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Social Media Data: Opportunities and Insights for Clinical Research

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Social Media Data: Opportunities and Insights for Clinical Research

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Many new data are emerging in recent years - real time data is collected through digital health technologies, including apps and wearables, monitoring data, social media data, public datasets, and patient organization data, in addition to primary and secondary datasets.

Real life data are highly informative and can be used to address a range of challenges throughout the product life cycle. Data from social media can generate valuable insights as patients often gather in digital communities to get answers and share their experiences. Conversations on social networks merit special consideration as they can have real world influence over treatment management decisions.

Social media data can reveal the motivations that impact patient healthcare decisions and behaviors through each stage of the care pathway. These data provide both the patient and caregiver perspectives at the same time. For this reason, conversations on social networks offer an opportunity to deepen our understanding on:

- The fears and hopes associated with patient treatments
- Daily needs and difficulties patients are facing in managing their disease
- The impact of disease on patient health related quality of life
- Identification in real life of the stages of the care pathway and patient perceptions
- Reactions to health policies

Watch this webinar for insights on how to collect, use, analyze, and interpret social media data in different contexts. Our experts share knowledge from over fifteen years of successfully developing and adapting algorithms to treat this kind of data.

Many new data are emerging in recent years - real time data is collected through digital health technologies, including apps and wearables, monitoring data, social media data, public datasets, and patient organization data, in addition to primary and secondary datasets.

Real life data are highly informative and can be used to address a range of challenges throughout the product life cycle. Data from social media can generate valuable insights as patients often gather in digital communities to get answers and share their experiences. Conversations on social networks merit special consideration as they can have real world influence over treatment management decisions.

Social media data can reveal the motivations that impact patient healthcare decisions and behaviors through each stage of the care pathway. These data provide both the patient and caregiver perspectives at the same time. For this reason, conversations on social networks offer an opportunity to deepen our understanding on:

- The fears and hopes associated with patient treatments
- Daily needs and difficulties patients are facing in managing their disease
- The impact of disease on patient health related quality of life
- Identification in real life of the stages of the care pathway and patient perceptions
- Reactions to health policies

Watch this webinar for insights on how to collect, use, analyze, and interpret social media data in different contexts. Our experts share knowledge from over fifteen years of successfully developing and adapting algorithms to treat this kind of data.

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Social Media Data: Opportunities and Insights for Clinical Research

