{"id":33125,"date":"2023-08-15T03:00:00","date_gmt":"2023-08-15T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=33125"},"modified":"2023-08-15T09:34:49","modified_gmt":"2023-08-15T16:34:49","slug":"evolution-of-conversational-intelligence-in-healthcare","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/08\/15\/evolution-of-conversational-intelligence-in-healthcare\/","title":{"rendered":"Evolution of Conversational Intelligence in Healthcare"},"content":{"rendered":"\n<p>Healthcare\u2019s adoption of <a href=\"https:\/\/www.managedhealthcareexecutive.com\/view\/advancing-healthcare-innovation-through-ai-adoption\" target=\"_blank\" rel=\"noreferrer noopener\">artificial intelligence (AI) and machine learning (ML) has accelerated quickly<\/a>. Data <a href=\"https:\/\/onlinemba.smu.edu\/blog\/future-of-data-analytics-in-healthcare\/#:~:text=While%20every%20facet%20of%20the,making%20preventative%20recommendations%20to%20patients\" target=\"_blank\" rel=\"noreferrer noopener\">analytics has transformed the industry<\/a> with insights from the information it gathers influencing how medical researchers, physicians, policy advocates and patients operate within the system. Data gleaned from AI improves healthcare in many areas, including:<\/p>\n\n\n\n<ul>\n<li>How organizations conduct medical studies.<\/li>\n\n\n\n<li>Patients\u2019 understanding of the costs of health insurance and medical tests.<\/li>\n\n\n\n<li>Physicians\u2019 abilities to make preventative recommendations to patients.<\/li>\n<\/ul>\n\n\n\n<p>Organizations also leverage this data to understand patient pain points, identify where call center agents need more training, and uncover insights from customer experience and marketing.<\/p>\n\n\n\n<p>But as the scope of data increases (with <a href=\"https:\/\/techjury.net\/blog\/how-much-data-is-created-every-day\/#gref\" target=\"_blank\" rel=\"noreferrer noopener\">over 200 zettabytes of data in cloud storage worldwide by 2025<\/a>), healthcare organizations will need more efficient methods to collect, evaluate and analyze it to gain insights for informing strategic decision-making.<\/p>\n\n\n\n<p><strong>AI\u2019s contribution to data analysis<\/strong><\/p>\n\n\n\n<p>AI-driven tools play an increasingly significant role in analytics, including those used to improve customer experience, by extracting valuable, actionable data insights. AI and ML enable healthcare organizations to more effectively listen to \u2014 and better understand \u2014 their customers\u2019 voices and solve common issues or barriers, like the &#8220;Eddy Effect,&#8221; disrupting customer interactions.&nbsp;<\/p>\n\n\n\n<p>There is, however, a caveat: An ML model\u2019s reliability is only as good as the dataset on which it\u2019s trained. These training and learning datasets are <a href=\"https:\/\/learn.g2.com\/training-data\" target=\"_blank\" rel=\"noreferrer noopener\">essential to ML models<\/a>, helping them make accurate predictions and perform desired tasks. ML relies on comprehensive, relevant, representative and uniform training data generated from unstructured data. The best ML models have a <a href=\"https:\/\/stephenallwright.com\/good-accuracy-score\/\" target=\"_blank\" rel=\"noreferrer noopener\">70% to 90% accuracy rate<\/a>.&nbsp;<\/p>\n\n\n\n<p>In healthcare, this unstructured data comes from sources, such as healthcare-focused conversations. The right technology can collect, evaluate and analyze conversational data at scale.&nbsp;<\/p>\n\n\n\n<p>Conversational data generated via call centers, chatbots and emails \u2014 and analyzed via AI, ML and natural language processing (NLP) \u2014&nbsp; will become increasingly critical in helping organizations assess, understand and resolve CS issues. Unlike traditional NPS and surveys, conversational data provides more nuanced, contextual and richer insight across diverse business functions. It enables organizations to:<\/p>\n\n\n\n<ul>\n<li>Identify customer pain points and challenges.<\/li>\n\n\n\n<li>Predict future events.<\/li>\n\n\n\n<li>Automate work processes and streamline systems.<\/li>\n\n\n\n<li>Enhance personalization.<\/li>\n<\/ul>\n\n\n\n<p><strong>The value of qualitative and quantitative insights<\/strong><\/p>\n\n\n\n<p>Data-backed storytelling combines quantitative and qualitative data to provide meaning. When it\u2019s brought to life, data enables people to connect to concepts that they find most valuable and personal. Quantitative data helps create concrete information in numerical form, while qualitative data helps create understanding and context behind the numbers.<\/p>\n\n\n\n<p>Imagine this scenario: a life sciences company sought solutions for improving its patients\u2019 journeys and standardizing call center agent messaging. While they\u2019d previously gathered insights from a small sampling of patient calls, the data wasn\u2019t fully representative.