Who needs to see a doctor and who doesn’t?
While triage is a term typically reserved for emergency situations, it also covers the decisions healthcare providers make many times over the course of a day as they field calls from patients with a seemingly endless array of symptoms.
In a recent commentary, Irving Loh, co-founder and CMO at Infermedica, a tech solution provider based in Poland and Colorado, observes that “(w)ith health providers stretched and populations increasing in age and in need of greater services, the challenge facing physicians will only grow. So, the question is: can medtech that simulates how doctors think assist them to reach quicker outcomes and improve patient diagnosis?”
In his view, the answer is “Yes,” and he points to the potential of AI to provide much-needed relief to overworked doctors. AI, he argues, “can streamline the triage process. It seamlessly integrates into the medical workflow and uses the same logic physicians embrace at medical school, delivering enhanced insights by calculating the likelihood of particular conditions based on the patient’s symptoms. . . . AI can facilitate a faster, automated route to outcome by reducing the steps that doctors need to take, ultimately meaning quicker answers and patient recovery.”
AI can also facilitate the collection of medical histories and the maintenance of up-to-date EHRs, giving providers more information and saving them valuable time.
“What’s more,” he notes, “the pandemic has highlighted that triage from home is crucial for patient safety. Indeed, some health providers are seeing patient levels drop because people are unwilling to come in even if they do require help. This is because pathogens and other illnesses are concentrated in waiting rooms, so AI tools that can help patients to evaluate whether it’s worth the risk of attending in person – or if self-care is more appropriate – are extremely valuable.”
For Loh, the key for healthcare organizations is to recognize how AI can imitate a doctor’s thought process. “Doctors think in probabilities. They are presented with symptoms, consider the next logical question, and look to eliminate or strengthen the likelihood of it being a certain condition, before directing patients to the correct level of care.
“AI works similarly, analysing multiple conditions at once and eliminating the least likely illnesses. This saves the physician time by providing the correct range of diagnoses much faster. Not only this, but it can determine the acuity level, a difficult skill for even the most experienced medical professionals.”
To be sure, it takes time, money and effort to successfully incorporate AI into a health system.
As Loh notes, “(a)ny form of technology needs to integrate seamlessly into the clinical workflow to be effective.”
That said, he concludes, “AI not only improves patient diagnosis by simulating how medical professionals think, but also enhances their experiences, ultimately improving patient care and health outcomes.”
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