  1. 1. Copyright 2022. All Rights Reserved. Contact Presenter for Permission Social Media Data: Opportunities and Insights for Clinical Research Elodie de Bock, PhD Senior Principal Patient Centred Outcomes ICON plc elodie.debock@iconplc.com Valentin Barbier, MSc Research Associate Patient Centred Outcomes ICON plc valentin.barbier@iconplc.com Adel Mebarki, MBA General Manager & Co-Founder Health Technology Kap Code adel.mebarki@kapcode.fr Joelle Malaab, MSc, MPH International Project Manager Health Technology Kap Code joelle.malaab@kapcode.fr
  2. 2. Social media data: Opportunities and insights for clinical research
  3. 3. A bit of theory: Social media listening and natural language processing Application in real world setting: Merck case study Testimonies: Nearly 2,000 from 262 patients and 679 caregivers Results: Tables and graphs Some interesting findings: Testimonies provide real world data that can be used to improve the care process and patient quality of life Perspectives for industry: Strengths of collecting the voices of patients and caregivers throughout the full product life cycle 3 Today’s webinar agenda Q&A
  4. 4. 4 Thanks to… – Paul Loussikian, Pierre Foulquié, Simon Renner and Stephane Schuck (Analyses) – Alexia Marrel (Qualitative input) – Murtuza Bharmal (Sponsor)
  5. 5. 5 From Social Network Data to Patient Insights
  6. 6. 6 Real time data Primary & Secondary Databases A new world disrupted by data Data collected through digital health technologies, including apps and wearables. For example: Hospital Episode Statistics, Prescription data, etc. For example, Drug Safety Monitoring Data Data representing patients and service users’ views and experiences, captured online Public Health and Social Care authorities’ datasets Data collected by patient organization New types of data, among many others Surveillance & Monitoring Social Media Patient Organisation Data Public Datasets
  7. 7. 7 Conversations on social networks provide insights into: – The fears and hopes associated with their treatments – Their daily difficulties and needs in managing their disease – The impact of their disease on their health-related quality of life – The real-life stages of the care pathway and patient perceptions – Reactions to health policies Social networks cannot be ignored! They can influence treatment decisions and change management. Patients gather in digital communities to get answers and share their experiences Social network data: A powerful tool to understand patient perspective
  8. 8. 8 PRODUCT LIFECYCLE LIFECYCLE NO PHASE RESEARCH PRECLINICAL & PHASE I PHASE II PHASE III REGISTRATION PERI-POST APPROVAL Existing treatments’ experience perception: fears, hopes, challenges and unmet needs Disease experience perception: daily difficulties and needs in disease management Treatment experience Gather patient insights throughout the full product life cycle Patient insights, useful at each stage
  9. 9. 9 Illustrations - Tools Developed by Kap Code
  10. 10. 10 Kap Code is a startup, spin off from the CRO Kappa Santé, dedicated to the analysis of real-life data and more specifically data from social networks using artificial intelligence and NLP methods that translate patient language into medical ontologies DEDICATED TO THE HEALTH SECTOR SCIENTIFIC EXPERTISE FOR OVER 15 YEARS MULTIDISCIPLINARY TEAM WITH MEDICAL EXPERTISE 60 SCIENTIFIC WORKS PUBLISHED 10 Kap Code in a few words
  11. 11. 11 Algorithms developed internally Discussion topics Specialty Age, Sex Difficulties encountered Unmet needs Creation of personas Geolocation Discussion topics Care pathway Quality of life Difficulties encountered Unmet needs Creation of personas Age, Sex Geolocation Perception Care pathway Quality of life Difficulties encountered Diagnostic error Unmet needs Vaccine perception Disinformation Identification of suicidal thoughts Treatment intake Therapeutic switch Misuse Signal detection Discussion topics Quality of life Difficulties encountered Unmet needs Creation of personas Age, Sex Geolocation Addressable digital populations Patient HCPs Caregivers Pathologies Other Treatment
  12. 12. 12 How does it work? SOURCES Generic and specialized Extraction Dataset Pre-processing Association rules Topic models Clustering Topics of discussions Web users typologies Quality of Life impacts TERMINOLOGY Lexical fields (ex: healthcare pathway) Encountered difficulties MODELING ANALYSIS Medical Vocabulary (MedDRA) Social networks and open access forums, in compliance with the GDPR Patient journey
