Korean Journal of Clinical Pharmacy (한국임상약학회지)

Korean College Of Clinical Pharmacy (한국임상약학회)
권호 표지
DOI QR코드

DOI QR Code

Development of Warfarin Talk: A Messenger Chatbot for Patients Taking Warfarin

와파린 복용 환자를 위한 메신저 기반 챗봇 개발

  • Lee, Han Sol (College of Pharmacy & Research Institute of Pharmaceutical Sciences, Seoul National University) ;
  • Kim, Yu Ri (Department of Pharmacy, Seoul National University Hospital) ;
  • Shin, Eun Jeong (Department of Pharmacy, Seoul National University Hospital) ;
  • Jang, Hong Won (Department of Pharmacy, Seoul National University Hospital) ;
  • Jo, Yun Hee (Department of Pharmacy, Seoul National University Hospital) ;
  • Cho, Yoon Sook (Department of Pharmacy, Seoul National University Hospital) ;
  • Kim, Jung Hoon (Binarylab) ;
  • Lee, Ju-Yeun (College of Pharmacy & Research Institute of Pharmaceutical Sciences, Seoul National University)
  • Received : 2020.11.16
  • Accepted : 2020.12.09
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Background: Despite the increased use of direct-acting oral anticoagulants, warfarin is still recommended as first-line therapy in patients with mechanical valves or moderate to severe mitral stenosis. Anticoagulation management services (AMSs) are warranted for patients receiving warfarin therapy due to the complexity of warfarin dosing and large interpatient variability. To overcome limited health care resources, we developed a messenger app-based chatbot that provides information to patients taking warfarin. Methods: We developed "WafarinTalk" as an add-on to the open-source messenger app KakaoTalk. We developed the prototype chatbot after building a database containing seven categories: 1) dosage and indications, 2) drug-drug interactions, 3) drug-food interactions, 4) drug-diet supplement interactions, 5) monitoring, 6) adverse events, and 7) precautions. We then surveyed 30 pharmacists and 10 patients on chatbot reliability and on participant satisfaction. Results: We found that 80% of the pharmacists agreed on the consistency of chatbot responses and 44% agreed on the appropriateness of chatbot. Furthermore, 47% of pharmacists said that they were willing to recommend the chatbot to patients. Of the seven categories, information on drug-food interaction was the most useful; 90% of patients said they were satisfied with the chatbot and 100% of patients said they were willing to use it when they were unable to see a pharmacist. We updated the prototype chatbot with feedback from the survey. Conclusion: This study showed that warfarin-related information could be provided to patients through a messenger application-based chatbot.

Keywords

References

  1. Nishimura RA, Otto CM, Bonow RO, et al. 2017 AHA/ACC focused update of the 2014 AHA/ACC guideline for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines. J Am Coll Cardiol 2017;70(2):252-89. https://doi.org/10.1016/j.jacc.2017.03.011
  2. Cha MJ. Stroke prevention in atrial fibrillation. Int J Arrhythmia 2017;18(3):137-42. https://doi.org/10.18501/arrhythmia.2017.022
  3. Jung BC. Antithrombotic therapy for patients with nonvalvular atrial fibrillation. Korean J Med 2016;90(3):189-97. https://doi.org/10.3904/kjm.2016.90.3.189
  4. DiPiro JT, Talbert RL, Yee GC, Matzke GR, Wells BG, Posey LM. Pharmacotherapy: A Pathophysiologic Approach, 10e. New York: McGraw-Hill Education, 2017; 255-8.
  5. Ansell J, Hirsh J, Hylek E, Jacobson A, Crowther M, Palareti G. Pharmacology and management of the vitamin K antagonists: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th Edition). Chest 2008;133(6 Suppl):160S98S.
  6. Holbrook AM, Pereira JA, Labiris R, et al. Systematic overview of warfarin and its drug and food interactions. Arch Intern Med 2005;165(10):1095-106. https://doi.org/10.1001/archinte.165.10.1095
  7. Witt DM, Nieuwlaat R, Clark NP, et al. American Society of Hematology 2018 guidelines for management of venous thromboembolism: optimal management of anticoagulation therapy. Blood Adv 2018;2(22):3257-91. https://doi.org/10.1182/bloodadvances.2018024893
  8. Kim JT, Jeong SR, Jeong H. Trend of messenger-based chatbot technology and military application plan. Defense and Technology 2017;459:118-27.
  9. Brooks R. e-Health Part 1: current state of play. Australian Nursing Journal 2012;20(2):20.
  10. Kim DH, Kim SS. Recent research trends on personal health records from the viewpoint of intelligent healthcare platform activation. Information and Communication Magazine, 2018;35(2):10-7.
  11. Vaira L, Bochicchio M. A., Conte M, Casaluci FM, Melpignano A. MamaBot: a system based on ML and NLP for supporting women and families during pregnancy. In: Proceedings of the 22nd International Database Engineering & Applications Symposium; Villa san Giovanni, Italy: Association for Computing Machinerry, 2018:273-7.
  12. Brixey J, Hoegen R, Lan W, et al. SHIHbot: A Facebook chatbot for Sexual Health Information on HIV/AIDS. Proceedings of the 18th annual SIGdial meeting on discourse and dialogue 2017;370-3.
  13. Fitzpatrick KK, Darcy A, Vierhile M. Delivering cognitive behavior therapy to young adults with symptoms of depression and anxiety using a fully automated conversational agent (Woebot): A Randomized Controlled Trial. JMIR Ment Health 2017;4(2):e19. https://doi.org/10.2196/mental.7785
  14. Cameron G, Cameron D, Megaw G, et al. Towards a chatbot for digital counselling. In: Proceedings of the 31st International BCS Human Computer Interaction Conference (HCI 2017), Sunderland, United Kingdom, July 3-6, 2017.
  15. Comendador BEV, Francisco BMB, Medenilla JS, Nacion SMT, Serac TBE. Pharmabot: A pediatric generic medicine consultant chatbot. Journal of Automation and Control Engineering. 2015;3(2):137-40. https://doi.org/10.12720/joace.3.2.137-140
  16. Lee A, Kim HC, Cha MC, Ji YG. A study on the client experience using chatbot based on counseling theory. Journal of the Ergonomics Society of Korea 2019;38(3):161-75. https://doi.org/10.5143/JESK.2019.38.3.161
  17. Lexi-Drugs. Lexicomp, Wolters Kluwer Health, Inc. Riverwoods, IL. Available from: https://online.lexi.com/lco/action/home. Accessed Aug 21, 2019.
  18. Micromedex solutions. IBM Watson Health, Greenwood Village, CO. Available from https://www.micromedexsolutions.com. Accessed Aug 21, 2019.
  19. Morville P. Ambient findability: What we find changes who we become, 1st ed. California: O'Reilly Media Inc, 2005: 108-10.
  20. Jeong SG. The effect of anthropomorphism and personalization of fashion shopping chatbot on service acceptance intention. M.S. Thesis for Textile, Merchandising, and Fashion Design Seoul National University. 2019.