Predictor Looks to Reduce Patient No-Shows

Among the reasons for skyrocketing U.S. healthcare costs is the relatively mundane problem of patient no-shows, which end up costing the healthcare sector billions of dollars in lost revenues each year. In response, healthcare analytics developers have come up with predictive models that helps hospitals and clinics gauge when appointments might not be kept. Using a data science platform developed by New York-based startup Dataiku (as in, “data-haiku”), healthcare analytics specialist Intermedix Corp. built a tool for predicting which patients are most likely to miss scheduled appointments. The partners said the tool is now being used in more than 50 U.S. private clinics. The problem of patient no-shows is common and growing. The analytics partners cited estimates that between 5 and 10 percent of patients missed scheduled appointments. In response,…

Link to Full Article: Predictor Looks to Reduce Patient No-Shows

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