Leveraging Social Media for Patient Insights in Pharmaceutical Industry. Narender Dureja, Shyam Chugh & Mannat Wadehra WHITE PAPER

Transcription

1 Leveraging Social Media for Patient Insights in Pharmaceutical Industry Narender Dureja, Shyam Chugh & Mannat Wadehra WHITE PAPER JANUARY 2014

2 TABLE OF CONTENTS EXECUTIVE SUMMARY 3 CURRENT SCENARIO AND OPPORTUNITY 4 TECHNOLOGY AS THE KEY ENABLER 6 HCL LIFE SCIENCES & HEALTHCARE PRACTICE 12 ABOUT THE AUTHORS 14 ABOUT HCL 15 2

3 EXECUTIVE SUMMARY Businesses ask this question all the time, How can my business survive in this new technological age? The answer lies in understanding their own and their competitors customers. A highly effective way of doing this is to leverage the power and pervasiveness of social media and to utilize the information available from these sources to bring the collective voice of customers and competitor s customers to a business. How social media has changed the art of marketing is there for everyone to see. Today, each business in every industry understands the importance of social media in developing business strategies or brand campaigns. When a person goes on the internet and talks about a brand or a company through Facebook/Twitter or blogs, that information gets stored on the web and is freely available. The topics people are discussing and the sentiment for each of discussed topics can be monitored and analyzed. Is it positive, negative or neutral? It may seem to be an overwhelming task to aggregate and quantify the data in a meaningful way. But with the availability of current tools and technology, it can be aggregated and quantified and when put to good use, the resulting insights are very beneficial to an organization. There has been an upsurge of social media usage by the industry as a whole. Manufacturing, Retail, Media & Entertainment, Travel and Hospitality all these industries have leveraged social media in unique ways, with spectacular results. For example, Retail industry has been leveraging insights from social media to find a correlation between product sales, support and customer voice to understand issues impacting consumer satisfaction. Companies today compare their product and services against key competitors that help them refine product positioning and develop data-driven campaigns targeted towards the right audience. Pharmaceutical industry has refrained from participating in social media forums due to a lack of clarity on what they can do while still remaining compliant with existing FDA guidelines. We believe that like all other industries, pharmaceutical industry should also listen to social media for numerous benefits that it can derive across the pharmaceutical value chain drug development, procurement, sales & marketing and post market launch etc. This document focuses on how the pharmaceutical industry can benefit from listening to patients sentiments on social media and derive insights across its brand, off label use, side effects, patient needs etc. 3

4 CURRENT SCENARIO AND OPPORTUNITY Nowadays, popular social networks such as Facebook, Twitter, YouTube, healthcare blogs and forums have become an essential part of a patient s information journey that usually starts with Google. Information procured during the process has empowered the patient in making decisions leading to a paradigm shift; patients are now more aware of their disease and exercise a greater say in the treatment process that impacts healthcare outcomes. They want to understand different options around their treatment and would like to discuss these with their friends and family members for additional opinions. A patient s key objective while interfacing with social media is to seek and give support while understanding the ups and downs of a health condition and its treatment. Patients often record and detail out their daily progress, with the social media platform becoming an electronic health record journal that invites advice and support from others. This whole process creates an emotional connection and is one of the reasons for social media bonding. This might explain why patients with rare diseases or conditions often have higher degrees of engagement within online forums, which provide them an easy access to others with similar circumstances and experiences. Also, patients often turn to social media for second opinions on managing their condition and treatment. HCL is of the opinion that it is not only patients but physicians, friends, family members, researchers and others that are also involved in the healthcare decisions. Rise of social media has given patients and physicians an avenue for practicing peer-to-peer healthcare. There are multiple healthcare websites, blogs, forums and communities supporting the same. Drug usage Effectiveness Treatment Prescriber segment I take Effexor for my IBS spasms, as my family physician prescribed. I wouldn t have known how well they work for IBS if I hadn t become severely depressed when my husband passed away, and I was put on Pemelor. I stopped taking that one because of weight gain. Just about all the antidepressants work for the pain on IBS. I hope you find help. It helped me. Side effect Stopped; drop-outs Here is an example of patient talking about her condition, treatment, prescriber, drug usage, effectiveness and side effects. Such information when packed in large numbers and pushed into analytical models, can generate useful insights for pharmaceutical companies. 4

