Sara Montagna
Stefano Ferretti
Lorenz Cuno Klopfenstein
ABSTRACT
Chronic patient self-management is crucial for maintaining physical and psychological health, reducing pressure on healthcare systems, and promoting patient empowerment. Digital technologies, particularly chatbots, have emerged as powerful tools for supporting patients in managing their chronic conditions. Large language models (LLMs), such as GPT-4, have shown potential in improving chatbot-based systems in healthcare. However, their adoption in clinical practice faces challenges, including reliability, the need for clinical trials, and privacy concerns. This paper proposes a general architecture for developing an LLM-based chatbot system that supports chronic patients while addressing privacy and security concerns. The architecture is designed to be independent of specific technologies and health conditions, focusing on data protection legislation compliance. A prototype of the system has been developed for hypertension management, demonstrating its potential for motivating patients to monitor their blood pressure and adhere to prescriptions.
- Tourkiah Alessa, Mark S Hawley, Emma S Hock, and Luc de Witte. 2019. Smartphone Apps to Support Self-Management of Hypertension: Review and Content Analysis. JMIR Mhealth Uhealth 7, 5 (28 May 2019), e13645.Google Scholar
- John W Ayers, Adam Poliak, Mark Dredze, Eric C Leas, Zechariah Zhu, Jessica B Kelley, Dennis J Faix, Aaron M Goodman, Christopher A Longhurst, Michael Hogarth, and Davey M Smith. 2023. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum.JAMA Internal Medicine (2023).Google Scholar
- Gioele Bigini, Mirko Zichichi, Emanuele Lattanzi, Stefano Ferretti, and Gabriele D’Angelo. 2023. On the Decentralization of Health Systems for Data Availability: a DLT-based Architecture. In 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC). 372–377.Google ScholarCross Ref
- Marco Cascella, Jonathan Montomoli, Valentina Bellini, and Elena Bignami. 2023. Evaluating the Feasibility of ChatGPT in Healthcare: An Analysis of Multiple Clinical and Research Scenarios. Journal of Medical Systems 47, 1 (2023), 33.Google ScholarCross Ref
- Noreen M. Clark, Marshall H. Becker, Nancy K. Janz, Kate Lorig, William Rakowski, and Lynda Anderson. 1991. Self-Management of Chronic Disease by Older Adults: A Review and Questions for Research. Journal of Aging and Health 3, 1 (1991), 3–27.Google ScholarCross Ref
- GBD 2019 Risk Factors Collaborators. 2020. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet 396, 10258 (2020), 1223–1249.Google Scholar
- Sarah Dineen-Griffin, Victoria Garcia-Cardenas, Kylie Williams, and Shalom I. Benrimoj. 2019. Helping patients help themselves: A systematic review of self-management support strategies in primary health care practice. PLOS ONE 14, 8 (2019), 1–29.Google ScholarCross Ref
- Leyre Echeazarra, Juanan Pereira, and Ramón Saracho. 2021. TensioBot: a Chatbot Assistant for Self-Managed in-House Blood Pressure Checking. Journal of Medical Systems 45, 4 (2021), 54. https://doi.org/10.1007/s10916-021-01730-xGoogle ScholarDigital Library
- Editorial. 2023. Will ChatGPT transform healthcare?Nature Medicine 29, 3 (2023), 505–506.Google Scholar
- Gianfranco Parati et al.2021. Home blood pressure monitoring: methodology, clinical relevance and practical application: a 2021 position paper by the Working Group on Blood Pressure Monitoring and Cardiovascular Variability of the European Society of Hypertension. Journal of Hypertension 39, 9 (2021).Google Scholar
- Raju Vaishya et al.2023. ChatGPT: Is this version good for healthcare and research?Diabetes & Metabolic Syndrome: Clinical Research & Reviews 17, 4 (2023), 102744.Google Scholar
- European Parliament and Council of the European Union. 2016. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Official Journal of the European Union. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32016R0679Google Scholar
- Alexandra Heidel and Christian Hagist. 2020. Potential Benefits and Risks Resulting From the Introduction of Health Apps and Wearables Into the German Statutory Health Care System: Scoping Review. JMIR Mhealth Uhealth 8, 9 (23 Sep 2020), e16444. https://doi.org/10.2196/16444Google Scholar
- N. Hernandez, L. Castro, J. Medina-Quero, J. Favela, L. Michan, and W. Ben. Mortenson. 2021. Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring. Journal of Healthcare Informatics Research 5, 3 (2021), 270–299. https://doi.org/10.1007/s41666-020-00087-zGoogle ScholarCross Ref
