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Clinical biochemistry test eliminator providing cost-effectiveness with five algorithms.
1Department of Medical Biochemistry, Harran University, Sanliurfa, Turkey.
Acta Clinica Belgica
|December 27, 2018
Summary
Artificial intelligence algorithms can effectively eliminate unnecessary laboratory tests, significantly reducing costs. This study demonstrated substantial savings by identifying and removing redundant tests like AST and fPSA.
Area of Science:
- Clinical Chemistry
- Medical Informatics
- Artificial Intelligence in Healthcare
Background:
- Unnecessary laboratory tests contribute to increased healthcare costs and resource utilization.
- Clinical decision support systems are crucial for optimizing diagnostic workflows.
Purpose of the Study:
- To evaluate the efficacy of five distinct artificial intelligence (AI) algorithms in eliminating unnecessary laboratory tests.
- To determine the cost-effectiveness of using AI for test selection in clinical diagnostics.
Main Methods:
- Clinician orders were analyzed using the ALIN IQ software, which implemented five AI algorithms.
- Specific tests targeted for elimination included AST, direct bilirubin, chlorine, fPSA, and fT3.
Main Results:
- Over 45 days, 5 AI algorithms successfully eliminated 39,157 tests, including AST, direct bilirubin, fPSA, FT3, and chlorine.
- This resulted in savings of $5,592.76 within the 45-day period.
- Projected annual savings are estimated at 363,710 tests and $45,363.49.
Conclusions:
- The study successfully demonstrated the elimination of five different laboratory tests using AI algorithms.
- Open-code smart software with adaptable algorithms can serve as effective test eliminators in diagnostic labs.
- AI-driven solutions offer significant potential for cost savings in global healthcare systems.
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