Continuous, Person-Centered Digital Precision Health and Wellness: A Structured Narrative Review Informing 10P-Health
Main Article Content
Keywords
longitudinal health data, artificial intelligence, interoperability, data governance, remote monitoring
Abstract
Background: Continuous, person-centered digital precision health and wellness aims to complement episodic, illness-triggered care with longitudinal multimodal data, analytical support, and accountable responses. 10P-Health (10PH) is the authors’ integrated formulation, organized around ten interdependent principles.
Objective: To examine literature and initiatives relevant to 10PH, identify capabilities, implementation conditions, limitations, and gaps in evidence on connections across principles and measurement-to-response pathways.
Methods: A non-exhaustive structured narrative review used PubMed, IEEE Xplore, Google Scholar, citation searching, and targeted official sources. English-language material from 2013 to 2026 was considered, with earlier seminal work retained for conceptual lineage. Heterogeneous conceptual, empirical, technical, policy, program, and product sources were organized by conceptual development, enabling data and technology, initiatives and products, implementation conditions, and evidence maturity. Source-reported findings were distinguished from author interpretation.
Results: The literature describes population-scale genomic and phenotypic resources, longitudinal multimodal programs, remote-monitoring workflows, digital-twin applications, interoperability approaches, and person- and organization-facing artificial intelligence products. It also identifies governance, privacy, safety, professional workflow, participation, equity, access, and economic evaluation as implementation requirements. The sources generally addressed individual capabilities or bounded workflows; evidence on how these connect across settings into end-to-end measurement-to-response pathways remains fragmented. No reviewed source evaluated 10PH as a complete integrated intervention.
Conclusion: Existing evidence identifies capabilities and implementation conditions relevant to 10PH but does not establish the integrated framework's feasibility, effectiveness, safety, equity, or cost-effectiveness. Future research should prospectively and independently evaluate defined use cases to test how data, validated interpretation, pertinent responses, accountable workflows, and meaningful person participation connect.
References
All of Us Research Program Genomics Investigators. (2024). Genomic data in the All of Us Research Program. Nature, 627(8003), 340–346. https://doi.org/10.1038/s41586-023-06957-x
Anthropic. (2026, January 11). Advancing Claude in healthcare and the life sciences. Retrieved July 31, 2026, from https://www.anthropic.com/news/healthcare-life-sciences
Armeni, P., Polat, I., De Rossi, L. M., Diaferia, L., Meregalli, S., & Gatti, A. (2022). Digital twins in healthcare: Is it the beginning of a new era of evidence-based medicine? A critical review. Journal of Personalized Medicine, 12(8), Article 1255. https://doi.org/10.3390/jpm12081255
Bertelsen, N., Dewulf, L., Ferrè, S., Vermeulen, R., Schroeder, K., Gatellier, L., Sargeant, I., Luzuriaga, D., Chapman, H., & Brooke, N. (2024). Patient engagement and patient experience data in regulatory review and health technology assessment: A global landscape review. Therapeutic Innovation & Regulatory Science, 58(1), 63–78. https://doi.org/10.1007/s43441-023-00573-7
Borges do Nascimento, I. J., Abdulazeem, H., Vasanthan, L. T., Martinez, E. Z., Zucoloto, M. L., Østengaard, L., Azzopardi-Muscat, N., Zapata, T., & Novillo-Ortiz, D. (2023). Barriers and facilitators to utilizing digital health technologies by healthcare professionals. npj Digital Medicine, 6, Article 161. https://doi.org/10.1038/s41746-023-00899-4
Bragazzi, N. L. (2013). From P0 to P6 medicine, a model of highly participatory, narrative, interactive, and “augmented” medicine: Some considerations on Salvatore Iaconesi's clinical story. Patient Preference and Adherence, 7, 353–359. https://doi.org/10.2147/PPA.S38578
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O'Connell, J., Cortes, A., Welsh, S., Young, A., Effingham, M., McVean, G., Leslie, S., Allen, N., Donnelly, P., & Marchini, J. (2018). The UK Biobank resource with deep phenotyping and genomic data. Nature, 562(7726), 203–209. https://doi.org/10.1038/s41586-018-0579-z