5 All these social interactions and engagements influence the patient s decision journey and delivery of healthcare services and products, making it a channel that pharmaceutical companies need to understand and influence. Social media analytics creates value in two ways direct and influenced. Direct value is created when an existing process aimed at generating a specific outcome can be done cheaper and/or faster using social media such as detailing, campaigns and patient education programs etc. Influenced value is created when an entirely new outcome is possible to be achieved using social media, such as determining brand reputation or disease management needs of patients. Overall, social media analytics helps pharmaceutical companies address issues such as: y Conversation themes around a disease y Information needs of patients y Patients primary concerns about a brand y Brand s sentiment over time y Reputation of a brand compared to competition y Leading indicators and trends for patients switching brands y Any adverse events that are reported by patients y Effects of drug combinations y Insights around drug availability, packaging and pricing affecting drug usage patterns y Views about patients drug compliance y Off-label use of a drug reported by patients Pharmaceutical companies should monitor these conversations relevant to their brands, industry and competition. Along with a rigorous assessment of current social media activities, this is a key to developing future strategies and determining goals focused on measurable return on investment. y Conduct forward and backward listening assessments. It is important to monitor relevant conversations both in real-time (forward listening) and historically (backwards listening). By tracing topics for the past year or so, companies can create a baseline of social media activity/sentiment for a specific drug or brand y Monitor product or service launches to gauge reaction and make adjustments if necessary y Improve current social media outreach by identifying key influencers and channels worth engaging. Look for opportunities to be a thought leader or share your expertise without directly promoting a product or brand y Improve customer support to increase customer satisfaction levels and improve brand loyalty y Compile business and market intelligence reports to stay one step ahead of the competition 5

6 TECHNOLOGY AS THE KEY ENABLER Technology plays an important role in understanding these conversations and generating needed insights. Following are three main steps: 1. Data Preparation and Gathering: This involves collecting the data from various sources like Facebook, Twitter and Web portals (for e.g. askapatient.com, mediguard.com), transforming it to an enterprise data model and ingesting it into a centralized enterprise store. 2. Data Analysis: Once the input data is passed through various analytical techniques and algorithm, structured pieces of information from the unstructured content like comments and descriptions are extracted and integrated with the existing structured pieces like date of reporting, gender and age etc. This helps identify trends and patterns that would not have been possible otherwise. 3. Data Visualization: This involves presenting the trends and patterns from the unstructured data as well as the structured data in various interactive and drill-down visual formats to display the generated insights. Examples include tag clouds, location map, bird s eye view and network maps etc. to quickly highlight the significant topics and keywords. Following are some of the data analysis techniques powered by Natural Language Processing (NLP) capabilities of new age text analytics provided by industry software vendors: 1. Named Entity Recognition: This is used to detect and extract various entities automatically from comments, blogs and conversations, including the names of the persons, organizations, locations, and home addresses etc. This information can help discover some useful insights. For example: a. While analyzing the comments related to Osteoporosis on Askapatient.com, the name of Gillian Sanson was automatically highlighted as a significant topic. When the corresponding comments were read, it was realized that people were talking about the book, The Myth of Osteoporosis and that Gillian is an important influencer in this field. b. While analyzing comments related to some diabetes drugs on Askapatient. com and Mediguard.com, Canada was highlighted as a location. It was found that some people in USA have been ordering the cheaper generic version of the drugs from Canada. One useful type of Named Entity is Keyword or Term of interest. This is highlighted as a significant term based on its NLP and statistical modelling algorithms. These automatically identified keywords can help with a quick summary or essence of the large volumes of conversations. For example, while analyzing the side effects reported for various diabetes drugs, nausea was the main keyword detected for one drug and weight gain was detected as the significant keyword for another drug. Thus it was very easy and quick to present the main side effect of these two drugs. 6

