{"id":7937,"date":"2016-08-03T14:30:12","date_gmt":"2016-08-03T18:30:12","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=7937"},"modified":"2016-08-04T09:33:26","modified_gmt":"2016-08-04T13:33:26","slug":"predictive-big-data-analytics-identify-high-risk-ed-patients-2","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/predictive-big-data-analytics-identify-high-risk-ed-patients-2\/7937\/","title":{"rendered":"Predictive Big Data Analytics Identify High-Risk ED Patients"},"content":{"rendered":"A predictive big data analytics algorithm using a variety of demographic and clinical data points may be helpful for identifying patients at high risk of hospitalization or ED use. Reducing unnecessary emergency department utilization and avoidable hospital admissions is a top priority for many healthcare systems, especially those seeking to cut costs and eliminate waste in preparation for value-based care. Register using code PATIMES16 and receive 15% off conference passes. While coordinated care models such as the patient-centered medical home (PCMH) have successfully helped providers reroute non-critical patients away from the ED, the use of predictive big data <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/predictive-big-data-analytics-identify-high-risk-ed-patients-2\/7937\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>A predictive big data analytics algorithm using a variety of demographic and clinical data points may be helpful for identifying patients at high risk of hospitalization or ED use. Reducing unnecessary emergency department utilization and avoidable hospital admissions is a top priority for many healthcare systems, especially those seeking to cut costs and eliminate waste [&hellip;]<\/p>\n","protected":false},"author":625,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11,48],"tags":[42,760,534,230,480,8],"class_list":["post-7937","post","type-post","status-publish","format-standard","hentry","category-industry-news","category-left-hand","tag-big-data","tag-chronic-disease-management","tag-clinical-analytics","tag-data-analytics","tag-population-health-management","tag-predictive-analytics"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7937","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/625"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=7937"}],"version-history":[{"count":3,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7937\/revisions"}],"predecessor-version":[{"id":7940,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/7937\/revisions\/7940"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=7937"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=7937"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=7937"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}