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AI could help reduce alcohol-related risks in surgery patients



Using AI to scan surgery patients’ medical records for signs of risky drinking might help spot those whose alcohol use raises their risk of problems during and after an operation, a new study suggests.

The AI record scan tested in the study could help surgery teams know in advance which patients may need more education about such risks, or treatment to help them reduce their drinking or stop drinking for a period of time before and after surgery.

The University of Michigan research shows that using a form of AI called natural language processing to analyse a patient’s entire medical record can spot signs of risky drinking documented in their charts, such as in doctor’s notes, even when they don’t have a diagnosis of an alcohol problem.

Senior author Anne Fernandez, Ph.D. is an addiction psychologist at the U-M Addiction Center and Addiction Treatment Services and an associate professor of psychiatry.

The researche said: “Given the excess surgical risk that can arise from even a moderate amount of daily alcohol use, and the challenges of implementing robust screening and treatment in the pre-op period, it’s vital that we explore other options for identifying patients who could most benefit from reducing use by themselves or with help, beyond those with a recorded diagnosis.”

The research team trained their AI model by letting it review 100 anonymous surgical patients’ records to look for risky drinking signs, and comparing its classifications with those of expert human reviewers.

Overall, the AI model matched the human expert classification most of the time.

The AI model found signs of risky drinking in the notes of 87 per cent of the patients who experts had identified as risky drinkers.

Meanwhile, only 29 per cent of these patients had a diagnosis code related to alcohol in their list of diagnoses, so many patients with higher risk for complications would have slipped under the radar for their surgical team.

The research team then allowed the AI model to review more than 53,000 anonymous patient medical records compiled through the Michigan Genomics Initiative.

The AI model identified three times more patients with risky alcohol use through the full-text search than the researchers found using diagnosis codes.

In all, 15 per centof patients met criteria via the AI model, compared to 5 per cent via diagnosis codes.

V. G. Vinod Vydiswaran, Ph.D. is lead author of the new paper and an associate professor of learning health sciences at the U-M Medical School.

The researcher said: “This evaluation of natural language processing to identify risky drinking in the records of surgical patients could lay the groundwork for efforts to identify other risks in primary care and beyond, with appropriate validation.

“Essentially, this is a way of highlighting for a provider what is already contained in the notes made by other providers, without them having to read the entire record.”

The new data suggest that surgical clinics that simply review the diagnosis codes listed in their incoming patients’ charts, and flag ones such as alcohol use disorder, alcohol dependence or alcohol-related liver conditions, would be missing many patients with an elevated risk.

The new study used the NLP form of AI not to generate new information, but to search for clues in the pages and pages of provider notes and data that make up a person’s entire medical record.

After validation, the tool could potentially be run on a patient’s record before they are seen in a pre-operative appointment and identify their risk level, Vydiswaran said.

Meanwhile, Fernandez is leading an effort to test a virtual coaching approach to help people scheduled for surgery understand the risks related to their level of drinking and support them in reducing their intake.

The researcher said: “Our goal is to identify people who may be in need of more treatment services, including medication for alcohol use disorder and support during their surgical recovery when alcohol abstinence is necessary.

“We are not aiming to replace the due diligence every provider must do, but to prompt them to talk with patients and get more information to act upon.

“These AI tools can do amazing things, but it’s important we use them to do things that could save time for busy clinicians, whether that’s related to alcohol or to drug use, disordered eating, or other chronic conditions.

“And if we are going to use them to spot potential issues, we need to be ready to offer treatment options too.”

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