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Artificial Intelligence Method That Improves Lab Test Management Discussed at AACC 2021

By Carolyn Moody, RN - News Editor
Posted on 29 Sep 2021
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Results from an evaluation of an artificial intelligence (AI) method that improves test utilization and reimbursement were presented by Patients Choice Laboratories (Indianapolis, IN, USA) at the 2021 AACC Annual Scientific Meeting & Clinical Lab Expo.

Of the five billion lab orders submitted each year, at least 20% are considered inappropriate. These inappropriate tests can lead to slower or incorrect diagnoses for patients. Such tests may also not be covered by Medicare if they weren’t meant to be used for particular medical conditions or if they were ordered with the wrong ICD-10 diagnostic codes, which in turn raises health costs.

Patients Choice Laboratories set out to determine if an automated test management system known as the Laboratory Decision System (LDS) could help improve test ordering. The LDS scores potential tests based on medical necessity and testing indication, helping providers minimize test misutilization and select the best tests for a given medical condition.

Using LDS, the researchers re-evaluated a total of 374,423 test orders from a reference laboratory, 48,049 of which had not met the criteria for coverage under Medicare. For 96.4% of the first 10,000 test claims, the LDS ranking system recommended alternative tests that better matched the medical necessity or had a more appropriate ICD-10 code. Of these recommendations, 80.5% would also meet Medicare policies. All of this indicates that the LDS could help correct mistaken or inappropriate lab orders.

“Our study implies that use of the automated test ordering system LDS would be extremely helpful for providers, laboratories, and payers,” said Dr. Rojeet Shrestha, PhD, of Patients Choice Laboratories. “Use of this algorithm-based testing selection and ordering database, which rates and scores potential tests for any given disease based on clinical relevance, medical necessity, and testing indication, would eventually help providers to select and order the right test and reduce over- and under-utilization of tests.”

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