&nbsp;<\/p>\n\n\n\n<p>The company adopted AI technology to analyze all conversational data from when patients began therapy to their last communication, explicitly focusing on patient journeys with high call volumes. The analysis enabled the organization to identify where:<\/p>\n\n\n\n<ul>\n<li>Agent messaging lacked consistency, causing patient stress or confusion.<\/li>\n\n\n\n<li>Patients developed routine or confusion (The Eddy Effect) within the journey.<\/li>\n\n\n\n<li>Opportunities existed for the organization to provide additional support to caregivers and patients.<\/li>\n<\/ul>\n\n\n\n<p>To identify the challenges these patients faced, the organization applied qualitative <em>and<\/em> quantitative approaches to the data it collected and analyzed. While these approaches appear to be opposite sides of the same coin, they complement each other, blending head and heart, big data and data immersion, and AI and humans, enabling organizations to tell a contextual, data-backed story. Storytelling is integral to understanding insights. Why? Because storytellers (those who analyze the data) can analyze all the touchpoints in a patient\u2019s journey to understand each patient\u2019s barriers.&nbsp;<\/p>\n\n\n\n<p><strong>Listening at scale helps solve common issues<\/strong><\/p>\n\n\n\n<p>While contact center agents generally focus on helping patients solve problems one interaction at a time, conversational intelligence enables organizations to hone in on systemic issues. AI identifies data and insights on the macro and micro levels, from issues preventing patients from getting what they need to interactions on a thematic level.&nbsp;<\/p>\n\n\n\n<p>Listening directly to patients&#8217; voices enables healthcare leaders to maximize the data their organizations have already collected to deliver insights and ROI. Comprehensive, actionable patient feedback derived from conversational data empowers leaders to create and implement patient-centric strategies. This strategy goes beyond meeting customer expectations \u2014&nbsp;and provide the most effective support. The best way to achieve this goal? By listening at scale.<\/p>\n\n\n\n<p><strong>About the Author<\/strong><\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-full\"><img decoding=\"async\" loading=\"lazy\" width=\"125\" height=\"187\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/08\/Michael-Armstrong-Headshot.jpg\" alt=\"\" class=\"wp-image-33126\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/08\/Michael-Armstrong-Headshot.jpg 125w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2023\/08\/Michael-Armstrong-Headshot-100x150.jpg 100w\" sizes=\"(max-width: 125px) 100vw, 125px\" \/><\/figure><\/div>\n\n\n<p><em>Michael Armstrong is the Chief Technology Officer at <a href=\"https:\/\/authenticx.com\/\" target=\"_blank\" rel=\"noreferrer noopener\">Authenticx<\/a> and a foundational leader in building our solution infrastructure. In this role, he leads a team of data engineers and scientists to translate big visionary ideas into practical and actionable software. Michael has extensive experience in engineering, data architecture, product development, and business intelligence.&nbsp;<\/em><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a href=\"http:\/\/inside-bigdata.com\/newsletter\/\" target=\"_blank\" rel=\"noreferrer noopener\">newsletter<\/a>.<\/em><\/p>\n\n\n\n<p><em>Join us on Twitter:&nbsp;<a href=\"https:\/\/twitter.com\/InsideBigData1\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on LinkedIn:&nbsp;<a href=\"https:\/\/www.linkedin.com\/company\/insidebigdata\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n<p><em>Join us on Facebook:&nbsp;<a href=\"https:\/\/www.facebook.com\/insideBIGDATANOW\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this contributed article, Michael Armstrong, Chief Technology Officer at Authenticx, discusses how AI-driven tools play an increasingly significant role in analytics, including those used to improve customer experience, by extracting valuable, actionable data insights. AI and ML enable healthcare organizations to more effectively listen to \u2014 and better understand \u2014 their customers\u2019 voices and solve common issues or barriers.<\/p>\n","protected":false},"author":10531,"featured_media":33055,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,65,115,182,180,74,67,268,56,97,1],"tags":[437,839,1085,334,96],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Evolution of Conversational Intelligence in Healthcare - insideBIGDATA<\/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:\/\/insidebigdata.com\/2023\/08\/15\/evolution-of-conversational-intelligence-in-healthcare\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Evolution of Conversational Intelligence in Healthcare - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, Michael Armstrong, Chief Technology Officer at Authenticx, discusses how AI-driven tools play an increasingly significant role in analytics, including those used to improve customer experience, by extracting valuable, actionable data insights. 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