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Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

  • Systems-Level Quality Improvement
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Abstract

The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and participatory medicine in telemedicine and ehealth. The academic databases used for searches are IEEE Xplore, PubMed, Science Direct, Web of Science and ResearchGate, taking into account publication dates from 2010 up to the present day. These databases cover the greatest amount of information on scientific texts in multidisciplinary fields, from engineering to medicine. Various search criteria were established, such as (“Predictive” OR “Personalized” OR “Preventive” OR “Participatory”) AND “Medicine” AND (“eHealth” OR “Telemedicine”) selecting the articles of most interest. A total of 184 publications about predictive, personalized, preventive and participatory (4P) medicine in telemedicine and ehealth were found, of which 48 were identified as relevant. Many of the publications found show how the P4 medicine is being developed in the world and the benefits it provides for patients with different illnesses. After the revision that was undertaken, it can be said that P4 medicine is a vital factor for the improvement of medical services. It is hoped that one of the main contributions of this study is to provide an insight into how P4 medicine in telemedicine and ehealth is being applied, as well as proposing outlines for the future that contribute to the improvement of prevention and prediction of illnesses.

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References

  1. Jenkins, S. L., and Maayan, A., Systems pharmacology meets predictive, preventive, personalized and participatory medicine. Pharmacogenomics 14(2):119–122, 2013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Khoury, M. J., Gwinn, M., Glasgow, R. E., and Kramer, B. S. A., Population perspective on how personalized medicine can improve health. Am. J. Prev. Med. 42(6):639–645, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Maier, M., Takano, T., and Sapir-Pichhadze, R., Changing paradigms in the management of rejection in kidney transplantation: Evolving from protocol-based care to the era of P4 medicine. Can. J. Kidney Health Dis. 4, 2017.

    Article  Google Scholar 

  4. Pravettoni, G., and Gorini, A., A P5 cancer medicine approach: Why personalized medicine cannot ignore psychology. J. Eval. Clin. Pract. 17(4):594–596, 2011.

    Article  PubMed  Google Scholar 

  5. Hood, L., and Friend, S. H., Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat. Rev. Clin. Oncol. 8(3):184–187, 2011.

    Article  PubMed  Google Scholar 

  6. Khemapech, I., Sansrimahachai, W., and Toahchoodee, M., A real-time Health Monitoring and warning system for bridge structures. Proceeding of IEEE Reg 10 Annu Int Conf/TENCON. 3010–3033, 2016.

  7. Sabra, S., Alobaidi, M., Malik, K. M., and Sabeeh, V., Performance evaluation for semantic-based risk factors extraction from clinical narratives. Proceeding of IEEE 8th Annu Comput Commun Work Conf CCWC. 695–701, 2018.

  8. Green, S., and Vogt, H., Personalizing medicine: Disease prevention in silico and in socio. HumanaMente J. Philos. Stud 90(30):105–145, 2016.

    Google Scholar 

  9. Pulciani, S., Di Lonardo, A., Fagnani, C., and Taruscio, D., P4 medicine versus Hippocrates. Ann. Ist. Super. Sanita 53(3):185–191, 2017.

    PubMed  Google Scholar 

  10. Ahmed, M. N., Toor, A. S., O’Neil, K., and Friedland, D., Cognitive computing and the future of health care cognitive computing and the future of healthcare: The cognitive power of IBM Watson has the potential to transform global personalized medicine. IEEE Pulse 8(3):4–9, 2017.

    Article  PubMed  Google Scholar 

  11. Vogt, H., Hofmann, B., and Getz, L., The new holism: P4 systems medicine and the medicalization of health and life itself. Med. Health Care Philos. 19(2):307–323, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Talukder, A. K., Chaitanya, M., Arnold, D., and Sakurai, K., Proof of disease: A blockchain consensus protocol for accurate medical decisions and reducing the disease burden. Proceeding of IEEE SmartWorld, Ubiquitous Intell Comput Adv Trust Comput Scalable Comput Commun Cloud Big Data Comput Internet People Smart City Innov SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI. 257–262, 2018.

  13. Zhang, Y., Cheng, Y., Jia, K., and Zhang, A., Opportunities for computational techniques for multi-omics integrated personalized medicine. Tsinghua Sci. Technol. 19(6):545–558, 2014.

