Revue de littérature sur l’usage de l’intelligence artificielle dans les dossiers de santé numériques personnels pour soutenir le développement de compétences d’autogestion chez les patients atteints de maladies complexes
Barre latérale de l'article
Contenu principal de l'article
Résumé
La qualité de vie des personnes atteintes de maladies complexes est influencée par leur implication dans la gestion de leur maladie et par leur niveau de compétence en autogestion. Ces personnes peuvent vivre des difficultés pour identifier les ressources éducatives libres d’accès et autres ressources libres utiles pour développer ces compétences. Nous avons mené une revue de littérature narrative dans le but d’identifier les technologies d’intelligence artificielle déjà utilisées dans les dossiers de santé numériques personnels et les enjeux auxquels ces technologies apportent des solutions. Nous avons cherché dans trois bases de données : PubMed, ScienceDirect et Scopus. Les mots clés ont été sélectionnés pour inclure l’intelligence artificielle et les dossiers de santé numériques personnels. Dans les 17 articles repérés, 4 grandes familles de technologies se sont démarquées : l’apprentissage automatique, les systèmes à base de règles, les ontologies et le traitement automatique du langage naturel. Dans la plupart des articles, diverses technologies sont combinées et contribuent à l’apprentissage de l’autogestion de manière complémentaire. Cette revue montre la possibilité d’enrichir la conception des dossiers de santé numériques personnels avec l’intelligence artificielle, et elle s’inscrit dans le cadre du développement de l’environnement numérique SPÉCIAL visant à assurer plus de soutien dans le parcours d’apprentissage de l’autogestion par le patient.
Téléchargements
Renseignements sur l'article
Cette œuvre est sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Partage dans les Mêmes Conditions 4.0 International.
Références
Alvarado, R. (2023). AI as an Epistemic Technology. Science and Engineering Ethics, 29(5), 32. https://doi.org/10.1007/s11948-023-00451-3 DOI: https://doi.org/10.1007/s11948-023-00451-3
Ammar, N., Olusanya, O. A., Melton, C., Chinthala, L., Huang, X., White, B. M., et Shaban-Nejad, A. (2023). Digital personal health coaching platform for promoting human papillomavirus infection vaccinations and cancer prevention: Knowledge graph-based recommendation system. JMIR Formative Research, 7. https://doi.org/10.2196/50210 DOI: https://doi.org/10.2196/50210
Babinet, O., et Isnard-Bagnis, C. (2020). La e-santé en question(s). Hygée éditions.
Briganti, G. (2023). Intelligence artificielle : une introduction pour les cliniciens. Revue des Maladies Respiratoires, 40(4), 308‑313. https://doi.org/10.1016/j.rmr.2023.02.005 DOI: https://doi.org/10.1016/j.rmr.2023.02.005
Carswell, C., Brown, J. V. E., Lister, J., Ajjan, R. A., Alderson, S. L., Balogun-Katung, A., Bellass, S., Double, K., Gilbody, S., Hewitt, C. E., Holt, R. I. G., Jacobs, R., Kellar, I., Peckham, E., Shiers, D., Taylor, J., Siddiqi, N., Coventry, P., on behalf of the DIAMONDS Research team. (2022). The lived experience of severe mental illness and long-term conditions: A qualitative exploration of service user, carer, and healthcare professional perspectives on self-managing co-existing mental and physical conditions. BMC Psychiatry, 22(1), 479. https://doi.org/10.1186/s12888-022-04117-5 DOI: https://doi.org/10.1186/s12888-022-04117-5
Carvalho, D. V., Pereira, E. M., et Cardoso, J. S. (2019). Machine learning interpretability: A survey on methods and metrics. Electronics, 8(8), 832. https://doi.org/10.3390/electronics8080832 DOI: https://doi.org/10.3390/electronics8080832
