This document discusses the potential for using digital technologies and artificial intelligence to improve healthcare, focusing on a case study of a 69-year-old woman with diabetes, heart disease, and hip fracture. It outlines how passive data collection from smartphones and wearables could help monitor patients remotely and adjust treatments. It also discusses regulatory approvals of digital health technologies and the potential for crowdsourcing data and customized solutions. Challenges around human factors and readiness for precision medicine are noted. The conclusion emphasizes that healthcare may transform to become more participatory and preventative with empowered patients as key partners through exponential technologies.
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Citation of a related scientific paper:
Manea, V., & Wac, K. (2020). Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method. Journal of Personalized Medicine, 10(4), 203.
The talk details:
Katarzyna Wac, "coQoL Approach", International Society for Quality of Life Research (ISOQOL) Conference, PLENARY SESSION: “Video killed the radio star”: How technology is changing the way we collect, analyze and interpret patient-relevant data, October 2020
Video: https://youtu.be/9c5lyD4gQD4
Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland.
The talk details:
Katarzyna Wac, “Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at a Problem?”, Keynote at the ZHAW Digital Health Lab Day, September 2021, Winterthur, Switzerland
Video: https://www.zhaw.ch/de/forschung/departementsuebergreifende-kooperationen/digital-health-lab/3-digital-health-lab-day/
Related Video: https://www.youtube.com/watch?v=WbjSiPT8scI
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Berrocal, A., Manea, V., De Masi, A., Wac, K., mQoL Lab: Step-By-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable And Ubiquitous Devices, 17th International Conference On Mobile Systems And Pervasive Computing (MobiSPC), August 2020.
The talk details:
Alexandre De Masi, Katarzyna Wac, Getting Most out of your SENSORS: Mixed-Methods Research Methodology Enabling Identification, Modelling and Predicting Human Aspects of Mobile Sensing “In the Wild”, 19th IEEE Conference on Sensors (IEEE SENSORS’20), October 2020.
Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland. The talk details:
Katarzyna Wac, “Remote quality of life assessment: ‘What is always speaking silently is the body'”. Digital Health Connect Conference, Sion, Switzerland
Video: https://www.digitalhealthconnect.ch/en/
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Berrocal, A., Manea, V., De Masi, A., Wac, K. mQoL-Lab: Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquitous Devices, MobiSPC 2020
The talk details:
Alexandre De Masi, Igor Matias, Vlad Manea, