{"id":10731,"date":"2016-05-01T10:13:01","date_gmt":"2016-05-01T09:13:01","guid":{"rendered":"http:\/\/catai.net\/blog\/?p=10731"},"modified":"2016-05-01T10:13:01","modified_gmt":"2016-05-01T09:13:01","slug":"new-diy-analytics","status":"publish","type":"post","link":"https:\/\/catai.net\/blog\/2016\/05\/new-diy-analytics\/","title":{"rendered":"New DIY Analytics"},"content":{"rendered":"<p>At the present moment Microsoft Analytics or Google Analytics or any Machine Learning applications can be carried out by yourself with some of the existing indexes and tools such as:<\/p>\n<ul>\n<li><a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC4128541\/\">LACE index<\/a> that accuratly predict high risk\u00a0readmision of CHF (chronic heart faillure) within 30 days after hospital discharge.<\/li>\n<\/ul>\n<div class=\"grid no_max_width\">\n<div class=\"col twelve_col nomargin shadow\">\n<div class=\"wrap\">\n<div class=\"page\">\n<div class=\"content\">\n<div class=\"container\">\n<div class=\"hide-overflow article lit-style content pmc-wm slang-all page-box-wide\">\n<div id=\"T1\" class=\"table-wrap table whole_rhythm\">\n<div class=\"large_tbl\">\n<table class=\"rendered small default_table\" style=\"height: 44px;\" border=\"1\" width=\"892\" frame=\"hsides\" rules=\"groups\">\n<tbody valign=\"top\">\n<tr>\n<td style=\"padding-left: 90px;\" colspan=\"3\" rowspan=\"1\" align=\"center\">Total LACE score\u2009=\u2009L-score\u2009+\u2009A-score\u2009+\u2009C-score\u2009+\u2009E-score 0\u20134 Low, 5\u20139 Moderate, &gt; 9 High Risk<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"padding-left: 90px;\">L-score is Lenght of stay<\/p>\n<p style=\"padding-left: 90px;\">A-score is Accuity of admission<\/p>\n<p style=\"padding-left: 90px;\">C-score is Comorbilities<\/p>\n<p style=\"padding-left: 90px;\">E-score is Emergency department visits<\/p>\n<p>Better predictors of high risk readmissions among CHF patients may be 1) number of previous emergency department\u00a0visits, 2) history of myocardial infartion, 3) and\/or history of\u00a0periferal vascular disease.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<ul>\n<li><a href=\"http:\/\/www.ahrq.gov\/sites\/default\/files\/publications\/files\/redtoolkitforms.pdf\">RED toolkit<\/a> meaning Re-Engineered Discharge, which include the <a href=\"http:\/\/ahrqrxprod1:9992\/Rhythmyx\/assembler\/render?sys_contentid=27842&amp;sys_revision=2&amp;sys_folderid=68397&amp;sys_context=0&amp;sys_siteid=305&amp;sys_variantid=572&amp;sys_authtype=0&amp;sys_command=edit\">taking care of myself guide<\/a>.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/05\/RED-toolkit.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-medium wp-image-10734\" src=\"https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/05\/RED-toolkit-300x190.png\" alt=\"RED toolkit\" width=\"300\" height=\"190\" srcset=\"https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/05\/RED-toolkit-300x190.png 300w, https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/05\/RED-toolkit-600x381.png 600w, https:\/\/catai.net\/blog\/wp-content\/uploads\/2016\/05\/RED-toolkit.png 882w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>At the present moment Microsoft Analytics or Google Analytics or any Machine Learning applications can be carried out by yourself with some of the existing indexes and tools such as: LACE index that accuratly predict high risk\u00a0readmision of CHF (chronic heart faillure) within 30 days after hospital discharge. Total LACE score\u2009=\u2009L-score\u2009+\u2009A-score\u2009+\u2009C-score\u2009+\u2009E-score 0\u20134 Low, 5\u20139 Moderate, [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"give_campaign_id":0,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[28],"tags":[],"class_list":["post-10731","post","type-post","status-publish","format-standard","hentry","category-sanidad"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>New DIY Analytics | Telemedicina<\/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:\/\/catai.net\/blog\/2016\/05\/new-diy-analytics\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"New DIY Analytics | Telemedicina\" \/>\n<meta property=\"og:description\" content=\"At the present moment Microsoft Analytics or Google Analytics or any Machine Learning applications can be carried out by yourself with some of the existing indexes and tools such as: LACE index that accuratly predict high risk\u00a0readmision of CHF (chronic heart faillure) within 30 days after hospital discharge. 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