  13. 13. Detec’t – best in class AI for healthcare 13 I. II. III. Important populations Quick & Accurate Financial Optimization OBSERVE à Infodemiology studies to understand patients’ behaviour à Health Related Quality of Life data extraction à Medical unmet needs and pain points identification à Care pathway analysis à Patient & Caregivers recruitment via Social networks à Applied to Observational studies, Focus groups and Clinical trials ENGAGE à Pharmacovigilance early signal detection à Fake news early detection à Competition monitoring MONITOR & ALERT
  14. 14. 14 14 Detec’t is an automated social network analysis based on methods of artificial intelligence and text mining Detec’t methodology Yesterday I had an appointment at the hospital with my OBGYN et and it seems that my pregnancy get complicated, due to my medications ... #FML Yesterday I had an appointment at the hospital with my OBGYN et and it seems that my pregnancy get complicated, due to my medications ... #FML Pronoun Verb Event Medical centre Healthcare practicioner Pregnancy Therapeutics Problematic «Yesterday I had an appointment at the hospital with my OBGYN et and it seems that my pregnancy get complicated, due to my medications ... #FML » 1st category 2nd category 3rd category EXTRACTION ENTITY DETECTION STEMMING MESSAGE CLASSIFICATION
  15. 15. 15 Application in Real World Setting
  16. 16. 16 Locally Advanced or Metastatic Bladder Cancer Online Study in the United States
  17. 17. 17 Study objectives Main topics of discussion Challenges and Unmet Needs Treatment experience perception Phase 1 Phase 2 Phase 3 A social media study was conducted to improve Merck's knowledge of patients with locally advanced or metastasized bladder cancer and their caregivers based in the United States1,2 These social media data were collected in 3 phases as shown below: The study is based on the total volume of testimonies retrieved with their evolution over time. It also includes the typology of online users (age and gender when available), as well as the distinction between patients and caregivers. The final results presented are accompanied by examples of anonymized testimonies to support the findings 1 Renner S. et al. Perceived Unmet Needs in Patients Living With Advanced Bladder Cancer and Their Caregivers: Infodemiology Study Using Data From Social Media in the United States, JMIR Cancer 2022; 8(3) 2 Bharmal M. et al. Patient and Caregiver Perception of Treatments for Locally Advanced or Metastatic Bladder Cancer: Insights from Social Media in the US, ISPOR 2022, Washington, DC, USA
  18. 18. 18 WEB Focus on testimonies discussing advanced urothelial/bladder cancer Extraction Methodology A 3-step extraction and filtration strategy was used to obtain a corpus of reliable caregiver and patient testimonies. A qualitative analysis was then carried out to deepen and collect information on the challenges and unmet needs experienced by these caregivers and these patients 144 029 testimonies 68 079 users Removal of nonmedical sources 1 Identification of patients’ & caregivers’ experiences 2 3 Caregiver CORPUS 1,214 testimonies 679 caregivers Quick Reminder Qualitative analysis focusing on the challenges and unmet needs mentioned in caregiver testimonies (saturation has been checked) Patient CORPUS 688 testimonies 262 patients “My husband had stage 4 bladder cancer treated with removal of bladder and prostate and construction of neobladder.” User: person who mentioned bladder cancer online Patient: person presenting as a bladder cancer patient Caregiver: patient's relative involved or not in the care
  19. 19. 19 General descriptive data Evolution of testimonies with a patient or a caregiver experience regarding advanced BC over time 0 10 20 30 40 50 60 2 0 1 5 - 0 1 2 0 1 5 - 0 3 2 0 1 5 - 0 5 2 0 1 5 - 0 7 2 0 1 5 - 0 9 2 0 1 5 - 1 1 2 0 1 6 - 0 1 2 0 1 6 - 0 3 2 0 1 6 - 0 5 2 0 1 6 - 0 7 2 0 1 6 - 0 9 2 0 1 6 - 1 1 2 0 1 7 - 0 1 2 0 1 7 - 0 3 2 0 1 7 - 0 5 2 0 1 7 - 0 7 2 0 1 7 - 0 9 2 0 1 7 - 1 1 2 0 1 8 - 0 1 2 0 1 8 - 0 3 2 0 1 8 - 0 5 2 0 1 8 - 0 7 2 0 1 8 - 0 9 2 0 1 8 - 1 1 2 0 1 9 - 0 1 2 0 1 9 - 0 3 2 0 1 9 - 0 5 2 0 1 9 - 0 7 2 0 1 9 - 0 9 2 0 1 9 - 1 1 2 0 2 0 - 0 1 2 0 2 0 - 0 3 2 0 2 0 - 0 5 2 0 2 0 - 0 7 2 0 2 0 - 0 9 2 0 2 0 - 1 1 2 0 2 1 - 0 1 2 0 2 1 - 0 3 Number of testimonies per month From January 1, 2015 to March 4, 2021 1,902 testimonies 941 patients or caregivers 28% patients (N=262/941) 72% caregivers (N=679/941) 88 sources
  20. 20. 20 Phase 1: Main Topics of Discussion
  21. 21. 21 The main topics of discussion in patient testimonies Discussions around the diagnosis and the different treatment possibilities (traditional or alternative) 35.9 % Exchange of messages of hope/support and sharing of patient experiences 16.5 % Discussions around the healthcare pathway (patient management, method used for screening/diagnosis, healthcare team, etc.) 15.2 % Symptoms and clinical signs of bladder cancer 8.5 % Focus on patient quality of life 5.0 % Others 19.0 % 5 themes Ø Topics of discussion were identified using the Biterm Topic Modelling, an unsupervised machine learning method that identifies main topics in a data set and categorizes messages according to these topics Ø A manual interpretation of the topics then follows