Chen, T., Hertog, E., Mahdi, A., & Vanderslott, S. (2025). A systematic review on patient and public attitudes toward health monitoring technologies across countries. npj Digital Medicine, 8, Article 433. https://doi.org/10.1038/s41746-025-01762-4
Flores, M., Glusman, G., Brogaard, K., Price, N. D., & Hood, L. (2013). P4 medicine: How systems medicine will transform the healthcare sector and society. Personalized Medicine, 10(6), 565–576. https://doi.org/10.2217/pme.13.57
Gavan, S. P., Thompson, A. J., & Payne, K. (2018). The economic case for precision medicine. Expert Review of Precision Medicine and Drug Development, 3(1), 1–9. https://doi.org/10.1080/23808993.2018.1421858
Gentili, A., Failla, G., Melnyk, A., Puleo, V., Di Tanna, G. L., Ricciardi, W., & Cascini, F. (2022). The cost-effectiveness of digital health interventions: A systematic review of the literature. Frontiers in Public Health, 10, Article 787135. https://doi.org/10.3389/fpubh.2022.787135
Gorini, A., & Pravettoni, G. (2011). P5 medicine: A plus for a personalized approach to oncology. Nature Reviews Clinical Oncology, 8(7), 444. https://doi.org/10.1038/nrclinonc.2010.227-c1
Health Tech World. (2025, January 16). Interview: The future of healthcare and inclusive AI. https://htworld.co.uk/news/ai/interview-the-future-of-healthcare-and-inclusive-ai/
Heryawan, L., Mori, Y., Yamamoto, G., Kume, N., Lazuardi, L., Fuad, A., & Kuroda, T. (2025). Fast Healthcare Interoperability Resources (FHIR)-based interoperability design in Indonesia: Content analysis of developer hub's social networking service. JMIR Formative Research, 9, Article e51270. https://doi.org/10.2196/51270
Hood, L. (2013). Systems biology and P4 medicine: Past, present, and future. Rambam Maimonides Medical Journal, 4(2), Article e0012. https://doi.org/10.5041/RMMJ.10112
Hood, L., & Friend, S. H. (2011). Predictive, personalized, preventive, participatory (P4) cancer medicine. Nature Reviews Clinical Oncology, 8(3), 184–187. https://doi.org/10.1038/nrclinonc.2010.227
Hood, L. E., & Galas, D. J. (2008). P4 medicine: Personalized, predictive, preventive, participatory-A change of view that changes everything [White paper]. Computing Community Consortium. https://cra.org/ccc/wp-content/uploads/sites/2/2015/05/P4_Medicine.pdf
Hou, Y.-C. C., Yu, H.-C., Martin, R., Cirulli, E. T., Schenker-Ahmed, N. M., Hicks, M., Cohen, I. V., Jönsson, T. J., Heister, R., Napier, L., Swisher, C. L., Dominguez, S., Tang, H., Li, W., Perkins, B. A., Barea, J., Rybak, C., Smith, E., Duchicela, K., … Caskey, C. T. (2020). Precision medicine integrating whole-genome sequencing, comprehensive metabolomics, and advanced imaging. Proceedings of the National Academy of Sciences of the United States of America, 117(6), 3053–3062. https://doi.org/10.1073/pnas.1909378117
Kaplan, D. M., Greenleaf, M., & Lam, W. A. (2023). Wear with care: A call for empirical investigations of adverse outcomes of consumer health wearables. Mayo Clinic Proceedings: Digital Health, 1(3), 413–418. https://doi.org/10.1016/j.mcpdig.2023.06.014
Kurki, M. I., Karjalainen, J., Palta, P., Sipilä, T. P., Kristiansson, K., Donner, K. M., Reeve, M. P., Laivuori, H., Aavikko, M., Kaunisto, M. A., Loukola, A., Lahtela, E., Mattsson, H., Laiho, P., Della Briotta Parolo, P., Lehisto, A. A., Kanai, M., Mars, N., Rämö, J., … Palotie, A. (2023). FinnGen provides genetic insights from a well-phenotyped isolated population. Nature, 613(7944), 508–518. https://doi.org/10.1038/s41586-022-05473-8
Lehne, M., Sass, J., Essenwanger, A., Schepers, J., & Thun, S. (2019). Why digital medicine depends on interoperability. npj Digital Medicine, 2, Article 79. https://doi.org/10.1038/s41746-019-0158-1
Love-Koh, J., Peel, A., Rejon-Parrilla, J. C., Ennis, K., Lovett, R., Manca, A., Chalkidou, A., Wood, H., & Taylor, M. (2018). The future of precision medicine: Potential impacts for health technology assessment. PharmacoEconomics, 36(12), 1439–1451. https://doi.org/10.1007/s40273-018-0686-6
O'Mahony, J. F., Newall, A. T., & van Rosmalen, J. (2015). Dealing with time in health economic evaluation: Methodological issues and recommendations for practice. PharmacoEconomics, 33(12), 1255–1268. https://doi.org/10.1007/s40273-015-0309-4
OpenAI. (2026a, January 8). Introducing OpenAI for healthcare. Retrieved July 31, 2026, from https://openai.com/index/openai-for-healthcare/
OpenAI. (2026b, July 23). Launching Health in ChatGPT. Retrieved July 31, 2026, from https://openai.com/index/health-in-chatgpt/
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D.,
Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J.,
Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Systematic Reviews, 10(1), 1–11.
