personalised healthcare
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2021 ◽  
Author(s):  
Pei-hua Huang ◽  
Ki-hun Kim ◽  
Maartje Schermer

BACKGROUND The concept of digital twins has great potential for transforming the existing healthcare system by making it more personalised. As a convergence of healthcare, artificial intelligence, and information and communication technologies, personalised healthcare services developed under the concept of digital twins raise a myriad of ethical issues. While some of the ethical issues are known to researchers working on digital health and personalised medicine, currently there is no comprehensive review that maps major ethical risks of digital twins for personalised healthcare services. OBJECTIVE This paper fills the research gap by identifying major ethical risks of digital twins for personalised healthcare services. We first propose a working definition for digital twins for personalised healthcare services (DTPHS) to facilitate future discussion on the ethical issues related to these emerging digital health services. We then developed a process-oriented ethical map to identify major ethical risks against each of the different data processing phases. METHODS This research aims to address this research gap by providing a comprehensive analysis of major ethical risks of DTPHSs. Due to the scarcity of literature on DTPHSs, we are unable to perform a systematic review of ethical concerns over DTPHSs. Thus, we resort to literature on eHealth, personalised medicine, precision medicine, and information engineering to identify potential issues. We develop a process-oriented ethical map to structure the inquiry in a more systematic way. The ethical map allows us to see how each of the major ethical concerns emerges during the process of transforming raw data into valuable information. RESULTS The process-oriented ethical analysis identified ten operational problems and the relevant ethical values. By structuring the operational problems and relevant ethical values in a clear logical flow, this process-oriented ethical map allows developers of DTPHSs and stakeholders to have a comprehensive overview of major ethical risks while refining the design of DTPHSs. The ethical values section on the map also helps developers of DTPHSs better understand which values they ought to consider while developing solutions for an operational problem they encounter.   CONCLUSIONS It is challenging to address all of the major ethical risks a DTPHS might encounter proactively without a conceptual map at hand. The process-oriented ethical map we propose here can assist developers of DTPHSs in analysing ethical risks in a more systematic manner. CLINICALTRIAL N/A


2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mira W. Vegter ◽  
Hub A. E. Zwart ◽  
Alain J. van Gool

AbstractPrecision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to address the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a ‘data mirror’ that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided.


2020 ◽  
Vol 30 (3) ◽  
pp. 14-16
Author(s):  
Sam Finnikin

Sam Finnikin discusses the growing attention on promoting shared decision making and personalised healthcare across the NHS


BJPsych Open ◽  
2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Eva Petkova ◽  
Hyung Park ◽  
Adam Ciarleglio ◽  
R. Todd Ogden ◽  
Thaddeus Tarpey

Summary This tutorial introduces recent developments in precision medicine for estimating treatment decision rules. The objective of these developments is to advance personalised healthcare by identifying an optimal treatment option for each individual patient based on each patient's characteristics. The methods detailed in this tutorial define composite variables from the patient measures that can be viewed as ‘biosignatures’ for differential treatment response, which we have termed ‘generated effect modifiers’. In contrast to most machine learning approaches to precision medicine, these biosignatures are derived from linear and non-linear regression models and thus have the advantage of easy visualisation and ready interpretation. The methods are illustrated using examples from randomised clinical trials.


Author(s):  
Freda Gonot-Schoupinsky ◽  
Gulcan Garip

Differential qualitative analysis (DQA) was developed as a pragmatic qualitative health methodology for the exploration of individual differences, behaviours, and needs within heterogeneous samples. Existing qualitative methodologies tend to emphasise the identification of general principles, an approach that can lead to standardised treatment, care, and medicine. DQA emphasises the identification of individual variation, in order to inform personalised healthcare. DQA comprises an accessible three-stage approach: first individual profiles are explored and differentiated into research-relevant subgroups; then each subgroup is analysed, and findings identified; finally, the data is analysed in its entirety and overall and subgroup findings are presented. DQA was developed as a new qualitative approach to: (1) emphasise the identification of person and patient-centered findings; (2) facilitate the analysis of sample heterogeneity, including variation in responses and intervention outcomes; (3) provide a convenient, pragmatic, systematic, and transparent methodology; (4) bridge the qualitative-quantitative divide with a mutually accessible approach. DQA may be particularly relevant for mixed methods research, early-stage interventions, and research exploring personalised and patient-centred care, and integrative medicine.


Author(s):  
Felician Campean ◽  
Daniel Neagu ◽  
Aleksandr Doikin ◽  
Morteza Soleimani ◽  
Thomas Byrne ◽  
...  

AbstractUnderpinned by a contemporary view of automotive systems as cyber-physical systems, characterised by progressively open architectures increasingly defined by their interaction with the users and the smart environment, this paper provides a critical and up-to-date review of automotive Integrated Vehicle Health Management (IVHM) systems. The paper discusses the challenges with prognostics and intelligent health management of automotive systems, and proposes a high-level framework, referred to as the Automotive Healthcare Analytic Factory, to systematically collect and process heterogeneous data from across the product lifecycle, towards actionable insight for personalised healthcare of systems.


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