Health Life

New decision support tool can provide personalized antibiotic treatment recommendations

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A new study led by researchers at the Harvard Pilgrim Health Care Institute developed an algorithm that could greatly reduce use of broad-spectrum antibiotics in outpatient settings, a step toward reducing antibiotic resistance. The findings will be published online November 4, 2020 in Science Translational Medicine.

As discussed by the authors, is a major threat to the practice of medicine and is driven in large part by overuse of antibiotics. Outpatient settings are where the vast majority of antibiotics are prescribed but are also where the fewest tools are available to help prescribers make optimal treatment decisions. This leads providers to prescribe in response to a real, as well as a perceived, increase in the rates of antibiotic resistant infection. However, use of broad-spectrum antibiotics, which work against a wide range of bacteria, promotes a vicious cycle where overuse further worsens the problem of resistance through a positive feedback loop. An example is urinary tract infection (UTI), which is a very common reason for using antibiotics among outpatients. Despite national guidelines urging the use of narrow-spectrum treatments as first line therapies, the most commonly prescribed treatments are ciprofloxacin and levofloxacin, which are broad spectrum, second line antibiotics associated with a host of adverse events.

Little attention has been paid to developing effective decision support tools for outpatient prescribers. Algorithms have been used for clinical decision support for since the 1970s but have not yet been widely adopted due to difficulties in integrating them into busy clinical practices. Sanjat Kanjilal, MD, MPH, lead author and Lecturer in Population Medicine at the Harvard Pilgrim Health Care Institute and Harvard Medical School, believes we now have the tools to do better. “Personalized decision support at the point of care may be an