{"id":32623,"date":"2023-06-13T03:00:00","date_gmt":"2023-06-13T10:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=32623"},"modified":"2023-06-12T11:02:56","modified_gmt":"2023-06-12T18:02:56","slug":"how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/","title":{"rendered":"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders"},"content":{"rendered":"\n

Healthcare organizations are collecting and storing more patient data than ever. Getting value from that data, however, remains a challenge because much of it is in unstructured form, buried within text-based documents created by clinicians across the healthcare landscape. Fortunately, advances in natural language processing (NLP) are making it possible for healthcare organizations today to derive searchable, actionable insights at scale from data that previously was inaccessible.<\/p>\n\n\n\n

Still, for all the news about the amazing capabilities of a massive language model like ChatGPT, people are realizing the model can invent plausible but untrue information that is fluently combined with true information. For medical NLP applications, this serves as a cautionary tale. <\/p>\n\n\n\n

Thus far, NLP traditionally used in healthcare \u2013 which is highly complex \u2013 has been disappointingly inaccurate. As medical NLP matures to use technology underlying large language models like ChatGPT, the key is leveraging deep-learning models built by medical experts knowledgeable in medical language across the care continuum and making these models explainable to medical experts. This will ensure that the output of the model can be grounded to the original sources of information in medical reports, thus delivering valuable and verifiable information to clinicians.<\/p>\n\n\n\n

Hidden patient data<\/strong><\/p>\n\n\n\n

Healthcare organizations generate massive quantities of historical patient data that is stored in electronic health records (EHR) systems. Most of this data \u2013 roughly 80% \u2013 is in unstructured text. This makes it exceedingly difficult for clinicians at the point of care to access the data without painstakingly reading it all, which often precludes them even knowing the information exists in the data. Unstructured data isn\u2019t actionable unless it can be retrieved, understood, and contextualized.<\/p>\n\n\n\n

Historically, providers have had human experts pore through unstructured data to uncover and interpret relevant clinical information. Such a human-driven process simply can\u2019t be scaled to allow millions of unstructured notes to be accessed and gleaned for useful information in a timely fashion. <\/p>\n\n\n\n

In recent years, healthcare organizations have tried to apply NLP to unstructured clinical data. Results have been subpar, though, because the NLP technology used to date has been far from medical grade. <\/p>\n\n\n\n

Let\u2019s say one of the clinical reports indicates that a patient has had coronary artery bypass surgery. Apart from the fact that clinicians often use short forms like \u2018cabg\u2019 to refer to this type of procedure, just finding the information alone may be insufficient for a clinician, who also may want to know when<\/em> the patient had the procedure, and whether there were complications. Less sophisticated NLP software would not be able to provide proper context for this data to a clinician at the point of care. <\/p>\n\n\n\n

The reason is because older medical NLP software is based on terminologies \u2013 identifying all the different terms for a certain symptom or disorder, for example \u2013 that it would list out and try to find matches for in the data. While this requires some level of medical knowledge, older medical NLP would fail when presented with ambiguity in the form of data it can\u2019t interpret. One byproduct of this ambiguity is that older NLP platforms would produce overwhelming amounts of output \u2013 exactly what a clinician at the point of care doesn\u2019t<\/em> need.<\/p>\n\n\n\n

Clinical support at the point of care<\/strong><\/p>\n\n\n\n

Deep learning is quickly transforming how medical NLP can help both providers at the point of care and medical researchers by quickly finding relevant clinical information within massive amounts of unstructured data. Nonetheless, deep learning models are not without their limitations. They are prone to overconfidence even when they are wrong, they need to be \u201cfine-tuned\u201d on reliable high-quality data, and they need to be explainable to human experts so the extracted information is easily grounded in the clinical text data used to extract the information.<\/p>\n\n\n\n

It is critical, then, to ensure deep learning models are trained on highly accurate medical knowledge that goes beyond recognizing terminologies to making relevant clinical connections and accurate inferences based on a holistic view of data. This presents huge opportunities for healthcare providers, clinical researchers, and payers to discover valuable information related to disease progression, treatment efficacy, population health trends, and many other use cases that would have been infeasible to identify using manual data review and analysis techniques.<\/p>\n\n\n\n

