scholarly journals Digital Twin Technology: Revolutionary to improve personalized healthcare

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Harita Garg
Keyword(s):  
Health Care ◽  
Human Enhancement ◽  
Data Driven ◽  
Societal Implications ◽  
Preventative Care ◽  
Ethical Implications ◽  
Health Care Practices ◽  
Digital Twins ◽  
The Status ◽  
Data Driven Approach

Personalized medicine uses fine grained information on individual persons, to pinpoint deviations from the normal. ‘Digital Twins’ in engineering provide a conceptual framework to analyze these emerging data-driven health care practices, as well as their conceptual and ethical implications for therapy, preventative care and human enhancement. Digital Twins stand for a specific engineering paradigm, where individual physical artifacts are paired with digital models that dynamically reflects the status of those artifacts. Moral distinctions namely may be based on patterns found in these data and the meanings that are grafted on these patterns. Ethical and societal implications of Digital Twins are explored. Digital Twins imply a data-driven approach to health care. This approach has the potential to deliver significant societal benefits, and can function as a social equalizer, by allowing for effective equalizing enhancement interventions

I mutevoli confini della medicalizzazione: prospettive e dilemmi del miglioramento umano

2009 ◽  
pp. 17-35
Author(s):  
Antonio Maturo
Keyword(s):  
Health Care ◽  
Health Promotion ◽  
Normal Individual ◽  
Human Enhancement ◽  
Ethical Perspective ◽  
Medical Gaze ◽  
Health Care Practices ◽  
Care Practices ◽  
Biomedical Interventions

- While medicalization is the process of extending the medical gaze on human conditions through the mechanism of pathologization, human enhancement actions are implemented towards normal conditions. In this sense, human enhancement can not be considered either health care or health promotion because its aim is optimization, not healing nor prevention. As the borders between normality and pathology are blurred, biomedical interventions aiming at improving a normal individual today could be conceived as health care practices directed towards a sick person tomorrow. Therefore, human enhancement actions should be analyzed through the lenses of the medicalization-theory proposed by Conrad - but on a long-term scale. Under an ethical perspective, human enhancement interventions - being very heterogeneous - should be analyzed case-by-case.Keywords: medicalization, human enhancement, medicine, normality, health promotion, disease.Parole chiave: medicalizzazione, miglioramento umano, medicina, normalitÀ, promozione della salute, malattia.

scholarly journals Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm

2018 ◽  
Vol 9 ◽  
Author(s):  
Koen Bruynseels ◽  
Filippo Santoni de Sio ◽  
Jeroen van den Hoven
Keyword(s):  
Health Care ◽  
Ethical Implications ◽  
Digital Twins

scholarly journals Ethics and Law in Research on Algorithmic and Data-Driven Technology in Mental Health Care: Scoping Review

10.2196/24668 ◽  
2021 ◽  
Vol 8 (6) ◽  
pp. e24668
Author(s):  
Piers Gooding ◽  
Timothy Kariotis
Keyword(s):  
Mental Health ◽  
Health Care ◽  
Mental Health Care ◽  
Applied Research ◽  
Empirical Studies ◽  
Legal Issues ◽  
Data Driven ◽  
Science Field ◽  
Ethical Implications ◽  
Health Initiatives

Background Uncertainty surrounds the ethical and legal implications of algorithmic and data-driven technologies in the mental health context, including technologies characterized as artificial intelligence, machine learning, deep learning, and other forms of automation. Objective This study aims to survey empirical scholarly literature on the application of algorithmic and data-driven technologies in mental health initiatives to identify the legal and ethical issues that have been raised. Methods We searched for peer-reviewed empirical studies on the application of algorithmic technologies in mental health care in the Scopus, Embase, and Association for Computing Machinery databases. A total of 1078 relevant peer-reviewed applied studies were identified, which were narrowed to 132 empirical research papers for review based on selection criteria. Conventional content analysis was undertaken to address our aims, and this was supplemented by a keyword-in-context analysis. Results We grouped the findings into the following five categories of technology: social media (53/132, 40.1%), smartphones (37/132, 28%), sensing technology (20/132, 15.1%), chatbots (5/132, 3.8%), and miscellaneous (17/132, 12.9%). Most initiatives were directed toward detection and diagnosis. Most papers discussed privacy, mainly in terms of respecting the privacy of research participants. There was relatively little discussion of privacy in this context. A small number of studies discussed ethics directly (10/132, 7.6%) and indirectly (10/132, 7.6%). Legal issues were not substantively discussed in any studies, although some legal issues were discussed in passing (7/132, 5.3%), such as the rights of user subjects and privacy law compliance. Conclusions Ethical and legal issues tend to not be explicitly addressed in empirical studies on algorithmic and data-driven technologies in mental health initiatives. Scholars may have considered ethical or legal matters at the ethics committee or institutional review board stage. If so, this consideration seldom appears in published materials in applied research in any detail. The form itself of peer-reviewed papers that detail applied research in this field may well preclude a substantial focus on ethics and law. Regardless, we identified several concerns, including the near-complete lack of involvement of mental health service users, the scant consideration of algorithmic accountability, and the potential for overmedicalization and techno-solutionism. Most papers were published in the computer science field at the pilot or exploratory stages. Thus, these technologies could be appropriated into practice in rarely acknowledged ways, with serious legal and ethical implications.

