{"id":31518,"date":"2018-04-30T16:00:21","date_gmt":"2018-04-30T16:00:21","guid":{"rendered":"https:\/\/caseyfamilyv2.wpengine.com\/implementing-predictive-analytics\/"},"modified":"2026-05-19T19:39:36","modified_gmt":"2026-05-19T19:39:36","slug":"implementing-predictive-analytics","status":"publish","type":"post","link":"https:\/\/www.casey.org\/es\/implementing-predictive-analytics\/","title":{"rendered":"Considerations for Implementing Predictive Analytics in Child Welfare"},"content":{"rendered":"
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The use of administrative data collected from child welfare and other sectors to support decision-making and improve outcomes continues to evolve. Predictive analytics uses existing data to predict the likelihood of future outcomes.<\/p>\n

This document can serve as a guide as jurisdiction leaders embark on engaging in predictive analytics. While many uses of predictive analytics currently exist, some potential uses in child welfare include helping to identify families who may benefit from early intervention services, identifying factors correlated with child safety and placement disruption, and predicting which children are most likely to experience long stays in out-of-home care.<\/p>\n

This brief highlights issues associated with applying predictive analytics in child welfare. Included are some considerations for child welfare agencies as they engage in predictive analytics.<\/p>\n<\/div>\n \n \n \n <\/div>\n<\/div>\n\n\n\n

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Report and infographic<\/div>\n

Considerations for Implementing Predictive Analytics in Child Welfare<\/h2>\n

This guide helps child welfare leaders use predictive analytics to improve early intervention and child safety while addressing key implementation challenges.<\/p>\n

DOWNLOAD REPORT<\/a><\/strong>
\nPDF: 956.08 KB<\/p>\n

DOWNLOAD INFOGRAPHIC<\/a><\/strong>
\nPDF: 77.67 KB (Preview<\/a>)
\nInfographic prints best on tabloid or legal size paper.<\/p>\n<\/div>\n <\/div>\n\n

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\n \"\"\n <\/div>\n\n <\/div>\n <\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

This brief highlights considerations for child welfare agencies as they engage in predictive analytics.<\/p>","protected":false},"author":18,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":"","footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[280,266],"content_type":[205,202],"hidden-tags-misc":[],"audience":[313,317],"content_location":[329],"topic":[411,412,417,457],"class_list":["post-31518","post","type-post","status-publish","format-standard","hentry","category-innovative-programs","category-safely-reduce-need-for-foster-care-across-nation","content_type-brief","content_type-infographic","audience-academia-researcher","audience-child-welfare-leader","content_location-national","topic-child-safety","topic-child-welfare-leadership","topic-data","topic-public-policy"],"acf":[],"yoast_head":"\nApplying Predictive Analytics - Casey Family Programs<\/title>\n<meta name=\"description\" content=\"This brief highlights considerations for child welfare agencies as they engage in predictive analytics.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.casey.org\/es\/implementing-predictive-analytics\/\" \/>\n<meta property=\"og:locale\" content=\"es_MX\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"2015 Out-of-Home Care Analysis\" \/>\n<meta property=\"og:description\" content=\"This inaugural From Data to Practice report explores the relationship between services and outcomes in a cohort of youth served in CFS out-of-home care.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.casey.org\/es\/implementing-predictive-analytics\/\" \/>\n<meta property=\"og:site_name\" content=\"Casey Family Programs\" \/>\n<meta property=\"article:published_time\" content=\"2018-04-30T16:00:21+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-05-19T19:39:36+00:00\" \/>\n<meta name=\"author\" content=\"Scott Brown\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"2015 Out-of-Home Care Analysis\" \/>\n<meta name=\"twitter:description\" content=\"This inaugural From Data to Practice report explores the relationship between services and outcomes in a cohort of youth served in CFS out-of-home care.\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Scott Brown\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.casey.org\\\/implementing-predictive-analytics\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.casey.org\\\/implementing-predictive-analytics\\\/\"},\"author\":{\"name\":\"Scott Brown\",\"@id\":\"https:\\\/\\\/www.casey.org\\\/#\\\/schema\\\/person\\\/277402869a8f7ae40f29a909eee4a2cb\"},\"headline\":\"Considerations for Implementing Predictive Analytics in Child Welfare\",\"datePublished\":\"2018-04-30T16:00:21+00:00\",\"dateModified\":\"2026-05-19T19:39:36+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.casey.org\\\/implementing-predictive-analytics\\\/\"},\"wordCount\":8,\"keywords\":[\"Child safety\",\"Data\"],\"articleSection\":[\"Innovative programs\",\"Safely reduce the need for foster care across the nation\"],\"inLanguage\":\"es\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.casey.org\\\/implementing-predictive-analytics\\\/\",\"url\":\"https:\\\/\\\/www.casey.org\\\/implementing-predictive-analytics\\\/\",\"name\":\"Applying Predictive Analytics - 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