7 2) Taxonomical Terms Recognition: This is the recognition of terms within different dictionaries, taxonomies or ontologies created by enterprises over their years of operations to manually categorize and classify information. These terms represent significant concepts around which business entities are organized. These taxonomies also contain a description of terms used to tag the content with the respective term. If done manually, this tagging could be extremely costly and time intensive. Text analytics techniques enable automatic tagging to quickly identify the relevant comments, blogs and posts of interest. Taxonomies are also created by various standards organizations that can be re-used by the industry. For example, US National Library of Medicine has created a comprehensive vocabulary, Medical Subject Headings (MeSH) for the purpose of indexing journal articles and books in the life sciences. 3) Machine Learning based Classification: This involves usage of a manually classified data set to help the text analytics tool build a model for classifying comments into various categories using algorithms including Naïve Bayes and Support Vector Machine (SVM) etc. This data set is also referred to as Training data set as it is used to train the tool. Once the model has been built, this can then be applied to classify new comments automatically. For example, this technique can be used to recognize the sentiment (positive/ negative/neutral) or classify the comment as enquiry/complaint/suggestion or determine if a customer is likely to churn or a comment pertains to a side effect or efficacy of a drug. 4) Machine Learning based Clustering: This involves unsupervised learning techniques to automatically divide the comments, blogs, etc. into various buckets and extract the main characteristics of each bucket using algorithms such as K-means. This helps in micro-segmentation to uncover co-relationships. For example, while analyzing side-effects reported by various diabetes drugs, it was found that nausea was significantly related to a drug. Once the data has been analyzed, findings can be presented through various powerful visualization techniques to enable the analysts and business users to quickly interpret trends and patterns. Some of the new age visualization techniques are: 1) Tag Clouds: This helps compare the relative significance of different topics/ keywords/terms of interest. Here is an example of a tag cloud generated from the side-effects enumerated for a diabetes drug. This visually represents nausea being the most significant side-effect. 7

8 2) Location Maps: This helps plot the location names on a map and quickly visualize the various locations of interest. Here is an example of location names automatically extracted and plotted on a map. Bigger circles represent higher frequency of occurrence. 3. Bird s Eye View: This helps to show multiple dimensions of information together in a single view. Here is an example of a bird s eye view depicting automatically generated keywords, drug names, age of respondents and autorecognized MeSH terms for a set of comments. 8

9 Keyword Drug Name Age Mesh Nausea (186) Byetta (156) Lantus (153) Blood sugar (148) Side effects (143) Insulin (134) Doctor (114) Diabetes (100) Ibs (98) Pounds (89) Lantus (88) Metformin (87) Medication (77) Actos (574) Lantus (358) Byetta (357) Januvia (245) Victoza (146) Invokana (4) (438) (400) (386) (298) (82) (67) (9) (4) Weight Gain (41) Body Weight Changes (36) News [Publication Type] (32) Publication Formats [Publication Type] (32) Digestive System Neoplasms (25) Pancreatic Neoplasms (25) Body Regions (24) Nausea (22) Risk (20) Foot (18) Glucose (18) Neoplasms (18) Blood Glucose (16) Signs and Symptoms, Digestive (16) Pharmaceutical Preparations (15) 4) Network Graphs: This helps visualize the relationships between various terms/ concepts. Here is an example that presents relationship of Weight Gain with Metformin. When these comments were analyzed further it was observed, people after moving away from Metformin to other diabetic drug reported Weight Gain and have mentioned the same repeatedly in their comments. 5) Pie Charts and Bar Charts: These help to visualize the classification and insights 9

10 derived from structured data analysis. Here are a few examples: a. Analysis of the comments related to diabetes helped in auto-classification into categories of Efficacy, Safety and Adverse Events using Machine Learning Classification techniques. This is plotted as a pie chart to represent the percentage distribution. Drug Effect Safety Efficacy Adverse Events None b. Plotting the age and gender data pertaining to diabetes and osteoporosis revealed powerful insights considering structured and unstructured data analyzed together. I. For diabetes, age plot shows this disease majorly affecting people from 36 years onwards Count Comment Count II. For diabetes, gender plot presents no significant gender bias. 10

11 Gender Undetermined Male Female *Undetermined is for comments where the gender information was missing III. For osteoporosis, age plot shows disease prevalence within the 50+ age group k k Count Comment Count IV. For osteoporosis, gender plot shows the disease majorly affecting females. Gender Undetermined Male Female 11