scholarly journals Evaluation of Transducer and Signature Selections on the Performance of Artificial Intelligence Machine Tool Wear Prognosis

2021 ◽  
Author(s):  
I-Chun Sun ◽  
Renchi Cheng ◽  
Kuo-Shen Chen
Keyword(s):  
Artificial Intelligence ◽  
Performance Index ◽  
Prediction Accuracy ◽  
Training Data ◽  
Data Driven ◽  
Data Types ◽  
Physical Knowledge ◽  
Status Monitoring ◽  
The Status ◽  
Data Driven Approach

Abstract The qualities of machined products are largely depended on the status of machines in various aspects. Thus, appropriate condition monitoring would be essential for both quality control and longevity assessment. Recently, with the advance in artificial intelligence and computational power, status monitoring and prognosis based on data driven approach becomes more practical. However, unlike machine vision and image processing, where data types are fixed and the performance index has already well defined, sensor selection and index for machine tools are versatile and not standardized at this moment. Without supporting of appropriate domain knowledge for selecting appropriate sensors and adequate performance index, pure data driven approach might suffer from unsatisfied prediction accuracy and needing of excessive training data, as well as the possibility of misjudgment. This would be a key obstacle for promoting data driven based prognosis in general intelligent manufacturing field. In this work, the status monitoring and prediction of a cutter wear problem is investigated to address the above concerns and to demonstrate the possible solutions by hiring a 5-axis machine center equipped with milling cutters of different wear levels. Transducers including accelerometers, microphones, current transformer, and acoustic emission sensors are mounted on the spindle, fixture, and nearby structures to monitor the milling process. The collected data are processed to extract various signatures and the key dominated indexes are identified. Finally, three multilayer perception (MLP) artificial neural network models are established. These models trained by different input features are compared to examine the influence of selected sensors and indexes on the prediction accuracy. The results show that with appropriate sensors and signatures, even with less amount of experimental data, the model can indeed achieve a better prediction. Therefore, a proper selection of indexes guided by physical knowledge based experiment or theoretical investigation would be critical.

Use of a Surgical Debriefing Checklist to Achieve Higher Value Health Care

2018 ◽  
Vol 33 (5) ◽  
pp. 514-522 ◽  
Author(s):  
Michael R. Rose ◽  
Katherine M. Rose
Keyword(s):  
Health Care ◽  
Safety Climate ◽  
Surgical Care ◽  
Data Driven ◽  
Multidisciplinary Teams ◽  
Four Dimensions ◽  
Senior Leader ◽  
Data Driven Approach ◽  
Efficiency And Productivity ◽  
The Ideal

Efforts to improve surgical care by using checklists have been inconsistent in results and not reproducible at scale. The ideal manner for using checklists, along with the time horizon for achieving meaningful and measurable benefits, has been unclear. This article describes a novel process for utilizing debriefing checklists to improve value in surgical care. Debriefings of 54 003 consecutive surgical cases and subsequent analysis of 4523 defects in care by multidisciplinary teams led to rapid-cycle iterative changes in care design and processes. Four dimensions of health care value were achieved: debrief-driven improvements reduced the proportion of surgical cases with reported defects, was associated with a significant reduction in the 30-day unadjusted surgical mortality, lowered costs by substantial gains in efficiency and productivity, and led to a better workforce safety climate. Meaningful and sustained improvements required consistent broad-based teamwork over multiple years, an evidence-based data-driven approach, and senior leader and governance engagement.

