NLP in Healthcare: Sources of Data for Text Mining<\/strong><\/p>\n Patient health records, order entries, and physician notes aren\u2019t the only sources of data in healthcare. In fact, 26 million people have already added their genetic information to commercial databases through take-home kits. And wearable devices have opened new floodgates of consumer health data. All told, Emerj lists 7 healthcare data sources that<\/a>, especially when taken together, form a veritable goldmine of healthcare data:<\/p>\n 1. The Internet of Things (think FitBit data)<\/p>\n 2. Electronic Medical Records\/Electronic Health Records (classic)<\/p>\n 3. Insurance Providers (claims from private and government payers)<\/p>\n 4. Other Clinical Data (including computerized physician order entries, physician notes, medical imaging records, and more)<\/p>\n 5. Opt-In Genome and Research Registries<\/p>\n 6. Social Media (tweets, Facebook comments, etc.)<\/p>\n 7. Web Knowledge (emergency care data, news feeds, and medical journals)<\/p>\n Just how much health data is there from these sources? More than 2,314 exabytes by 2020, says BIS Research. For reference, just 1 exabyte is 10^9 gigabytes. Or, written out, 1EB=1,000,000,000GB. That\u2019s a lot of GB.<\/p>\n But adding to the ocean of healthcare data doesn\u2019t do much if you\u2019re not actually using it. And many experts agree<\/a> that utilization of this data is\u2026 underwhelming. So let\u2019s talk about text analytics in healthcare, particularly focusing on new and emerging applications of the technology.<\/p>\n Improving Customer Care While Reducing Medical Information Department Costs<\/strong><\/p>\n Every physician knows how annoying it can be to get a drug-maker to give them a straight, clear answer. Many patients know it, too. For the rest of us, here\u2019s how it works:<\/p>\n 1. You (a physician, patient or media person) call into a biotechnology or pharmaceutical company\u2019s Medical Information Department (MID) Simple in theory, sure. Unfortunately, the pharma\/biotech business is complicated. Biogen, for example, develops therapies for people living with serious neurological and neurodegenerative diseases. When you call into their MID to ask a question, Biogen\u2019s operators are there to answer your inquiry. Naturally, you expect a quick, clear answer. At Biogen Japan, any call that lasts more than 1 minute is automatically escalated to expensive second-line medical directors. Before, Biogen struggled with a high number of calls being escalated because their MID agents spent too long parsing through FAQs, product information brochures, and other resources.<\/p>\n Today, Biogen uses text analytics (and some other technologies) to answer these questions more quickly, thereby improving customer care while reducing their MID operating costs. When you call into their MID, operators use a search application<\/a> that combines natural language processing and machine learning to immediately suggest best-fit answers and related resources to people\u2019s inquiries. MID operators can type in keywords or exact questions and get what they need in seconds. Early testing already shows faster answers and fewer calls sent to medical directors, and the application also helps new hires work at the level of experienced operators, further reducing costs.<\/p>\n Hearing How People Really Talk About and Experience ADHD<\/strong><\/p>\n The human brain is terribly complicated, and two people may experience the same condition in vastly different ways. This is especially true of conditions like Attention Deficit Hyperactivity Disorder (ADHD). In order to optimize treatment, physicians need to understand exactly how their individual patients experience it. But people often tell their doctor one thing, and then turn around and tell their friends and family something else entirely.<\/p>\n Previously, a Lexalytics data scientist used our text analytics and natural language processing to analyze data from Reddit, multiple ADHD blogs, news websites, and scientific papers sourced from the PubMed and HubMed databases. Based on the output, they modeled the conversations to show how people talk about ADHD in their own words.<\/p>\n The results showed stark differences in how people talk about ADHD in research papers, on the news, in Reddit comments and on ADHD blogs. Although our analysis was fairly basic, our methods show how using text analytics in this way can help healthcare providers connect with their patients and develop personalized treatment plans.<\/p>\n Facilitating Value-Based Care Models by Demonstrating Real-World Outcomes<\/strong><\/p>\n Our analysis of conversations surrounding ADHD is just one example in the large field of text analytics in healthcare. Everyone involved in the healthcare value chain, including HCPs, drug manufacturers, and insurance companies are using text analytics as part of the drive towards value-based care models.<\/p>\n Within the value-based care model, and outcome-based care in general, providers and payers all want to demonstrate that their patients are experiencing positive outcomes after they leave the clinical setting. To do this, more and more stakeholders are using text analytics systems to analyze social media posts, patient comments, and other sources of unstructured patient feedback. These insights help HCPs and others identify positive outcomes to highlight and negative outcomes to follow-up with.<\/p>\n Some HCPs even use text analytics to compare what patients say to their doctors, versus what they say to their friends, to identify how they can improve patient-clinician communication. In fact, the larger trend here almost exactly follows the push in more retail-focused industries towards data-driven Voice of Customer: using technology to understand how people talk about and experience products and services, in their own words.<\/p>\n More Applications of Text Analytics and Natural Language Processing in Healthcare<\/strong><\/p>\n The above applications of text analytics in healthcare are just the tip of the iceberg. McKinsey has identified several more applications of NLP in healthcare, under the umbrellas of \u201cAdministrative cost reduction\u201d and \u201cMedical value creation\u201d. Click this link<\/a> to learn more on McKinsey\u2019s website.<\/p>\n Meanwhile, this 2018 paper<\/a> in The University of Western Ontario Medical Journal titled \u201cThe promise of natural language processing in healthcare\u201d dives into how and where NLP is improving healthcare. The authors, Rohin Attrey and Alexander Levitt, divide healthcare NLP applications into four categories. These cover NLP for:<\/p>\n Next, researchers from Sant Baba Bhag Singh University<\/a> explored how healthcare groups can use sentiment analysis. The authors concluded that using sentiment analysis to examine social media data is an effective way for HCPs to improve treatments and patient services by understanding how patients talk about their Type-1 and Type-2 Diabetes treatments, drugs, and diet practices.<\/p>\n Finally, market research firm Emerj<\/a> has written up a number of NLP applications for hospitals and other HCPs, including systems from IQVIA, 3M, Amazon and Nuance Communications. These applications include improving compliance with industry standards and regulations; accelerating and improving medical coding processes; building clinical study cohorts; and speech-to-text for doctors and healthcare providers.<\/p>\n Some More Things to Consider: Data Ethics, AI Fails, and Algorithmic Bias<\/strong><\/p>\n If you\u2019re thinking about building or buying any data analytics system for use in a healthcare or biopharma environment, here are some more things you should be aware of and take into account. All of these are especially relevant for text analytics in healthcare.<\/p>\n
\n2. Your call is routed to the MID contact center
\n3. MID operators reference all available documentation to provide an answer, or punt your question to a full clinician<\/p>\n![](\"http:\/\/customerthink.com\/wp-content\/uploads\/research-paper-taxonomy.png\")
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