    Article  CAS  Google Scholar 

  14. Hood, L., and Flores, M., A personal view on systems medicine and the emergence of proactive P4 medicine: Predictive, preventive, personalized and participatory. New Biotechnol. 29(6):613–624, 2012.

    Article  CAS  Google Scholar 

  15. Sobradillo, P., Pozo, F., and Agustí, Á., P4 medicine: The future around the corner. Arch. Bronconeumol. 47(1):35–40, 2011.

    Article  PubMed  Google Scholar 

  16. Noell, G., Faner, R., and Agustí, A., From systems biology to p4 medicine: Applications in respiratory medicine. Eur. Respir. Rev. 27(147):1–15, 2018.

    Article  Google Scholar 

  17. Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P. A. et al., The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ. 339, 2009.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Poon, C. C. Y., Wang, M. D., Bonato, P., and Fenstermacher, D. A., Special issue on health informatics and personalized medicine. Proceeding of IEEE Trans Biomed Eng. 60(1):143–146, 2013.

    Article  Google Scholar 

  19. Hood, L., and Tian, Q., Systems approaches to biology and disease enable translational systems medicine. Genomics Proteomics Bioinformatics 10(4):181–185, 2012.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bengoechea, J. A., Infection systems biology: From reactive to proactive (P4) medicine. Int. Microbiol. 15(2):55–60, 2012.

    PubMed  Google Scholar 

  21. Persch, A. C., Braveman, B. H., and Metzler, C. A., P4 medicine and pediatric occupational therapy. Am. J. Occup. Ther. 67(4):383–388, 2013.

    Article  PubMed  Google Scholar 

  22. Shapira, N., Women’s higher health risks in the obesogenic environment: A gender nutrition approach to metabolic dimorphism with predictive, preventive, and personalized medicine. EPMA J. 4(1):1, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wegener, D., Rossi, S., Buffa, F., Delorenzi, M., and Rüping, S., Towards an environment for data mining based analysis processes in bioinformatics and personalized medicine. Netw Model Anal Heal Informatics Bioinforma 2(1):29–44, 2013.

    Article  Google Scholar 

  24. Miralles, F., Gomez-Cabrero, D., Lluch-Ariet, M., Tegnér, J., Cascante, M., and Roca, J., Predictive medicine: Outcomes, challenges and opportunities in the synergy-COPD project. J. Transl. Med. 12(2):S12, 2014.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bresó, A., Sáez, C., Vicente, J., Larrinaga, F., Robles, M., and García-Gómez, J. M., Knowledge-based personal health system to empower outpatients of diabetes mellitus by means of P4 medicine. Human. In: Data Mining in Clinical Medicine. 237–257, 2015.

    Google Scholar 

  26. Guzzi, P. H., Agapito, G., Milano, M., and Cannataro, M., Methodologies and experimental platforms for generating and analysing microarray and mass spectrometry-based omics data to support P4 medicine. Brief. Bioinform. 17(4):553–561, 2016.

    Article  PubMed  Google Scholar 

  27. Kodrič, K., Čamernik, K., Černe, D., Komadina, R., and Marc, J., P4 medicine and osteoporosis: A systematic review. Wien. Klin. Wochenschr. 128(7):480–491, 2016.

    Article  PubMed  CAS  Google Scholar 

  28. Vogt, H., Hofmann, B., and Getz, L., Personalized medicine: Evidence of normativity in its quantitative definition of health. Theor. Med. Bioeth. 37(5):401–416, 2016.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Moța, M., Prevention of diabetes and 4P medicine. Rom J Diabetes Nutr Metab Dis. 24(1):7–12, 2017.

    Google Scholar 

  30. Sagner, M., McNeil, A., Puska, P., Auffray, C., Price, N. D., Hood, L. et al., The P4 health Spectrum – A predictive, preventive, personalized and participatory continuum for promoting Healthspan. Prog. Cardiovasc. Dis. 59(5):506–521, 2017.

    Article  PubMed  Google Scholar 

  31. Trovato, G. M., Behavior, nutrition and lifestyle in a comprehensive health and disease paradigm: Skills and knowledge for a predictive, preventive and personalized medicine. EPMA J. 3(1):8, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Younesi, E., and Hofmann-Apitius, M., From integrative disease modeling to predictive, preventive, personalized and participatory (P4) medicine. EPMA J. 4(1):23, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Simon, R., Clinical trials for predictive medicine: New challenges and paradigms. Clin. Trials 7(5):516–524, 2010.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wang, F.-Y., and Wong, P. K., Intelligent systems and Technology for Integrative and Predictive Medicine: An ACP approach. ACM Trans. Intell. Syst. Technol. 4(2):1–6, 2013.