The goal of medical NLP and deep learning models for medicine should not be to replace clinicians so diagnoses and reports can be produced by machines. Rather, it should provide clinicians with the most accurate and relevant clinical information about a patient at the point of care. Accomplishing this will require a collaboration between AI-powered medical NLP and clinicians with vast medical knowledge. It\u2019s these collaborations that will finally deliver on the promise of medical NLP.\u00a0<\/p>\n\n\n\n

About the Author<\/strong><\/p>\n\n\n

\n
\"\"<\/figure><\/div>\n\n\n

Anoop Sarkar, PhD, is the Chief Technology Officer for emtelligent<\/a>, a leader in the development of clinical-grade natural language processing (NLP) software for healthcare organizations. Dr. Sarkar is a renowned expert in machine learning for NLP and also serves as Professor of Computer Science at Simon Fraser University in British Columbia. Dr. Sarkar has published more than 90 research articles with more than 4300 citations. He holds a PhD in Computer Science from the University of Pennsylvania. He published his first NLP paper in the 1993 meeting of the Association for Computational Linguistics (ACL).<\/em><\/p>\n\n\n\n

Sign up for the free insideBIGDATA newsletter<\/a>.<\/em><\/p>\n\n\n\n

Join us on Twitter: https:\/\/twitter.com\/InsideBigData1<\/a><\/em><\/p>\n\n\n\n

Join us on LinkedIn: https:\/\/www.linkedin.com\/company\/insidebigdata\/<\/a><\/em><\/p>\n\n\n\n

