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SYSTEMS-LEVEL QUALITY IMPROVEMENT
Predictive, Personalized, Preventive and Participatory (4P) Medicine
Applied to Telemedicine and eHealth in the Literature
Susel Góngora Alonso
1
&Isabel de la Torre Díez
1
&Begoña García Zapiraín
2
Received: 28 January 2019 / Accepted: 5 April 2019 /Published online: 12 April 2019
#Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract
The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and
participatory medicinein 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 (BPredictive^OR BPersonalized^OR BPreventive^OR BParticipatory^) AND
BMedicine^AND (BeHealth^OR BTelemedicine^) 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
identifiedas 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.
Keywords eHealth .Predictive .Personalized .Preventive .Participatory .Teleme dicine
Introduction
P4 medicine describes a focus on systems that include predic-
tive, personalized, preventive and participative aspects [1]. It
proposes the integration of numerous points of biological data,
which include longitudinal molecular, cellular and phenotypical
measurements, as well as individual genome sequences, in or-
der to better define the health or wellbeing of every person,
predict transitions to illness and orient medical interventions
[2,3]. The implementation of P4 medicine from a clinical point
of view will create predictive and personalized models that
represent the wellbeing of every patient or of a disease, which
enables the design of new pharmacological tests that take into
account the heterogeneity of responses to therapies and the
stratification of the illness [4,5].
Computer technology has been applied to various domains
in order to obtain a greater yield. From the point of view of
preventive medicine, portable devices are useful for monitor-
ing and warning. Building a software system that facilitates
monitoring and warning is one promising solution for promot-
ingpreventivemedicine[6,7].
Thefutureofhealthservicesiscenteredonofferingpeople
a complete image of the many factors that affect their health.
Real-time analysis enables doctors, researchers and other in-
terested parties to take the most informed decisions, at the
same tame as offering patients greater control over their own
medical attention [8,9]. Cognitive computing constitutes a
new evolution of algorithms and systems with language-
This article is part of the Topical Collection on Systems-Level Quality
Improvement
*Isabel de la Torre Díez
isator@tel.uva.es
Susel Góngora Alonso
suselgongoraalonso@gmail.com
Begoña García Zapiraín
mbgarciazapi@deusto.es
1
Department of Signal Theory and Communications, and Telematics
Engineering, University of Valladolid, Paseo de Belén, 15,
47011 Valladolid, Spain
2
University of Deusto, Avenida de las Universidades 24,
48007 Bilbao, Spain
Journal of Medical Systems (2019) 43: 140
https://doi.org/10.1007/s10916-019-1279-4
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