https://doi.org/10.1186/S13643-021-01626-4/FIGURES/1
Price, N. D., Magis, A. T., Earls, J. C., Glusman, G., Levy, R., Lausted, C., McDonald, D. T., Kusebauch, U., Moss, C. L., Zhou, Y., Qin, S., Moritz, R. L., Brogaard, K., Omenn, G. S., Lovejoy, J. C., & Hood, L. (2017). A wellness study of 108 individuals using personal, dense, dynamic data clouds. Nature Biotechnology, 35(8), 747–756. https://doi.org/10.1038/nbt.3870
Rao, N. D., Fullerton, S. M., Shirts, B. H., Chen, A. T., & Henrikson, N. B. (2024). Applying health equity implementation science frameworks to population genetic screening. Frontiers in Health Services, 4, Article 1455365. https://doi.org/10.3389/frhs.2024.1455365
Riahi, V., Diouf, I., Khanna, S., Boyle, J., & Hassanzadeh, H. (2025). Digital twins for clinical and operational decision-making: Scoping review. Journal of Medical Internet Research, 27, Article e55015. https://doi.org/10.2196/55015
Rocque, G. B., Franks, J. A., Deng, L., Caston, N. E., Williams, C. P., Azuero, A., Dent, D., Jackson, B. E., McGowan, C., Henderson, N. L., Huang, C.-H. S., Ingram, S., Odom, J. N., Eltoum, N., Weiner, B., Howell, D., Stover, A. M., Pierce, J. Y., & Basch, E. M. (2025). Remote symptom monitoring with electronic patient-reported outcomes in clinical cancer populations. JAMA Network Open, 8(5), Article e259852. https://doi.org/10.1001/jamanetworkopen.2025.9852
Schüssler-Fiorenza Rose, S. M., Contrepois, K., Moneghetti, K. J., Zhou, W., Mishra, T., Mataraso, S., Dagan-Rosenfeld, O., Ganz, A. B., Dunn, J., Hornburg, D., Rego, S., Perelman, D., Ahadi, S., Sailani, M. R., Zhou, Y., Leopold, S. R., Chen, J., Ashland, M., Christle, J. W., … Snyder, M. P. (2019). A longitudinal big data approach for precision health. Nature Medicine, 25(5), 792–804. https://doi.org/10.1038/s41591-019-0414-6
Sriram, R. D., & Subrahmanian, E. (2020). Transforming health care through digital revolutions. Journal of the Indian Institute of Science, 100(4), 753–772. https://doi.org/10.1007/s41745-020-00195-0
Tong, L., Shi, W., Isgut, M., Zhong, Y., Lais, P., Gloster, L., Sun, J., Swain, A., Giuste, F., & Wang, M. D. (2024). Integrating multi-omics data with EHR for precision medicine using advanced artificial intelligence. IEEE Reviews in Biomedical Engineering, 17, 80–97. https://doi.org/10.1109/RBME.2023.3324264
Torab-Miandoab, A., Samad-Soltani, T., Jodati, A., & Rezaei-Hachesu, P. (2023). Interoperability of heterogeneous health information systems: A systematic literature review. BMC Medical Informatics and Decision Making, 23, Article 18. https://doi.org/10.1186/s12911-023-02115-5
World Health Organization. (2021). Ethics and governance of artificial intelligence for health. https://www.who.int/publications/i/item/9789240029200
