For every five appointments at Boston Children’s Hospital, one patient doesn’t show up.
Missed appointments are a common problem at health systems. And they’re a particularly attractive target for machine learning researchers, who can use patient datasets to get a handle on what’s causing patients to miss out on needed care. In new research published this month, a group of researchers at Boston Children’s crunched more than 160,000 hospital appointment records from almost 20,000 patients for clues. Their model found patients who had a history of no-shows were more likely to miss future appointments, as were patients with language barriers and those scheduled to see their provider on days with bad weather.
They’re predictions that, in theory, could help a health system target interventions to the patients at highest risk of missing their appointments and offer them whatever help they need making it in. But even though Boston Children’s leaders helped develop and test the model, the health system isn’t yet sold on taking it out of pilot mode and actually putting it into practice.
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