{"id":9401,"date":"2022-11-11T23:39:33","date_gmt":"2022-11-11T23:39:33","guid":{"rendered":"https:\/\/risefoc.com\/?p=9401"},"modified":"2023-06-30T18:27:52","modified_gmt":"2023-06-30T18:27:52","slug":"the-power-of-big-data-preventing-insurance-denials","status":"publish","type":"post","link":"https:\/\/risefoc.com\/the-power-of-big-data-preventing-insurance-denials\/","title":{"rendered":"The Power of Big Data: Preventing Insurance Denials"},"content":{"rendered":"<p><img fetchpriority=\"high\" decoding=\"async\" class=\"aligncenter wp-image-9402 size-large\" src=\"https:\/\/risefoc.com\/wp-content\/uploads\/2022\/11\/big-data-1024x682.jpeg\" alt=\"\" width=\"800\" height=\"533\" srcset=\"https:\/\/risefoc.com\/wp-content\/uploads\/2022\/11\/big-data-1024x682.jpeg 1024w, https:\/\/risefoc.com\/wp-content\/uploads\/2022\/11\/big-data-300x200.jpeg 300w, https:\/\/risefoc.com\/wp-content\/uploads\/2022\/11\/big-data-768x512.jpeg 768w, https:\/\/risefoc.com\/wp-content\/uploads\/2022\/11\/big-data-1536x1023.jpeg 1536w, https:\/\/risefoc.com\/wp-content\/uploads\/2022\/11\/big-data.jpeg 1920w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Since the advent of digital healthcare, huge amounts of data have been created and stored. The question is, how do we use it?<\/span><\/p>\n<h1><span style=\"font-weight: 400;\">Big Data, Big Value<\/span><\/h1>\n<p><span style=\"font-weight: 400;\">Big data in healthcare is a valuable asset with a wide range of possibilities. The amount of data healthcare churns out each second is astronomical. Before COVID-19 came on the scene, a 2018 Statista estimate predicted that 2,214 exabytes of new data could be generated globally in 2020. An <\/span><a href=\"https:\/\/www.backblaze.com\/blog\/what-is-an-exabyte\/\"><span style=\"font-weight: 400;\">exabyte<\/span><\/a><span style=\"font-weight: 400;\"> is <\/span><i><span style=\"font-weight: 400;\">one billion<\/span><\/i><span style=\"font-weight: 400;\"> gigabytes, or 10<\/span><span style=\"font-weight: 400;\">18<\/span><span style=\"font-weight: 400;\"> bytes (one quintillion).\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">So, how can you visualize something that big?\u00a0\u00a0<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">If one gigabyte is the size of the earth, then one exabyte is the size of the sun.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Five exabytes likely equals all of the words <\/span><i><span style=\"font-weight: 400;\">ever<\/span><\/i><span style=\"font-weight: 400;\"> spoken by humans.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">To audio record 1 exabyte of data, you would have needed to start speaking into the recording 237, 823 years ago.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">So \u2013 it\u2019s big. Really big. This volume of data includes patient level data, historical trends over time, by diagnosis, by age, and any demographic or segment that you can think of. Even more interesting, this large volume of data can be used as the basis for learning \u2013 otherwise known as artificial intelligence (AI), part of <\/span><a href=\"https:\/\/www.healthitanswers.net\/claims-data-the-good-the-bad-and-the-ugly\/\"><span style=\"font-weight: 400;\">predictive analytics<\/span><\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">No Crystal Ball Here<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To think of a concrete example of predictive analytics, consider what happens when a house is sold. The real estate agent will look at the appraisal, compare the house to other houses that have recently sold in that neighborhood, and consider the overall housing market. By using all of that data, a price is set that the agent <\/span><i><span style=\"font-weight: 400;\">predicts<\/span><\/i><span style=\"font-weight: 400;\"> the house will sell at.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Healthcare uses data to predict which patients will be at a higher risk for readmissions, which might be at a high risk for a blood clot or infection, and which interventions have the best outcomes. Armed with this information, clinicians can work to <\/span><i><span style=\"font-weight: 400;\">prevent<\/span><\/i><span style=\"font-weight: 400;\"> those particular outcomes \u2013 whether that might be changing the care plan, adding a specific intervention, or using a specific medicine.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the world of insurance claims, predictive analytics can be used to head off insurance denials before they have a chance to occur. Why would healthcare organizations want to harness the power of this data?<\/span><\/p>\n<p><b>The AMA\u2019s Administrative Burden Index (ABI) calculates that healthcare providers spend up to 14% of their revenue just to get paid for services.