  22. 22. 22 Sharing experiences and messages of hope and support 22.5 % Complications around bladder cancer 19.1 % Focus on diagnosis methods and medical acts 18.3 % Scientific information on drug treatments (clinical trials, scientific articles, etc.) 9.3 % Discussions around social coverage, insurance and the financial aspect around the care 5.3 % Accompanying the patient in the terminal phase and until death 7.6 % Others 17.9 % 6 themes The main topics of discussion in caregiver testimonies
  23. 23. 23 Phase 2: Challenges and Unmet Needs
  24. 24. Patient Focus
  25. 25. 25 Main challenges and unmet needs during the journey* B E F O R E B L A D D E R C A N C E R A F T E R T R E A T M E N T / M E D I C A L I N T E R V E N T I O N S – 5 7 . 9 % (n=55) • 29.5% Fear, occurrence, and management of treatment-related AEs and special situations • 7.4% General knowledge/information about a treatment • 5.3% Difficulty or delay in accessing treatment D I A G N O S I S & S C R E E N I N G – 1 7 . 9 % (number of challenges identified in patients’ testimonials =17) • 9.5% Misdiagnosis/Prognosis Error • 6.3% Screening/Diagnostic delay or lateness P A T H O L O G Y – 2 6 . 3 % (n=25) • 13.7% Progression/ Worsening/ Complication/ Recurrence of Disease • 9.5% Consideration and management of symptoms of BC • 2.1% Consideration and management of pain H A R D S H I P S E X I S T I N G A T A L L S T E P S O F T H E J O U R N E Y / T R A N S V E R S A L – 4 2 . 1 % ( n = 4 0 ) 21.1% Psychological impact: loneliness, depression, anxiety, fear, distress, personality change... 8.4% Need for sharing / Experiences / Support: discussion groups, social networks 4.2% Financial impact of the care: insurance, social coverage, cost... C A R E & F O L L O W - U P – 1 4 . 7 % (n=14) • 3.2% Burden of care: frequency of hospitalizations, numerous consultations, emergency room visits, etc. • 3.2% Disagreement in care: heterogeneity of medical decisions and opinions, disagreement between patient and medical team • 3.2% Problems of training or practice of the care team: lack of practice, lack of knowledge of the pathology R E M I S S I O N – 8 . 4 % (n=8) • 7.4% Sequelae of illness or care I M P A C T O N P A T I E N T E N V I R O N M E N T – 6 . 3 % • 2.1% Professional impact for the patient: part-time therapy, work interruptions, etc. • 2.1% Change in relationship: couple, family, friends • 2.1% Impact on daily activities (n=6) *340 testimonies analyzed of which 95 included challenges or unmet needs
  26. 26. 26 21.1% n=20 13.7% n=13 9.5% n=9 9.5% n=9 8.4% n=8 7.4% n=7 7.4% n=7 6.3% n=6 5.3% n=5 29.5% n=28 1 Fear, occurrence, and management of treatment-related AEs and special situations 2 Psychological impact Loneliness, depression, anxiety, fear, distress, personality change... 3 Progression/Worsening Complication/Recurrence of Disease 4 Misdiagnosis/Prognosis Error 5 Consideration and management of symptoms of BC 6 Need of Sharing / Experiences / Support discussion groups, social networks 7 General knowledge/information about a treatment 8 Sequelae of illness or care 9 Screening/Diagnostic delay or lateness 10 Difficulty or delay in accessing treatment Main unmet needs and challenges mentioned in patients’ testimonies* *340 testimonies analyzed of which 95 included challenges or unmet needs TOP 10
  27. 27. Caregiver Focus
  28. 28. 28 Main challenges and unmet needs during the journey* B E F O R E B L A D D E R C A N C E R A F T E R T R E A T M E N T / M E D I C A L I N T E R V E N T I O N S – 3 5 . 0 % (n=62) • 12.4% Fear, occurrence, and management of treatment-related AEs and special situations • 6.2% Wandering, therapeutic dead ends or ineffective treatments • 5.6% Difficulty or delay in accessing treatment D I A G N O S I S & S C R E E N I N G – 1 5 . 3 % (number of challenges identified in caregivers’ testimonials =27) • 7.3% Screening/Diagnostic delay or lateness • 4.0% Fear/Shock of the diagnosis disclosure • 3.4% Misdiagnosis/Prognosis Error P A T H O L O G Y – 2 3 . 2 % (n=41) • 6.2% Consideration and management of symptoms of BC • 5.1% Progression/ Worsening/ Complication/ Recurrence of Disease • 3.4% Altered general condition: fatigue, weight loss H A R D S H I P S E X I S T I N G A T A L L S T E P S O F T H E J O U R N E Y / T R A N S V E R S A L – 5 0 . 8 % ( n = 9 0 ) 26.0% Psychological impact: loneliness, depression, anxiety, fear, distress, personality change... 15.8% Need for sharing / Experiences / Support: discussion groups, social networks 4.5% Financial impact of the care: insurance, social coverage, cost... C A R E & F O L L O W - U P – 1 2 . 