Chaney, M. A. (2021). So you want to write a narrative review article? Journal of Cardiothoracic and Vascular Anesthesia, 35(10), 3045-3049. https://doi.org/10.1053%2Fj.jvca.2021.06.017 DOI: https://doi.org/10.1053/j.jvca.2021.06.017
Chung, K., et Park, R. C. (2019). Chatbot-based heathcare service with a knowledge base for cloud computing. Cluster Computing, 22, 1925‑1937. https://doi.org/10.1007/s10586-018-2334-5 DOI: https://doi.org/10.1007/s10586-018-2334-5
Cooper, R., Witham, M. D., Bartle, V., et Sayer, A. A. (2022). Defining and measuring multiple long-term conditions in research. BMJ Medicine, 1(1), e000287. https://doi.org/10.1136/bmjmed-2022-000287 DOI: https://doi.org/10.1136/bmjmed-2022-000287
Dumez, V., et L’Espérance, A. (2024). Beyond experiential knowledge: A classification of patient knowledge. Social Theory & Health, 22(3), 173-186. https://doi.org/10.1057/s41285-024-00208-3 DOI: https://doi.org/10.1057/s41285-024-00208-3
Easton, K., Potter, S., Bec, R., Bennion, M., Christensen, H., Grindell, C., Mirheidari, B., Weich, S., Witte, L. de, Wolstenholme, D., et Hawley, M. S. (2019). A virtual agent to support individuals living with physical and mental comorbidities: Co-design and acceptability testing. Journal of Medical Internet Research, 21(5), e12996. https://doi.org/10.2196/12996 DOI: https://doi.org/10.2196/12996
Evans, P., Vansteenkiste, M., Parker, P., Kingsford-Smith, A., et Zhou, S. (2024). Cognitive load theory and its relationships with motivation: A self-determination theory perspective. Educational Psychology Review, 36(1), 7. https://doi.org/10.1007/s10648-023-09841-2 DOI: https://doi.org/10.1007/s10648-023-09841-2
Ge, C., Yin, C., Liu, Z., Fang, L., Zhu, J., et Ling, H. (2020). A privacy preserve big data analysis system for wearable wireless sensor network. Computers & Security, 96, 101887. https://doi.org/10.1016/j.cose.2020.101887 DOI: https://doi.org/10.1016/j.cose.2020.101887
Goblet, X., et Rey, C. (2020). Suivi thérapeutique intelligent par recommandation à base d’ontologie et de règles. APIA (Conférence Nationale sur les Applications Pratiques de l’Intelligence Artificielle), Angers, France
Gonzalez-Zacarias, A. A., Mavarez-Martinez, A., Arias-Morales, C. E., Stoicea, N., et Rogers, B. (2016). Impact of demographic, socioeconomic, and psychological factors on glycemic self-management in adults with type 2 diabetes mellitus. Frontiers in Public Health, 4. https://doi.org/10.3389/fpubh.2016.00195 DOI: https://doi.org/10.3389/fpubh.2016.00195
Hellqvist, C. (2021). Promoting self-care in nursing encounters with persons affected by long-term conditions—A proposed model to guide clinical care. International Journal of Environmental Research and Public Health, 18(5), 1‑17. https://doi.org/10.3390/ijerph18052223 DOI: https://doi.org/10.3390/ijerph18052223
Ho, J. T., See, M. T. A., Tan, A. J. Q., Levett-Jones, T., Lau, T. C., Zhou, W., et Liaw, S. Y. (2023). Healthcare professionals’ experiences of interprofessional collaboration in patient education: A systematic review. Patient education and counseling, 116, 107965. https://doi.org/10.1016/j.pec.2023.107965 DOI: https://doi.org/10.1016/j.pec.2023.107965
Humeau, T., Savard, I., Lemire, D., Dionne, P.-O., Angulo-Mendoza, G.-A., Plante, P., Pinard. A.-M. et Roy, J.-S. (2022). FORCES 3 : exploitation à des fins pédagogiques des données d’un portail d’apprentissage de l’autogestion de la douleur. Développement d’une architecture de collecte et d’analyse de données et d’un module de suivi du développement des compétences. Médiations et médiatisations, (12), 74-97. https://doi.org/10.52358/mm.vi12.287 DOI: https://doi.org/10.52358/mm.vi12.287
