.  Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis.

Authors:  Jesus Gomez Rossi; Ben Feldberg; Joachim Krois; Falk Schwendicke
Journal:  JMIR Med Inform       Date:  2022-08-12
  2 in total

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