12 HCL LIFE SCIENCES & HEALTHCARE PRACTICE HCL is one of the market leaders in life sciences & healthcare IT service providers in India. HCL s clientele includes seven of the top global pharmaceutical companies, six of the top ten health insurers, one of the largest payer conglomerates in US and top five service providers in UK. With experienced and certified technology and domain specialists, HCL offers services in the critical areas of drug discovery, clinical development, drug safety, pharmacovigilance, regulatory compliance, manufacturing and plant automation, sales and marketing, member experience management, fraud, waste and abuse management, ICD-10 transformation and others. What the market lacks today is an enabler who specializes in data science around patient sentiments; data preparation, models to relate and analyse sentiments based on medical taxonomies and state of the art visualization tools. HCL Technologies, with years of experience and growing success stories every year, has the aptitude and the competency in patient sentiment monitoring and analytics. HCL has developed its own text/social analytics solutions IllumInfo and Socialytics. A. ILLUMINFO IllumInfo is a search and analytics tool that enables text analytics on the enterprise search platform of the organization. It uses rich-text analysis to bring forward new and actionable insights from the enterprise content. IllumInfo offers many benefits: a. Transforms information into insights by uncovering trends, patterns and relationships from enterprise content through intuitive analytics b. Provides advanced content discovery and exploration interface that can be used by users to drill down and investigate insights c. Extends the organization s current investment in Enterprise Search Platform by plumbing the output from text analytics software into it 12

13 B. SOCIALYTICS In this era of social sites and hyper personalization, there is plenty of unstructured data in the social media which can be utilized to understand consumers preferences and their inclinations towards specific products or practices. To structure and analyze the large volume of social data, every organization needs a sophisticated yet easy-to-use tool for continuous monitoring and analysis. Socialytics is a tool developed by HCL to answer this problem. It measures, enhances and leverages the social media presence of a brand/ individual and provides detailed reporting and visualization of social trends and sentiments. It can serve as a sophisticated personalization engine to understand customer preferences. Socialytics can also serve as a single inbox for all the big social media channels like Facebook, Twitter, YouTube, LinkedIn etc., used for social CRM. 13

14 ABOUT THE AUTHORS Narender Dureja Narender Dureja is part of Life Sciences & Healthcare Practice at HCL, currently designated as Associated Vice President. He has been in the software industry for past 23 years handling Program & Delivery Management, Strategy, Innovation, Consulting and Business Development across industry key verticals; Life Sciences and Healthcare, Compliance, Direct Marketing, Enterprise Resource Planning and Business Analytics. He currently heads the Global Life Sciences & Healthcare Analytics Practice at HCL. Shyam Chugh Shyam is part of HCL s Enterprise Content Management & Portal Practice and focuses on building capability around enterprise search and unstructured content analytics. He has over 15 years of experience in software development encompassing both services and product development. Mannat Wadehra Mannat is part of Life Sciences and Healthcare Practice at HCL. His focus areas are Research and Business Development around Big Data and Text Analytics in Life Sciences and Healthcare. He is an MBA with over 4 years of experience in implementation of complex business solutions in US Healthcare domain. 14

15 ENGINEERING AND R&D SERVICES CUSTOM APPLICATION SERVICES Contact : Healthcare@hcl.com ENTERPRISE APPLICATION SERVICES ENTERPRISE TRANSFORMATION SERVICES IT INFRASTRUCTURE MANAGEMENT BUSINESS PROCESS OUTSOURCING ABOUT HCL About HCL Technologies HCL Technologies is a leading global IT services company working with clients in the areas that impact and redefine the core of their businesses. Since its emergence on global landscape after its IPO in 1999, HCL has focused on transformational outsourcing, underlined by innovation and value creation, offering an integrated portfolio of services including software-led IT solutions, remote infrastructure management, engineering and R&D services and Business services. HCL leverages its extensive global offshore infrastructure and network of offices in 31 countries to provide holistic, multi-service delivery in key industry verticals including Financial Services, Manufacturing, Consumer Services, Public Services and Healthcare & Life sciences. HCL takes pride in its philosophy of Employees First, Customers Second which empowers its 88,332 transformers to create real value for the customers. HCL Technologies, along with its subsidiaries, had consolidated revenues of US$ 5.0 billion, as on 31st December 2013 (on LTM basis). For more information, please visit About HCL Enterprise HCL is a $6.4 billion leading global technology and IT enterprise comprising two companies listed in India HCL Technologies and HCL Infosystems. Founded in 1976, HCL is one of India s original IT garage start-ups. A pioneer of modern computing, HCL is a global transformational enterprise today. Its range of offerings includes product engineering, custom & package applications, BPO, IT infrastructure services, IT hardware, systems integration, and distribution of information and communications technology (ICT) products across a wide range of focused industry verticals. The HCL team consists of over 91,000 professionals of diverse nationalities, who operate from 31 countries including over 500 points of presence in India. HCL has partnerships with several leading global 1000 firms, including leading IT and technology firms. For more information, please visit