ISO. (2014). Health informatics—Capacity-based eHealth architecture roadmap—Part 2: Architectural components and maturity model (ISO/TR 14639-2 14639‑2). https://www.iso.org/standard/54903.html
Jain, D. K., Srinivas, K., Srinivasu, S. V. N., et Manikandan, R. (2021). Machine learning-based monitoring system with IoT using wearable sensors and pre-convoluted fast recurrent neural networks (P-FRNN). IEEE Sensors Journal, 21(22), 25517‑25524. https://doi.org/10.1109/JSEN.2021.3091626 DOI: https://doi.org/10.1109/JSEN.2021.3091626
Jain, S. R., Sui, Y., Ng, C. H., Chen, Z. X., Goh, L. H., et Shorey, S. (2020). Patients’ and healthcare professionals’ perspectives towards technology-assisted diabetes self-management education. A qualitative systematic review. PLOS ONE, 15(8), e0237647. https://doi.org/10.1371/journal.pone.0237647 DOI: https://doi.org/10.1371/journal.pone.0237647
Kocsis, O., Arvanitis, G., Lalos, A., Moustakas, K., Sont, J. K., Honkoop, P. J., Chung, K. F., Bonini, M., Usmani, O. S., Fowler, S., et Simpson, A. (2017). Assessing machine learning algorithms for self-management of asthma. Dans (Proceedings of the) 2017 E-health and bioengineering conference (EHB) (p. 571-574). https://doi.org/10.1109/EHB.2017.7995488 DOI: https://doi.org/10.1109/EHB.2017.7995488
Krishnakumar, A., Verma, R., Chawla, R., Sosale, A., Saboo, B., Joshi, S., Shaikh, M., Shah, A., Kolwankar, S., et Mattoo, V. (2021). Evaluating glycemic control in patients of south asian origin with type 2 diabetes using a digital therapeutic platform: Analysis of real-world data. Journal of Medical Internet Research, 23(3), e17908. https://doi.org/10.2196/17908 DOI: https://doi.org/10.2196/17908
La fabriqueREL (2025). La fabriqueREL. https://fabriquerel.org
Laplante, J. (2021). Agentivité. Anthropen. https://doi.org/10.47854/NJFW6857 DOI: https://doi.org/10.47854/NJFW6857
Li, X., Zheng, H., Chen, J., Zong, Y., et Yu, L. (2024). User interaction interface design and innovation based on artificial intelligence technology. Journal of Theory and Practice of Engineering Science, 4(03), 1-8. https://doi.org/10.53469/jtpes.2024.04(03).01 DOI: https://doi.org/10.53469/jtpes.2024.04(03).01
Longwitz, A., et Palokas, M. (2023). Diabetes self-management education for adults with type 2 diabetes via telehealth in conjunction with remote patient monitoring: A best practice implementation project. JBI Evidence Implementation, 21(2), 156‑166. https://doi.org/10.1097/XEB.0000000000000360 DOI: https://doi.org/10.1097/XEB.0000000000000360
Lorenzi, L., Mattivi, F., Malacarne, J., et Melgani, F. (2015). E-health system adopting a priority index. 184‑188. https://hdl.handle.net/11572/115434
Lucyk, K., et McLaren, L. (2017). Commentaire - La « santé publique et des populations » au Canada va-t-elle à l’avenir s’unir ou se diviser? Réflexions de l’intérieur. Promotion de la santé et prévention des maladies chroniques au Canada, 37(7), 244‑249. https://doi.org/10.24095/hpcdp.37.7.03f DOI: https://doi.org/10.24095/hpcdp.37.7.03f
Luštrek, M., Bohanec, M., Barca, C. C., Ciancarelli, M. C., Clays, E., Dawodu, A. A., Derboven, J., Smedt, D. D., Dovgan, E., Lampe, J., Marino, F., Mlakar, M., Pioggia, G., Puddu, P. E., Rodríguez, J. M., Schiariti, M., Slapničar, G., Slegers, K., Tartarisco, G.,… Vodopija, A. (2021). A personal health system for self-management of congestive heart failure (HeartMan): Development, technical evaluation, and proof-of-concept randomized controlled trial. JMIR Medical Informatics, 9(3), e24501. https://doi.org/10.2196/24501 DOI: https://doi.org/10.2196/24501
Merlière, Y. (2022). Les enjeux de l’information médicale du patient : Du Dossier Médical Personnel (DMP) à Mon Espace Santé (MES). Journal de droit de la santé et de l’assurance maladie, 34. https://hal.science/hal-03974849/
