Industry News: Machine learning tools for COVID-19 patient screening and improved lab test management discussed at the 2021 AACC Annual Scientific Meeting

The new machine learning tool could help healthcare workers to quickly screen and direct the flow of COVID-19 patients arriving at hospitals

28 Sep 2021


Cell population data (CPD) driven machine learning (ML) tools offer an efficient screening of COVID-19 patients at presentation to the hospital to backing early expulsion and directing patients’ flow-from amid the initial presentation to the hospital. Evaluation results of this algorithm, along with an artificial intelligence method that improves test utilization and reimbursement, were presented 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 to triage them and separate them from other vulnerable patients who may be immunocompromised or have pre-existing medical conditions. However, this step can be difficult 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 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, a predictive model differentiating 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 the rapid expulsion of SARS-CoV-2 from emergency departments, aiding prompt care decisions, directing the 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 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 the 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.”

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