- Richelle J. Koopman, Shannon M. Canfield, Jeffery L. Belden, Pete Wegier, Victoria A. Shaffer, K. D. Valentine, Akshay Jain, Linsey M. Steege, Sonal J. Patil, Mihail Popescu, and Michael L. LeFevre. 2020. Home blood pressure data visualization for the management of hypertension: designing for patient and physician information needs. BMC Medical Informatics and Decision Making 20, 1 (2020), 195. https://doi.org/10.1186/s12911-020-01194-yGoogle ScholarCross Ref
- Liliana Laranjo, Adam G Dunn, Huong Ly Tong, Ahmet Baki Kocaballi, Jessica Chen, Rabia Bashir, Didi Surian, Blanca Gallego, Farah Magrabi, Annie Y S Lau, and Enrico Coiera. 2018. Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association 25, 9 (07 2018), 1248–1258.Google ScholarCross Ref
- Niloofar Mohammadzadeh and Reza Safdari. 2014. Patient monitoring in mobile health: opportunities and challenges. Medical archives (Sarajevo, Bosnia and Herzegovina) 68, 1 (2014), 57–60. https://doi.org/10.5455/medarh.2014.68.57-60Google Scholar
- Sara Montagna, Stefano Mariani, and Martino Francesco Pengo. 2023. A Chatbot-based Recommendation Framework for Hypertensive Patients. In 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS). 730–733.Google ScholarCross Ref
- Act of Congress enacted by the 104th United States Congress. 1996. Health Insurance Portability and Accountability Act of 1996 (HIPAA). Public law 104. 191.Google Scholar
- Harm op den Akker, Miriam Cabrita, Rieks op den Akker, Valerie M. Jones, and Hermie J. Hermens. 2015. Tailored motivational message generation: A model and practical framework for real-time physical activity coaching. Journal of Biomedical Informatics 55 (2015), 104–115. https://doi.org/10.1016/j.jbi.2015.03.005Google ScholarDigital Library
- Xudong Pan, Mi Zhang, Shouling Ji, and Min Yang. 2020. Privacy Risks of General-Purpose Language Models. In 2020 IEEE Symposium on Security and Privacy (SP). 1314–1331.Google Scholar
- Jianing Qiu, Lin Li, Jiankai Sun, Jiachuan Peng, Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam, Frank P. W. Lo, Bo Xiao, Wu Yuan, Dong Xu, and Benny Lo. 2023. Large AI Models in Health Informatics: Applications, Challenges, and the Future. arxiv:2303.11568 [cs.AI]Google Scholar
- Monique Tabak, Harm op den Akker, and Hermie Hermens. 2014. Motivational cues as real-time feedback for changing daily activity behavior of patients with COPD. Patient Education and Counseling 94, 3 (2014), 372–378.Google ScholarCross Ref
- Lorainne Tudor Car, Dhakshenya Ardhithy Dhinagaran, Bhone Myint Kyaw, Tobias Kowatsch, Shafiq Joty, Yin-Leng Theng, and Rifat Atun. 2020. Conversational Agents in Health Care: Scoping Review and Conceptual Analysis. Journal of Medical Internet Research 22, 8 (2020), e17158.Google ScholarCross Ref
- Mirko Zichichi, Stefano Ferretti, Gabriele D’Angelo, and Víctor Rodríguez-Doncel. 2020. Personal Data Access Control Through Distributed Authorization. In 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). IEEE, 1–4.Google Scholar
- Mirko Zichichi, Stefano Ferretti, and Gabriele D’Angelo. 2020. On the Efficiency of Decentralized File Storage for Personal Information Management Systems. In 2020 IEEE Symposium on Computers and Communications (ISCC). 1–6.Google Scholar
- Mirko Zichichi, Stefano Ferretti, Gabriele D’Angelo, and Víctor Rodríguez-Doncel. 2022. Data governance through a multi-DLT architecture in view of the GDPR. Cluster Computing 25, 6 (2022), 4515 – 4542.Google ScholarDigital Library
Index Terms
- Data Decentralisation of LLM-Based Chatbot Systems in Chronic Disease Self-Management
Recommendations
Experiences in mHealth for chronic disease management in 4 countries
ISABEL '11: Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication TechnologiesThis paper describes mHealth applications to deal with Non Communicable Diseases in North and Latin America: In Chile, a project focused on Diabetes Mellitus type 2; In the United States, Honduras, and Mexico, projects focused in diabetes, heart failure,...
Localizing chronic disease management: information work and health translations
ASIST '13: Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information BoundariesBased on interviews with people who had diabetes, high blood pressure, and kidney disease in Flint, Michigan, we found people actively doing information work to manage their health in the face of poverty, potentially violent conditions, high stress, and ...
Adopting the sensemaking perspective for chronic disease self-management
Display Omitted Health self-monitoring technologies call for new theories of health behaviors.The sensemaking perspective is adopted to chronic disease self-management.We conduct meta-synthesis of qualitative studies of diabetes self-management.The meta-...
Comments