Join us on Facebook: https:\/\/www.facebook.com\/insideBIGDATANOW<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, Anoop Sarkar, PhD, Chief Technology Officer, emtelligent, discusses how providing clinicians with the most accurate and relevant information about a patient at the point of care requires a collaboration between AI-powered medical NLP and clinicians with deep medical knowledge. These collaborations will fulfill the promise of medical NLP. <\/p>\n","protected":false},"author":10513,"featured_media":21511,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[526,115,182,180,74,61,67,56,97,1],"tags":[437,1254,635,96],"acf":[],"yoast_head":"\nHow\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders - 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\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, Anoop Sarkar, PhD, Chief Technology Officer, emtelligent, discusses how providing clinicians with the most accurate and relevant information about a patient at the point of care requires a collaboration between AI-powered medical NLP and clinicians with deep medical knowledge. These collaborations will fulfill the promise of medical NLP.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2023-06-13T10:00:00+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2023-06-12T18:02:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/11\/ai-in-healthcare.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"500\" \/>\n\t<meta property=\"og:image:height\" content=\"334\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Editorial Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Editorial Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/\",\"url\":\"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/\",\"name\":\"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2023-06-13T10:00:00+00:00\",\"dateModified\":\"2023-06-12T18:02:56+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning & Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9\",\"name\":\"Editorial Team\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g\",\"caption\":\"Editorial Team\"},\"sameAs\":[\"http:\/\/www.insidebigdata.com\"],\"url\":\"https:\/\/insidebigdata.com\/author\/editorial\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/","og_locale":"en_US","og_type":"article","og_title":"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders - insideBIGDATA","og_description":"In this contributed article, Anoop Sarkar, PhD, Chief Technology Officer, emtelligent, discusses how providing clinicians with the most accurate and relevant information about a patient at the point of care requires a collaboration between AI-powered medical NLP and clinicians with deep medical knowledge. These collaborations will fulfill the promise of medical NLP.","og_url":"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/","og_site_name":"insideBIGDATA","article_publisher":"http:\/\/www.facebook.com\/insidebigdata","article_published_time":"2023-06-13T10:00:00+00:00","article_modified_time":"2023-06-12T18:02:56+00:00","og_image":[{"width":500,"height":334,"url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/11\/ai-in-healthcare.jpg","type":"image\/jpeg"}],"author":"Editorial Team","twitter_card":"summary_large_image","twitter_creator":"@insideBigData","twitter_site":"@insideBigData","twitter_misc":{"Written by":"Editorial Team","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/","url":"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/","name":"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders - insideBIGDATA","isPartOf":{"@id":"https:\/\/insidebigdata.com\/#website"},"datePublished":"2023-06-13T10:00:00+00:00","dateModified":"2023-06-12T18:02:56+00:00","author":{"@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9"},"breadcrumb":{"@id":"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/insidebigdata.com\/2023\/06\/13\/how-nlp-can-provide-deeper-actionable-data-insights-for-all-healthcare-stakeholders\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/insidebigdata.com\/"},{"@type":"ListItem","position":2,"name":"How\u00a0NLP Can Provide Deeper, Actionable Data Insights for All Healthcare Stakeholders"}]},{"@type":"WebSite","@id":"https:\/\/insidebigdata.com\/#website","url":"https:\/\/insidebigdata.com\/","name":"insideBIGDATA","description":"Your Source for AI, Data Science, Deep Learning & Machine Learning Strategies","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/insidebigdata.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9","name":"Editorial Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g","caption":"Editorial Team"},"sameAs":["http:\/\/www.insidebigdata.com"],"url":"https:\/\/insidebigdata.com\/author\/editorial\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/11\/ai-in-healthcare.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-8ub","jetpack-related-posts":[{"id":17898,"url":"https:\/\/insidebigdata.com\/2017\/05\/16\/nlp-can-help-healthcare-catch\/","url_meta":{"origin":32623,"position":0},"title":"How NLP Can Help Healthcare \u201cCatch Up\u201d","date":"May 16, 2017","format":false,"excerpt":"In this special guest feature, Simon Beaulah, Senior Director of Healthcare at Linguamatics, discusses how natural language processing (NLP) has become a crucial tool in healthcare and the life sciences as these sectors struggle to catch up to other industries and transform their big data into actionable data.","rel":"","context":"In "Analytics"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":18152,"url":"https:\/\/insidebigdata.com\/2017\/06\/15\/3-reasons-natural-language-processing-can-help-healthcare-providers\/","url_meta":{"origin":32623,"position":1},"title":"3 Reasons How Natural Language Processing Can Help Healthcare Providers","date":"June 15, 2017","format":false,"excerpt":"In this special guest feature, Ravi Gopalan, Founder & Chief Technology Officer at Binary Fountain, discusses how healthcare providers can begin to understand and act on the increasing amount of data with patient feedback management solutions using healthcare-specific natural language processing (NLP).","rel":"","context":"In "Analytics"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":13870,"url":"https:\/\/insidebigdata.com\/2015\/10\/13\/ready-for-prime-time-natural-language-processing-for-healthcare\/","url_meta":{"origin":32623,"position":2},"title":"Ready for Prime Time: Natural Language Processing for Healthcare","date":"October 13, 2015","format":false,"excerpt":"In this special guest feature, Anand Shroff, CTO of Health Fidelity, discusses the challenges to using natural language processing in the hospital setting.","rel":"","context":"In "Analytics"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":33125,"url":"https:\/\/insidebigdata.com\/2023\/08\/15\/evolution-of-conversational-intelligence-in-healthcare\/","url_meta":{"origin":32623,"position":3},"title":"Evolution of Conversational Intelligence in Healthcare","date":"August 15, 2023","format":false,"excerpt":"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\u2026","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2023\/08\/Machine_Learning_shutterstock_742653250_special.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":19625,"url":"https:\/\/insidebigdata.com\/2017\/12\/20\/trinetx-unveils-natural-language-processing-improve-protocol-design-accelerate-identification-patients-clinical-trials\/","url_meta":{"origin":32623,"position":4},"title":"TriNetX Unveils Natural Language Processing to Improve Protocol Design and Accelerate Identification of Patients for Clinical Trials","date":"December 20, 2017","format":false,"excerpt":"TriNetX, the global health research network for healthcare organizations, biopharmaceutical companies, and Contract Research Organizations (CROs), announced the general availability of its Natural Language Processing (NLP) service.","rel":"","context":"In "Big Data"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":21021,"url":"https:\/\/insidebigdata.com\/2018\/09\/03\/use-nlp-extract-unstructured-medical-data-text\/","url_meta":{"origin":32623,"position":5},"title":"The Use of NLP to Extract Unstructured Medical Data From Text","date":"September 3, 2018","format":false,"excerpt":"In this contributed article, Luke Potgieter, tech advisor for John Snow Labs, discusses the important of NLP for the healthcare industry, and offers two common AI-in-healthcare use cases. NLP is a very useful and important tool for use in AI in dealing with unstructured data.","rel":"","context":"In "AI Deep Learning"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2018\/08\/John-Snow-Labs-fig.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/32623"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/10513"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=32623"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/32623\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/21511"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=32623"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=32623"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=32623"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}