\u00a0<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Consider these <\/span><a href=\"https:\/\/www.sifthealthcare.com\/blog\/2019-healthcare-claims-denials\"><span style=\"font-weight: 400;\">stats<\/span><\/a><span style=\"font-weight: 400;\"> about claims:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">9% of hospital claims are initially denied. This equals an average of almost $5 million per hospital system and a national total of $262 billion.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">90% of all denied claims are preventable! But \u2013 only 66% are recoverable. Effort put into the front end to prevent denials is much more efficient than reworking claims and hoping for the best.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Denials add an extra 16 days in A\/R, hindering cash flow.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Reworking a claim costs $118 \u2013 a whopping total of $8.6 billion in administrative costs. This means that only 35% of providers appeal denials.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Nearly ALL of the top drivers of claims denials are easy to flag in a well-built system:<\/span><\/li>\n<\/ul>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Omitted patient information<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Duplications<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Prior adjudication<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">No payer coverage<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Late submissions<\/span><\/li>\n<\/ul>\n<h2><span style=\"font-weight: 400;\">Look Upstream in the Revenue Cycle<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">To <\/span><a href=\"https:\/\/www.hfma.org\/topics\/hfm\/2018\/september\/61778.html\"><span style=\"font-weight: 400;\">prevent much of the revenue leakage<\/span><\/a><span style=\"font-weight: 400;\"> that healthcare is now plagued with, organizations have to look upstream \u2013 to the many processes that lead up to the claim submission. Departments like patient access, HIM, financial services, and clinical staff all have an impact on the data that goes into a clean claim submission. Unfortunately, many times that data is spread out across a few programs. Patients may be registered in one program, clinical staff chart in another, and financial services use yet another. This siloed and disparate data makes it difficult to review and pull together.\u00a0<\/span><\/p>\n<p><b>Enter technology and analytics. Denials alerting systems take the guesswork and data-crunching out of process improvements.\u00a0<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Software that automatically detects increases in denials, categorizes them by root causes, and organizes your preventative efforts.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Go even further by using modeling that examines account attributes and provides targeted account lists within each alert-group. For example, cardio, outpatient, and CPT123 groups.\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Use dynamic alert groups based on all the combinations of CARC, RARC, and Payer that flag at the appropriate granularity of data.\u00a0<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Also, denials analytics software can flag issues that are known to be common denials reasons, sending the chart for correction <\/span><i><span style=\"font-weight: 400;\">before<\/span><\/i><span style=\"font-weight: 400;\"> the claim is filed.\u00a0<\/span><\/p>\n<p><b>Contact Us<\/b><\/p>\n<p><span style=\"font-weight: 400;\">Qodex is helping healthcare organizations across the country find meaningful patterns in their denials data and address the root causes \u2013 saving time and money. Qodex is part of the innovative Rise Family of Companies. Contact us for a free demo and more information <\/span><a href=\"https:\/\/risefoc.com\/about\/#contact\"><span style=\"font-weight: 400;\">here<\/span><\/a><span style=\"font-weight: 400;\">. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Since the advent of digital healthcare, huge amounts of data have been created and stored. The question is, how do we use it? Big Data, Big Value Big data in healthcare is a valuable asset with a wide range of possibilities. The amount of data healthcare churns out each second is astronomical. Before COVID-19 came [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":9402,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","om_disable_all_campaigns":false,"inline_featured_image":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[9],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Power of Big Data: Preventing Insurance Denials - Rise Family of Companies | Healthrise | Qodex | Agile<\/title>\n<meta name=\"description\" content=\"The Power of Big Data: Preventing Insurance Denials Rise Family of Companies | Healthrise | Qodex | Agile\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/risefoc.com\/the-power-of-big-data-preventing-insurance-denials\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Power of Big Data: Preventing Insurance Denials - 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