    Google Scholar 

  35. Pham, T., Tran, T., Phung, D., and Venkatesh, S., Predicting healthcare trajectories from medical records: A deep learning approach. J. Biomed. Inform. 69:218–229, 2017.

    Article  PubMed  Google Scholar 

  36. Tuck, M. G., Alemi, F., Shortle, J. F., Avramovic, S., and Hesdorffer, C., A comprehensive index for predicting risk of Anemia from patients’ diagnoses. Big Data 5(1):42–52, 2017.

    Article  PubMed  Google Scholar 

  37. Swan, M., Health 2050: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory biocitizen. J. Pers. Med. 2(3):93–118, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Filipova-Neumann, L., and Hoy, M., Managing genetic tests, surveillance, and preventive medicine under a public health insurance system. J. Health Econ. 34(1):31–41, 2014.

    Article  PubMed  Google Scholar 

  39. Finch, L., Youl, P., Marshall, A. L., Soyer, H. P., Baade, P., and Janda, M., User preferences for text message-delivered skin cancer prevention and early detection. J. Telemed. Telecare 21(4):227–234, 2015.

    Article  PubMed  Google Scholar 

  40. Nohara, Y., Kai, E., Ghosh, P. P., Islam, R., Ahmed, A., Kuroda, M. et al., Health checkup and telemedical intervention program for preventive medicine in developing countries: Verification study. J. Med. Internet Res. 17(1):e2, 2015.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hengartner, M. P., Kawohl, W., Haker, H., Rössler, W., and Ajdacic-Gross, V., Big five personality traits may inform public health policy and preventive medicine: Evidence from a cross-sectional and a prospective longitudinal epidemiologic study in a Swiss community. J. Psychosom. Res. 84:44–51, 2016.

    Article  PubMed  Google Scholar 

  42. Bernabé, B. P., Cralle, L., and Gilbert, J. A., Systems biology of the human microbiome. Curr. Opin. Biotechnol. 51:146–153, 2018.

    Article  CAS  Google Scholar 

  43. O’Donnell, J. C., Personalized medicine and the role of health economics and outcomes research: Issues, applications, emerging trends, and future research. Value Health 16(6):S1–S3, 2013.

    Article  PubMed  Google Scholar 

  44. Evers, A. W., Rovers, M. M., Kremer, J. A., Veltman, J. A., Schalken, J. A., Bloem, B. R. et al., An integrated framework of personalized medicine: From individual genomes to participatory health care. Croat Med J. 53(4):301–303, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Ullman-Cullere, M. H., and Mathew, J. P., Emerging landscape of genomics in the electronic health record for personalized medicine. Hum. Mutat. 32(5):512–516, 2011.

    Article  PubMed  Google Scholar 

  46. Carlsten, C., Brauer, M., Brinkman, F., Brook, J., Daley, D., McNagny, K. et al., Genes, the environment and personalized medicine: We need to harness both environmental and genetic data to maximize personal and population health. EMBO Rep. 15(7):736–739, 2014.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gwinn, M., and Khoury, M. J., Genomic, Personalized Medicine and Public Health. Genomic and Personalized Medicine. Elsevier Inc., 216–226, 2013.

  48. Kouris, I., Tsirmpas, C., Mougiakakou, S. G., Iliopoulou, D., and Koutsouris, D., E-health towards ecumenical framework for personalized medicine via Decision Support System. Proceeding of2010 Annu Int Conf IEEE Eng Med Biol Soc EMBC’10. 2881–2885, 2010.

  49. Weiss, S. T., Implementing personalized medicine in the academic health center. J Pers Med. 6(18), 2016.

    Article  PubMed Central  Google Scholar 

  50. Ginsburg, G. S., and Kuderer, N. M., Comparative effectiveness research, genomics-enabled personalized medicine, and rapid learning health care: A common bond. J. Clin. Oncol. 30(34):4233, 2012.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Vieta, E., Personalized medicine applied to mental health: Precision psychiatry. Rev. Psiquiatr. Salud. Ment. 8(3):117–118, 2015.

    Article  PubMed  Google Scholar 

  52. Boland, M. R., Hripcsak, G., Shen, Y., Chung, W. K., and Weng, C., Defining a comprehensive verotype using electronic health records for personalized medicine. J. Am. Med. Inform. Assoc. 20(e2):232–238, 2013.

