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Member Engagement Using Sentiment Analysis for Health Plans

Member Engagement Using Sentiment Analysis for Health Plans

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Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. Sentiment analysis for health plans deals with member opinions to improve healthcare services and patient experience.

Sentiment analysis (or opinion mining) is a natural language processing technique used to determine whether data is positive, negative or neutral. Sentiment analysis for health plans deals with member opinions to improve healthcare services and patient experience.

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Member Engagement Using Sentiment Analysis for Health Plans

  1. 1. This document is confidential and contains proprietary information, including trade secrets of CitiusTech. Neither the document nor any of the information contained in it may be reproduced or disclosed to any unauthorized person under any circumstances without the express written permission of CitiusTech. Member Engagement using Sentiment Analysis for Health Plans March 2021 | Author: Nikita Agrawal, Healthcare BA CitiusTech Thought Leadership
  2. 2. 2 ▪ Sentiment Analysis: Introduction ▪ Sentiment Analysis: Data Sources ▪ Sentiment Analysis: Use Cases ▪ Sentiment Analysis: Process Flow ▪ Sentiment Analysis: Payer Landscape ▪ Sentiment Analysis: Challenges and Mitigation ▪ References Agenda
  3. 3. 3 What is Sentiment Analysis? ▪ Process of understanding consumer experience using text data ▪ Utilize Natural Language Processing (NLP), Machine Learning (ML), and Deep Text Analytics to obtain insights from text data ▪ Sentiment analysis algorithms fall under three emotions: Positive, Neutral and Negative ▪ 72% and 68% of companies believe that sentiment analysis will lead to improved customer experience and reduce overall cost, respectively Benefits for Health Plans ▪ Valuable insights from unstructured data could help health plans understand the emotions of consumers and streamline offerings ▪ Healthcare payers can leverage consumer sentiment analysis to maximize member satisfaction index and analyze member issues at a granular level Sentiment Analysis: Introduction Better Customer Service ▪ Understand the emotional state and create a positive experience for members in need of assistance Improved Products and Services ▪ Interpret member needs by analyzing data generated on a payer’s website, call center, social media, and other online touchpoints Consumer Delight ▪ Forecast demand by analyzing trends to create a delightful consumer experience and increasing engagement
  4. 4. 4 Sentiment Analysis: Data Sources Sentiment Analysis enables health plans to achieve better consumer engagement, improved member experience, and reduce member attrition Reach Frequency of Data High Medium Low Member level ▪ Call Center ▪ Chatbot ▪ Patient Feedback ▪ Provider/PCP Notes ▪ Email ▪ Letters through mail ▪ Survey Population level ▪ Social Media ▪ Blogs -
  5. 5. 5 Sentiment Analysis: Use Cases * Additional effort from payer's end – Floating survey to each member after every physician visit ( Random / for each member) Use Cases Process and Benefits Improved Brand Image ▪ Brand mentions on social media platforms share insights into customer sentiment ▪ Blogs published by members provide information on member sentiments related to policy changes, claims processing, drug pricing, or just brand perception Enhanced Product Development ▪ Measurement of sentiments on health plans, process flow for claims management, pricing, provider allotment, and more, will help improve current services and products ▪ Higher negative sentiment towards a process acts as an indicator for improvement Measuring Provider Performance* ▪ Collect feedback as a proxy survey from member every time they avail provider services ▪ Determine a comprehensive sentiment for each provider based on survey to improve member satisfaction and engagement Call Center Functionality ▪ Categorizing calls based on positive/negative/neutral sentiments, along with determining trends based on voice and text data (with visual display) ▪ Analyzing related phrases, providing context for root cause analysis, and suggesting actionable items (as per mood, level of confusion, and more) to create a positive feedback
  6. 6. 6 Sentiment Analysis: Process Flow Data Collection Data ingested from social media, blogs, call center transcripts, website, surveys, chatbots, and more Pre-processing Data cleaning and data transformation Filtering Repeated letters, stop words, and blank spaces are removed. Data is normalized Sentiment Collection Feature selection Sentiment Classification Polarity (positive, negative, neutral), and KPIs (accuracy, precision, recall) Use Case - Call Center Functionality ▪ Top 10 topics members enquired about ▪ Sentiment for every topic ▪ Comprehensive call sentiment Topic Categorization Context of Sentiment Determined Data Converted from Voice to Text Sentiment Classification Actionable Insights Member calls customer care Statement ▪ This is the third time I am calling for claims ▪ I don’t understand this penalty ▪ What are the benefits if I switch back? Call Center Voice Data Emotion ▪ Anger ▪ Distress ▪ Curious
  7. 7. 7 Payers are leveraging sentiment analysis to increase customer engagement and reduce overall operational cost. Health Plan Examples ▪ Humana automated its tasks with unstructured data using natural language processing and sentiment analysis. Humana uses voice and sentiment analysis in its call centers to improve customer engagement/experience ▪ Florida Blue uses sentiment and speech analytics to reduce operational cost, and to gauge member sentiment, trend analysis, and generate additional insights ▪ To understand member opinion on brand perception and claims related queries, BCBS NC implemented sentiment analysis and linguistic modelling. This also resulted in significant administrative cost saving Sentiment Analysis: Payer Landscape
  8. 8. 8 Challenges Managing Unstructured Data ▪ Context identification in unstructured data is challenging ▪ Understanding which data can give what insight is important Limited Knowledge on Usage and Utilization ▪ Understanding the tools, functionalities and impact ▪ Keeping up with latest technology trends and strategies to turn data into actionable analytics Accuracy of Categorizing Emotions ▪ Irony and sarcasm, Multipolarity ▪ Word ambiguity Ethics ▪ Establishing and maintaining ethical practices (For example, using AI to track voice/text for identifying claims fraud and mental health condition) Sentiment Analysis: Challenges and Mitigation Mitigation Real-time Data Collection ▪ Exhaustive data collection ▪ Ensure member opinions are obtained from all possible sources Streamlined Data ▪ Collecting data with a specific purpose ▪ Increasing reusability of data Conducting Proxy Surveys ▪ Dependable alternative source for member data collection ▪ Ensures year-round supply of member sentiment data
  9. 9. 9 ▪ https://monkeylearn.com/sentiment-analysis/ ▪ https://www.altexsoft.com/blog/business/sentiment-analysis-types-tools-and-use-cases/ ▪ https://www.calabrio.com/wfo/customer-interaction-analytics/call-center-sentiment-analysis/ ▪ https://www.mckinsey.com/business-functions/risk/our-insights/ratings-revisited-textual- analysis-for-better-risk-management References
  10. 10. About CitiusTech 4,000+ Healthcare IT professionals worldwide 1,500+ Healthcare software engineering 400+ FHIR / HL7 certified professionals 25%+ CAGR over last 5 years 110+ Healthcare customers ▪ Healthcare technology companies ▪ Hospitals, IDNs & medical groups ▪ Payers and health plans ▪ ACO, MCO, HIE, HIX, NHIN and RHIO ▪ Pharma & Life Sciences companies 10 Thank You Authors: Nikita Agrawal Healthcare BA thoughtleaders@citiustech.com

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