Towards Data Driven Stamping Process Modelling by Optical Inline Monitoring of Cutting Surface Parameters

2021 ◽  
Author(s):  
Maximilian Lorenz ◽  
Matthias Menzl ◽  
Christian Donhauser ◽  
Michael Layh ◽  
Bernd R. Pinzer
Keyword(s):  
Quantitative Description ◽  
Quality Metrics ◽  
Process Modelling ◽  
Data Driven ◽  
Cutting Surface ◽  
Image Capturing ◽  
The Status ◽  
Data Driven Approach ◽  
Cut Surface

Abstract Stamping is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. For this reason, a highly efficient process is crucial. The cutting process is sensitive to a multitude of parameters. A process that is not correctly adjusted is subject to considerable wear and therefore not efficient. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a cutting part, which in turn requires a quantitative description of the quality of a part. A data driven approach allows to meet this challenge and quantify these influences. We developed an optical inline monitoring system, which consists of a image capturing, triangulation and image processing, that is capable of deriving quality metrics from 2D images and triangulation data of the cutting surface, directly inside the machine and without affecting the process. We identify features that can be automatically turned into quality metrics, like fraction of the burnish surface or the cut surface inclination. As an application, we show that the status of tool wear can be inferred by monitoring the burnish surface, with immediate consequences for predictive maintenance. Furthermore, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data driven process modelling and ultimately the implementation of intelligent stamping machines.

The Shifting Borders of Medicalization: Perspectives and Dilemmas of Human Enhancement

2009 ◽  
pp. 13-30
Author(s):  
Antonio Maturo
Keyword(s):  
Health Care ◽  
Health Promotion ◽  
Normal Individual ◽  
Human Enhancement ◽  
Ethical Perspective ◽  
Medical Gaze ◽  
Health Care Practices ◽  
Care Practices ◽  
Biomedical Interventions

- While medicalization is the process of extending the medical gaze on human conditions through the mechanism of pathologization, human enhancement actions are implemented towards normal conditions. In this sense, human enhancement can not be considered either health care or health promotion because its aim is optimization, not healing nor prevention. As the borders between normality and pathology are blurred, biomedical interventions aiming at improving a normal individual today could be conceived as health care practices directed towards a sick person tomorrow. Therefore, human enhancement actions should be analyzed through the lenses of the medicalization-theory proposed by Conrad - but on a long-term scale. Under an ethical perspective, human enhancement interventions - being very heterogeneous - should be analyzed case-by-case.Keywords: medicalization, human enhancement, medicine, normality, health promotion, disease.Parole chiave: medicalizzazione, miglioramento umano, medicina, normalitÀ, promozione della salute, malattia.

scholarly journals Observational Study Using the Tools of Lean Six Sigma to Improve the Efficiency of the Resident Rounding Process

2011 ◽  
Vol 3 (2) ◽  
pp. 144-150 ◽  
Author(s):  
David V. Chand
Keyword(s):  
Health Care ◽  
Observational Study ◽  
Survey Data ◽  
Six Sigma ◽  
Care Delivery ◽  
Quality Care ◽  
Lean Six Sigma ◽  
Data Driven ◽  
Data Driven Approach ◽  
Family Centered

Abstract Background Recent focus on resident work hours has challenged residency programs to modify their curricula to meet established duty hour restrictions and fulfill their mission to develop the next generation of clinicians. Simultaneously, health care systems strive to deliver efficient, high-quality care to patients and families. The primary goal of this observational study was to use a data-driven approach to eliminate examples of waste and variation identified in resident rounding using Lean Six Sigma methodology. A secondary goal was to improve the efficiency of the rounding process, as measured by the reduction in nonvalue-added time. Methods We used the “DMAIC” methodology: define, measure, analyze, improve, and control. Pediatric and family medicine residents rotating on the pediatric hospitalist team participated in the observation phase. Residents, nurses, hospitalists, and parents of patients completed surveys to gauge their attitudes toward rounds. The Mann-Whitney test was used to test for differences in the median times measured during the preimprovement and postimprovement phases, and the Student t test was used for comparison of survey data. Results and Discussion Collaborative, family-centered rounding with elimination of the “prerounding” process, as well as standard work instructions and pacing the process to meet customer demand (takt time), were implemented. Nonvalue-added time per patient was reduced by 64% (P  =  .005). Survey data suggested that team members preferred the collaborative, family-centered approach to the traditional model of rounding. Conclusions Lean Six Sigma provides tools, a philosophy, and a structured, data-driven approach to address a problem. In our case this facilitated an effort to adhere to duty hour restrictions while promoting education and quality care. Such approaches will become increasingly useful as health care delivery and education continue to transform.

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