{"id":289,"date":"2013-11-11T10:04:29","date_gmt":"2013-11-11T15:04:29","guid":{"rendered":"https:\/\/solutionsreview.com\/business-intelligence\/?p=289"},"modified":"2016-10-04T12:58:41","modified_gmt":"2016-10-04T16:58:41","slug":"predictive-modeling-reduces-patient-waiting-times-at-johns-hopkins","status":"publish","type":"post","link":"https:\/\/solutionsreview.com\/business-intelligence\/predictive-modeling-reduces-patient-waiting-times-at-johns-hopkins\/","title":{"rendered":"Predictive Modeling Reduces Patient Waiting Times at Johns Hopkins"},"content":{"rendered":"

\"Predictive<\/a>Reducing patient waiting times is an ongoing challenge for hospital emergency departments.\u00a0 In 2011, 20 percent of American adults reported at least one emergency room visit within the past year, and 7 percent reported two or more visits, according to Health, United States, 2012, the federal government\u2019s comprehensive annual report on American\u2019s health.<\/p>\n

About one year ago, the emergency department (ED) at Johns Hopkins Hospital in Baltimore adopted a dashboard program from Tableau Software.\u00a0 The dashboard interface helps doctors, nurses, shift coordinators, and administrators, with monitoring patient whereabouts.\u00a0 The system used by the Johns Hopkins staff can be accessed through authorized PCs or tablets.<\/p>\n

Johns Hopkins Hospital has 67 private exam rooms and six trauma rooms. \u00a0The ED, which sees more than 50,000 patients annually, has pioneered the usage of the interactive Tableau software.\u00a0 The software used by hospital staff is updated every five minutes, gathering patient status information from five or six departmental systems. \u00a0Staff can monitor the supply and demand of beds and estimate how many admissions from the ED will occur.<\/p>\n

Hospital administrators are looking to create other predictive models that will improve patient care.\u00a0 The Tableau software has effectively reduced patient waiting times.\u00a0 Last year, patients were staying in the emergency room for 10 or 11 hours.\u00a0 This year, patient waiting times are averaging about 4 hours.\u00a0 Other departments at Johns Hopkins Hospital are asking ED administrators for assistance with introducing their own Tableau dashboards.<\/p>\n

The popularity of executive information systems is increasing explains James Gaston, senior director of clinical and business intelligence at the Healthcare Information and Management Systems Society.\u00a0 Gaston says that general-purpose dashboards \u201care one type of BI report application that healthcare is beginning to embrace as it improves IT overall.\u201d<\/p>\n

With the success of the Tableau dashboard for the Hopkins emergency department, perhaps other hospital emergency departments will utilize\u00a0the software as well.\u00a0 Reducing patient waiting times in emergency rooms will help millions of patients.<\/p>\n

https:\/\/www.informationweek.com\/big-data\/news\/software\/business-intelligence\/tableau-helps-johns-hopkins-cut-emergenc\/240163426<\/a><\/p>\n

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Reducing patient waiting times is an ongoing challenge for hospital emergency departments.\u00a0 In 2011, 20 percent of American adults reported at least one emergency room visit within the past year, and 7 percent reported two or more visits, according to Health, United States, 2012, the federal government\u2019s comprehensive annual report on American\u2019s health. About one […]<\/p>\n","protected":false},"author":1,"featured_media":303,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"acf":[],"yoast_head":"\nPredictive Modeling Reduces Patient Waiting Times at Johns Hopkins | Solutions Review<\/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:\/\/solutionsreview.com\/business-intelligence\/predictive-modeling-reduces-patient-waiting-times-at-johns-hopkins\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Doug Atkinson\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" 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