Newswise — ATLANTA – Scientists have created a new machine learning tool that could help healthcare workers to 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.
Streamlining Hospital Admission of COVID-19 Patients
It is important for clinicians to quickly diagnose COVID-19 patients when they arrive at hospitals, both to triage them and to separate them from other vulnerable patients who may be immunocompromised or have pre-existing medical conditions. This can be difficult, however, because COVID-19 shares many symptoms with other viral infections, and the most accurate PCR-based tests for COVID-19 can take several days to yield results.
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, has therefore 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.
“The true negative labeling efficiency of our research advocates its utility as a screening test for rapid expulsion of SARS-CoV-2 from emergency departments, aiding prompt care decisions, directing patient-case flow, and fulfilling the role of a ‘pre-test’ concerning orderly RT-PCR testing where it is not handy,” said Haider. “We propose this test to accept the challenge of critical diagnostic needs in resource constrained settings where molecular testing is not under the flag of routine testing panels.”
Optimizing Lab Test Selection and Reimbursement
Of the 5 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.
Rojeet Shrestha, PhD, of Patients Choice Laboratories in Indianapolis, 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 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 Shrestha. “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.”
Abstract Information
AACC Annual Scientific Meeting registration is free for members of the media. Reporters can register online here: https://www.xpressreg.net/register/aacc0921/media/landing.asp
Abstract A-226: Machine-learning based decipherment of cell population data; a promising hospital front-door screening tool for COVID-19 will be presented during:
Student Poster Presentation
Monday, September 27
9 a.m. – 5 p.m.
Scientific Poster Session
Tuesday, September 28
9:30 a.m. - 5 p.m. (presenting author in attendance from 1:30 - 2:30 p.m.)
Abstract B-011: Use of artificial intelligence for effective test utilization and to increase reimbursement will be presented during:
Scientific Poster Session
Wednesday, September 29
9:30 a.m. - 5 p.m. (presenting author in attendance from 1:30 - 2:30 p.m.)
All sessions will take place in the Poster Hall, which is located in Exhibit Hall C of the Georgia World Congress Center in Atlanta.
About the 2021 AACC Annual Scientific Meeting & Clinical Lab Expo
The AACC Annual Scientific Meeting offers 5 days packed with opportunities to learn about exciting science from September 26-30. Plenary sessions explore COVID-19 vaccines and virus evolution, research lessons learned from the pandemic, artificial intelligence in the clinic, miniaturization of diagnostic platforms, and improvements to treatments for cystic fibrosis.
At the AACC Clinical Lab Expo, more than 400 exhibitors will fill the show floor of the Georgia World Congress Center in Atlanta with displays of the latest diagnostic technology, including but not limited to COVID-19 testing, artificial intelligence, mobile health, molecular diagnostics, mass spectrometry, point-of-care, and automation.
About AACC
Dedicated to achieving better health through laboratory medicine, AACC brings together more than 50,000 clinical laboratory professionals, physicians, research scientists, and business leaders from around the world focused on clinical chemistry, molecular diagnostics, mass spectrometry, translational medicine, lab management, and other areas of progressing laboratory science. Since 1948, AACC has worked to advance the common interests of the field, providing programs that advance scientific collaboration, knowledge, expertise, and innovation. For more information, visit www.aacc.org.
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