{"id":11907,"date":"2018-11-19T13:12:00","date_gmt":"2018-11-19T18:12:00","guid":{"rendered":"https:\/\/engineering.jhu.edu\/magazine-archive\/?p=11907"},"modified":"2018-11-19T16:14:44","modified_gmt":"2018-11-19T21:14:44","slug":"reducing-no-show-rates","status":"publish","type":"post","link":"https:\/\/engineering.jhu.edu\/magazine-archive\/2018\/11\/reducing-no-show-rates\/","title":{"rendered":"Upstarts: Reducing ‘No-Show’ Rates"},"content":{"rendered":"\"\"\n

Patients who don’t show up for their scheduled medical appointments are a huge drain on health care providers’ time and resources, reducing appointment availability, increasing wait times, and negatively impacting patient satisfaction. To solve this problem, a team of researchers from the Johns Hopkins Malone Center for Engineering in Healthcare<\/a> has developed a new algorithm that can reduce no-show rates and increase appointment availability.<\/p>\n

“The new approach developed with our Johns Hopkins Community Physician<\/a> partners has allowed the clinic to add over 70 pediatric appointments to its schedule per week, improving outpatient access for more children, while also reducing the no-show rate 16 percent for patients who are highly likely to miss scheduled appointments,” says Scott Levin<\/a>, associate professor of emergency medicine at the Johns Hopkins University School of Medicine<\/a>.<\/p>\n

As part of their research, Levin and his team examined operations at two JHCP clinics, where they discovered that, on average, 20 percent of patients failed to show up for their scheduled appointments.<\/p>\n

First, the team developed a machine-learning algorithm that predicts the likelihood that an individual\u00a0will show up. The model takes various predictors into account\u2014demographics, economic status, medical history\u2014and spits out a probability \u201cno-show score.\u201d Based on these scores, providers can identify \u201chigh-risk\u201d patients and decide on further intervention.<\/p>\n

Second, the researchers examined how providers can fill these \u201chigh-risk\u201d appointment slots to most efficiently use their resources. For example, some have staff members make additional live reminder\/confirmation calls to such patients. Other departments give these high-risk appointment slots to patients who urgently need to be seen.<\/p>\n

Researchers say the next step is to integrate the model into Epic, Johns Hopkins Medicine\u2019s electronic patient care records system, before expanding it across the Johns Hopkins Health System and, eventually, to hospitals across the nation.<\/p>\n","protected":false},"excerpt":{"rendered":"

Algorithm helps medical offices to schedule reminders and fill vacant appointment slots with patients who urgently need to be seen.<\/p>\n","protected":false},"author":4,"featured_media":11911,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[24],"tags":[269,1004,2038,2063,3787,3792,3797,3802,3807],"class_list":["post-11907","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-impact","tag-malone-center-for-engineering-in-healthcare","tag-machine-learning","tag-health-care","tag-johns-hopkins-engineering","tag-johns-hopkins-medicine","tag-scott-levin","tag-emergency-medicine","tag-johns-hopkins-community-physicians","tag-healthcare","issue-winter-2019"],"acf":[],"yoast_head":"\nUpstarts: Reducing 'No-Show' Rates - JHU Engineering Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/engineering.jhu.edu\/magazine-archive\/2018\/11\/reducing-no-show-rates\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Upstarts: Reducing 'No-Show' Rates - 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