4 % (n=22) • 3.4% Communication problems: lack of empathy, lack of information transmission • 2.3% Disagreement in care: heterogeneity of medical decisions and opinions, disagreement between patient and medical team • 2.3% Difficulty in accessing care: distance from the place of care, medical desert, difficulty in making an appointment, insufficient number of HCPs, lack of availability of HCPs or specialists R E M I S S I O N – 4 . 0 % (n=7) • 3.4% Sequelae of illness or care E N D O F L I F E & D E A T H – 1 2 . 4 % (n=22) • 10.2% Burden of end-of-life support for the patient or loved ones & grief work • 2.3% End-of-life management and palliative care I M P A C T O N P A T I E N T E N V I R O N M E N T – 1 7 . 0 % • 9.6% Impact of being a caregiver: energy-consuming support for caregivers, time-consuming, moving... • 5.1% Change in relationship: couple, family, friends • 1.1% Impact on daily activities (n=30) *423 testimonies analyzed of which 177 included challenges or unmet needs
  29. 29. 29 15.8% n=28 12.4% n=22 10.2% n=18 9.6% n=17 7.3% n=13 6.2% n=11 6.2% n=11 5.6% n=10 5.1% n=9 26.0% n=46 1 Psychological impact Loneliness, depression, anxiety, fear, distress, personality change... 2 Need of Sharing / Experiences / Support discussion groups, social networks 3 Fear, occurrence, and management of treatment-related AEs and special situations 4 Burden of end-of-life support for the patient or loved ones & grief work 5 Impact of being a caregiver energy-consuming support for caregivers, time-consuming, moving... 6 Screening/Diagnostic delay or lateness 7 Wandering, therapeutic dead ends or ineffective treatments 8 Consideration and management of symptoms characteristic of BC 9 Difficulty or delay in accessing treatment 10 Change in relationship couple, family, friends Caregiver Patient Both Main unmet needs and challenges mentioned in caregivers’ testimonies whether they are patient-centered, caregiver-centered or both* *423 testimonies analyzed of which 177 included challenges or unmet needs TOP 10
  30. 30. 30 Caregiver Patient Both n=18 n=17 15.8% n=28 n=7 n=5 n=9 n=3 n=4 1.7% n=3 26.0% n=46 1 Psychological impact Loneliness, depression, anxiety, fear, distress, personality change... 2 Burden of end-of-life support for the patient & grief work 3 Impact of being a caregiver energy-consuming support for caregivers, time-consuming, moving... 4 Need of Sharing / Experiences / Support discussion groups, social networks 5 Fear/Shock of the diagnosis disclosure 6 Acceptance of the disease 7 Change in relationship couple, family, friends 8 General knowledge/scientific information about BC 9 End-of-life management and palliative care 10 Covid19 n=13 n=11 n=11 n=10 n=6 n=6 n=9 n=6 n=8 n=22 1 Fear, occurrence, and management of treatment-related AEs and specials situations 2 Screening/Diagnostic delay or lateness 3 Wandering, therapeutic dead ends or ineffective treatments 4 Consideration and management of symptoms characteristic of BC 5 Difficulty or delay in accessing treatment 6 Altered general condition fatigue, weight loss 7 Misdiagnosis/Prognosis Error 8 Progression/Worsening/ Complication/ Recurrence of Disease 9 Sequelae of illness or care 10 Financial impact of the care: insurance, social coverage, cost... RANK BASED ON CAREGIVER-CENTERED CHALLENGES RANK BASED ON PATIENT-CENTERED CHALLENGES 2.8% 6.2% 6.2% 10.2% 9.6% 4.0% 5.1% 3.4% 5.1% 5.6% 3.4% 3.4% 7.3% 12.4% 4.5% 1.7% 2.3% Main unmet needs and challenges mentioned in caregivers’ testimonies whether they are patient-centered, caregiver-centered or both* *423 testimonies analyzed of which 177 included challenges or unmet needs
  31. 31. 31 Phase 3: Treatment Experience Perception
  32. 32. 32 General data and overview of the collected perceptions of treatment A qualitative analysis was carried out specifically among the testimonies of patients and caregivers on relevant experiences concerning treatments (n=299), whether chemotherapy OR immunotherapy è Filtering of testimonies using key words related to the treatment area concerned Overall treatment intake 80% 20% All testimonies (n=299) Treatment was not taken Treatment was taken Patient testimonies Overall perception of treatment 88% 12% Caregiver testimonies Treatment was taken Treatment was not taken Patient testimonies Caregiver testimonies All testimonies (n=122) (n=177) (n=299) (n=177) (n=122) Treatment was taken Treatment was not taken 45% 33% 5% 17% No perception expressed Negative Mixed Positive 37% 43% 7% 13% No perception expressed Negative Mixed Positive 57% 18% 2% 23% No perception expressed Negative Mixed Positive CHEMOTHERAPY : 222 testimonies (80 from patients / 142 from caregivers) IMMUNOTHERAPY : 77 testimonies (42 from patients / 35 from caregivers) 75% 25%
  33. 33. Chemotherapy Focus