    Article  Google Scholar 

  53. McClellan, K. A., Avard, D., Simard, J., and Knoppers, B. M., Personalized medicine and access to health care: Potential for inequitable access? Eur. J. Hum. Genet. 21(2):143–147, 2013.

    Article  PubMed  Google Scholar 

  54. Antoñanzas, F., Juárez-Castelló, C. A., and Rodríguez-Ibeas, R., Some economics on personalized and predictive medicine. Eur. J. Health Econ. 16(9):985–994, 2015.

    Article  PubMed  Google Scholar 

  55. Anaya, J. M., Duarte-Rey, C., Sarmiento-Monroy, J. C., Bardey, D., Castiblanco, J., and Rojas-Villarraga, A., Personalized medicine. Closing the gap between knowledge and clinical practice. Autoimmun. Rev. 15(8):833–842, 2016.

    Article  PubMed  Google Scholar 

  56. Estape, E. S., Mays, M. H., and Sternke, E. A., Translation in data mining to advance personalized medicine for health equity. Intell. Inf. Manag. 8(1):9–16, 2016.

    PubMed  PubMed Central  Google Scholar 

  57. Agapito, G., Guzzi, P., and Cannataro, M., A parallel software pipeline for DMET microarray genotyping data analysis. High-Throughput. 7(2):17, 2018.

    Article  PubMed Central  CAS  Google Scholar 

  58. Berrouiguet, S., Perez-Rodriguez, M. M., Larsen, M., Baca-García, E., Courtet, P., and Oquendo, M., From eHealth to iHealth: Transition to participatory and personalized medicine in mental health. J. Med. Internet Res. 20(1):e2, 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Sagner, M., McNeil, A., and Arena, R., The Next Chapter: The Future of Health Care and Lifestyle Interventions. Lifestyle Medicine: Lifestyle, the Environment and Preventive Medicine in Health and Disease. Elsevier, 437–446. 2017

  60. Hood, L., and Auffray, C., Participatory medicine: A driving force for revolutionizing healthcare. Genome Med. 5(12):110, 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Vasilateanu, A., Radu, I. C., and Buga, A., Environment crowd-sensing for asthma management. Proceeding of E-Health Bioeng Conf EHB. 1–4. 2015

  62. Morley, J. E., and Anker, S. D., Myopenia and precision (P4) medicine. J. Cachexia. Sarcopenia Muscle 8(6):857–863, 2017.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Nikolaiev, S., and Timoshenko, Y., Reinvention of the cardiovascular diseases prevention and prediction due to ubiquitous convergence of mobile apps and machine learning. Proceeding of 2015 Inf Technol Innov Bus Conf ITIB. 23–26, 2015.

  64. Biesecker, L. G., Hypothesis-generating research and predictive medicine. Genome Res. 23(7):1051–1053, 2013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Gupta, P. D., Pharmacogenetics, pharmacogenomics and ayurgenomics for personalized medicine: A paradigm shift. Indian J. Pharm. Sci. 77(2):135–141, 2015.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Vilhelmsson, A., Value-based health care delivery, preventive medicine and the medicalization of public health. Cureus 9(3):10–13, 2017.

    Google Scholar 

  67. Hood, L., Systems biology and P4 medicine: Past, present, and future. Rambam Maimonides Med J. 4(2), 2013.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Yan, Q. Systems and dynamical medicine: The roles of translational bioinformatics. In: Translational Bioinformatics and Systems Biology Methods for Personalized Medicine. 13–24, 2017.

    Chapter  Google Scholar 

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Acknowledgements

This research has been partially supported by European Commission and the Ministry of Industry, Energy and Tourism under the project AAL-20125036 named “Wetake Care: ICT- based Solution for (Self-) Management of Daily Living”.

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Authors and Affiliations

  1. Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain

    Susel Góngora Alonso & Isabel de la Torre Díez

  2. University of Deusto, Avenida de las Universidades 24, 48007, Bilbao, Spain

    Begoña García Zapiraín

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  1. Susel Góngora Alonso
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Correspondence to Isabel de la Torre Díez.

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Alonso, S.G., de la Torre Díez, I. & Zapiraín, B.G. Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature. J Med Syst 43, 140 (2019). https://doi.org/10.1007/s10916-019-1279-4

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