Scientists have created a new machine learning tool that could help healthcare workers quickly screen and direct the flow of COVID-19 patients arriving at hospitals.
Results from an evaluation of this algorithm, along with an artificial intelligence method that improves test utilization and reimbursement, were presented today at the 2021 AACC Annual Scientific Meeting & Clinical Lab Expo.
A team of researchers led by Rana Zeeshan Haider, PhD, and Tahir Sultan Shamsi, FRCP, of the National Institute of Blood Disease in Karachi, Pakistan, created a machine learning algorithm to help healthcare workers efficiently screen incoming COVID-19 patients. The scientists extracted routine diagnostic and demographic data from the records of 21,672 patients presenting at hospitals and applied several statistical techniques to develop this algorithm, which is a predictive model that differentiates between COVID-19 and non-COVID-19 patients. During validation experiments, the model performed with an accuracy of up to 92.5% when tested with an independent dataset and showed a negative predictive value of up to 96.9%. The latter means that the model is particularly reliable when identifying patients who don’t have COVID-19.
Other researchers developed an automated lab management system to help improve test ordering.
set out along with colleagues to determine if an automated test management system known as the Laboratory Decision System (LDS) could help improve test ordering.
The Laboratory Decision System (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 Rojeet Shrestha, PhD, of Patients Choice Laboratories in Indianapolis “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.”
