An automated process that combines natural language processing and machine learning has identified people who inject drugs (PWIDs) in electronic health records faster and more accurately than current methods that rely on manual reviews of folders.
Currently, people who inject drugs are identified by International Classification of Diseases (ICD) codes that are specified in patients’ electronic health records by health care providers or extracted from these notes by coders. trained humans who review them for billing purposes. But there is no specific ICD code for injection drug use, so providers and coders must rely on a combination of non-specific codes as a proxy for identifying PWIDs – a slow approach that can lead to inaccuracies.
Researchers manually reviewed 1,000 records from 2003 to 2014 of people admitted to Veterans Administration hospitals with Staphylococcus aureus bacteremia, a common infection that develops when bacteria enters openings in the skin, such as those of the injection sites. They then developed and trained algorithms using natural language processing and machine learning and compared them with 11 proxy combinations of ICD codes to identify PWIDs.
Study limitations include potentially poor documentation by providers. Also, the dataset used is from 2003 to 2014, but the injection drug use epidemic has since shifted from prescription opioids and heroin to synthetic opioids like fentanyl, which the algorithm may miss because the dataset where he learned the classification does not have many examples of this drug. Finally, the results may not apply to other circumstances since they are based entirely on data from the Veterans Administration.
The use of this artificial intelligence model significantly speeds up the process of identifying PWIDs, which could improve clinical decision-making, health services research, and administrative oversight.
“Using natural language processing and machine learning, we were able to identify people who inject drugs in thousands of notes in minutes, compared to the weeks it would take a manual screener to do so” said lead author Dr. David Goodman-Meza, assistant professor of medicine in the division of infectious diseases at UCLA’s David Geffen School of Medicine. “This would allow health systems to identify PWID to better allocate resources such as needle service programs and addiction and mental health treatment for people who use drugs.”
The study is published in the peer-reviewed journal Open Forum on Infectious Diseases.
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David Goodman-Meza et al, natural language processing and machine learning to identify people who inject drugs in electronic health records, Open Forum on Infectious Diseases (2022). DOI: 10.1093/ofid/ofac471
Provided by University of California, Los Angeles
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