Mitchell, E. G., Desai, P., Smaldone, A., Cassells, A., Tobin, J. N., Albers, D., Levine, M., et Mamykina, L. (2025). T2 Coach: A qualitative study of an automated health coach for diabetes self-management. Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, 1‑17. https://doi.org/10.1145/3706598.3714404 DOI: https://doi.org/10.1145/3706598.3714404
Norman, D. A. (2023). Design for a better world: Meaningful, sustainable, humanity centered. MIT Press. https://books.google.com/books?hl=fr&lr=&id=5Wx2EAAAQBAJ&oi=fnd&pg=PA1&dq=Design+for+a+Better+World:+Meaningful,+Sustainable,+Humanity-Centered&ots=3fGuK2l-xa&sig=VmkRDIJzmp448iZrvEnnGpwfxAk
Osborn, S., et Choo, K.-K. R. (2024). A blockchain patient-centric records framework for older adult healthcare. International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, 18-36. https://doi.org/10.1007/978-3-031-50051-0_2 DOI: https://doi.org/10.1007/978-3-031-50051-0_2
Peleg, M., Shahar, Y., Quaglini, S., Fux, A., García-Sáez, G., Goldstein, A., Hernando, M. E., Klimov, D., Martínez-Sarriegui, I., Napolitano, C., Parimbelli, E., Rigla, M., Sacchi, L., Shalom, E., et Soffer, P. (2017). MobiGuide: A personalized and patient-centric decision-support system and its evaluation in the atrial fibrillation and gestational diabetes domains. User Modeling and User-Adapted Interaction, 27(2), 159‑213. https://doi.org/10.1007/s11257-017-9190-5 DOI: https://doi.org/10.1007/s11257-017-9190-5
Pelletier, C. (2024). Against Personalised Learning. International Journal of Artificial Intelligence in Education, 34(1), 111‑115. https://doi.org/10.1007/s40593-023-00348-z DOI: https://doi.org/10.1007/s40593-023-00348-z
Rabbi, M., Aung, M. S., Gay, G., Reid, M. C., et Choudhury, T. (2018). Feasibility and acceptability of mobile phone–based auto-personalized physical activity recommendations for chronic pain self-management: Pilot study on adults. Journal of Medical Internet Research, 20(10), e10147. https://doi.org/10.2196/10147 DOI: https://doi.org/10.2196/10147
Rahayu, N. W., Ferdiana, R., et Kusumawardani, S. S. (2022). A systematic review of ontology use in E-Learning recommender system. Computers and Education: Artificial Intelligence, 3, 100047. https://doi.org/10.1016/j.caeai.2022.100047 DOI: https://doi.org/10.1016/j.caeai.2022.100047
Rajapaksha, S., Abhayarathne, W. J. A., Kumari, S. G. K., De Silva, M. V. L. U., et Wijesuriya, W. M. S. M. (2019). A Mobile application to predict and manage high blood pressure and personalized recommendations. International Conference on Advancements in Computing (ICAC,) 422-426. https://doi.org/10.1109/ICAC49085.2019.9103337 DOI: https://doi.org/10.1109/ICAC49085.2019.9103337
Rim, R., Amin, M. M., et Mahfoudhi, A. (2023). Bayesian learning for user modeling. Research in Computing Science, 152(12), 153-165. https://www.rcs.cic.ipn.mx/2023_152_12/Bayesian%20Learning%20for%20User%20Modeling.pdf
Roehrs, A., da Costa, C. A., Righi, R. da R., Rigo, S. J., et Wichman, M. H. (2019). Toward a model for personal health record interoperability. IEEE Journal of Biomedical and Health Informatics, 23(2), 867‑873. https://doi.org/10.1109/JBHI.2018.2836138 DOI: https://doi.org/10.1109/JBHI.2018.2836138
Savard, I., Léveillée, A-K., Dellazizzo, L., Boulet-Lavoie, N., Latulippe, K., Mendoza, G. A. A., Plante, P., Roy, J.-S., et Lemire, D. (2026). Orchestrer les ressources libres d’accès validées et les technologies d’intelligence artificielle au sein d’un environnement numérique SPÉCIAL : pour un engagement accru des apprenants et une collaboration interprofessionnelle optimisée [soumis pour publication].