  34. 34. 34 52% 36% 5% 7% 71% 23% 6% Overall data on perceptions of chemotherapy Overall perception of treatment Caregiver posts Patient posts All testimonies (n=222) (n= 80) (n= 142) INSIGHTS “Hello. I’ve been recently been dx’d with muscle invasive bladder cancer which involves the urethra and a lymph node. Was scheduled for surgery on August 18 first, but then canceled so that I get chemo treatments first. Had the first one last week and going for the 2nd one this week.” Patient “I knew what to expect and how miserable chemo was gonna be... I suggest doing research to prepare yourself. […] It will be okay chemo is so bad, minus the sickness and all. Family will get you threw this too! I believe mine just came back after 3 years and am pretty nervous for my sons sake. Its never fun hearing the doctor say "time for another round“.” Overall treatment intake 84% 16% All posts (n=222) Treatment was not taken by patients Treatment was taken by patients Patient posts 87% 13% Caregiver posts Treatment was taken Treatment was not taken (n=80) (n=142) Treatment was taken by patients Treatment was not taken by patients No perception expressed Negative Mixed Positive No perception expressed Negative Positive Negative No perception expressed Mixed Positive q Chemotherapy treatments are perceived more negatively (36%) than positively (7%) overall, both in patient and caregiver testimonies q Main perceived benefits of chemotherapy: effectiveness, extension of life span, and few adverse events q Main perceived drawbacks of chemotherapy: adverse events/pain, lack of effectiveness, and access criteria Caregiver CHEMOTHERAPY : 222 testimonies (80 from patients / 142 from caregivers) 73% 27% 41% 44% 8% 7%
  35. 35. Immunotherapy focus
  36. 36. 36 26% 22% 5% 47% 29% 9% 7% 55% Overall data on perceptions of immunotherapy Overall perception of treatment Caregiver testimonies Patient testimonies All testimonies (n=77) (n= 42) (n= 35) INSIGHTS “If so I just want you to know that Opdivo an immunotherapy drug caused my metastatic lymph nodes to disappear in 2 weeks. […] and the life saving Opdivo is keeping the cancer that would kill me sooner at bay.” Patient “My husband was diagnosed with stage 4 bladder cancer (both small cell and transitional cell carcenoma). He is currently taking immunotherapy which has kept the other cancer at microscopic size which has had numerous side effects like loss of taste buds and loss of the adrenal and pituitary glands.” Overall treatment intake 78% 22% All testimonies (n=77) Treatment was not taken Treatment was taken Patient testimonies 88% 12% Caregiver testimonies Treatment was taken Treatment was not taken (n=42) (n=35) 80% 20% Treatment was taken Treatment was not taken No perception expressed Negative Mixed Positive No perception expressed Negative Positive Negative No perception expressed Mixed Positive Mixed q Immunology treatments are perceived positively in almost half of the testimonies (47%) q The perception of immunology treatments is more negative in caregivers' testimonies (37%) than in patients' testimonies (10%) q Main perceived benefits of immunotherapy: effectiveness, few adverse events, targeted therapy q Main perceived drawbacks of immunotherapy: lack of effectiveness, AE/pain, and long-term sequelae Caregiver IMMUNOTHERAPY : 77 testimonies (42 from patients / 35 from caregivers) 23% 37% 3% 37%
  37. 37. 37 Conclusion and Key Insights
  38. 38. 38 Take away messages Numerous patients and their caregivers share their voices about bladder cancer and its advanced forms on social media in the USA. They share their concerns, challenges but also their perception of treatments. These testimonies provide real-world data that can be used to improve the care process and quality of life Importance of the psychological aspect, even for caregivers Importance of information about adverse events and their management Importance of support services for patients and their caregivers
  39. 39. 39 Summary of Key Findings & Perspective
  40. 40. 40 – Social network data: real-world data that provide very rich panel information to analyse patient and caregiver perspectives about disease and treatment – This informs, clarifies and highlights the unmet needs in disease management and in existing treatments – Gather patient insights throughout the full product life cycle using social network data can be a powerful strategy – This can be used to: – Improve the care process and the quality of life of patients – Enhance adherence to treatment – Tweak the perception of a specific treatment – Etc. To remember
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  42. 42. ICONplc.com © 2022 ICON. All rights reserved. Any questions? Email our presentation author(s) directly! kapcode.fr Thank you!
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