Shaik, T., Tao, X., Li, Y., Dann, C., McDonald, J., Redmond, P., et Galligan, L. (2022). A review of the trends and challenges in adopting natural language processing methods for education feedback analysis. IEEE Access, 10, 56720‑56739. https://doi.org/10.1109/ACCESS.2022.3177752 DOI: https://doi.org/10.1109/ACCESS.2022.3177752
Tang, P. C., Ash, J. S., Bates, D. W., Overhage, J. M., et Sands, D. Z. (2006). Personal Health Records: Definitions, Benefits, and Strategies for Overcoming Barriers to Sdoption. Journal of the American Medical Informatics Association : JAMIA, 13(2), 121–126. https://doi.org/10.1197/jamia.M2025 DOI: https://doi.org/10.1197/jamia.M2025
Todi, K., Bailly, G., Leiva, L., et Oulasvirta, A. (2021). Adapting user interfaces with model-based reinforcement learning. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1‑13. https://doi.org/10.1145/3411764.3445497 DOI: https://doi.org/10.1145/3411764.3445497
Tsang, K. C. H., Pinnock, H., Wilson, A. M., et Ahmar Shah, S. (2020). Application of machine learning to support self-management of asthma with mHealth. Dans (Proceedings of the) 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montréal, QC, Canada, 2020-July, 5673‑5677. https://doi.org/10.1109/EMBC44109.2020.9175679 DOI: https://doi.org/10.1109/EMBC44109.2020.9175679
van Buchem, M. M., de Hond, A. A. H., Fanconi, C., Shah, V., Schuessler, M., Kant, I. M. J., Steyerberg, E. W., et Hernandez-Boussard, T. (2024). Applying natural language processing to patient messages to identify depression concerns in cancer patients. Journal of the American Medical Informatics Association: JAMIA, 31(10), 2255‑2262. https://doi.org/10.1093/jamia/ocae188 DOI: https://doi.org/10.1093/jamia/ocae188
Vijay, L., et Kumar, T. (2024). AI-driven advancements: Optimising chronic disease care through personal health record management. 1083 LNNS, 355‑364. https://doi.org/10.1007/978-3-031-67431-0_34 DOI: https://doi.org/10.1007/978-3-031-67431-0_34
Virvou, M. (2022). The emerging era of human-AI interaction: Keynote address. Dans (Proceedings of the) 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA), Corfu, Greece, 2022 (p. 1 10). IEEE. https://doi.org/10.1109/IISA56318.2022.9904422 DOI: https://doi.org/10.1109/IISA56318.2022.9904422
Wang, B., et Giabbanelli, P. J. (2024). Identifying informative features to evaluate student knowledge as causal maps. International Journal of Artificial Intelligence in Education, 34(2), 301‑331. https://doi.org/10.1007/s40593-023-00329-2 DOI: https://doi.org/10.1007/s40593-023-00329-2
Yang, T.-T., Zheng, H.-X., Cao, S., Jing, M.-L., Hu, J., Zuo, Y., Chen, Q.-Y., et Zhang, J.-J. (2025). Harnessing an artificial intelligence–based large language model with personal health record capability for personalized information support in postsurgery myocardial infarction: Descriptive qualitative study. Journal of Medical Internet Research, 27. https://doi.org/10.2196/68762 DOI: https://doi.org/10.2196/68762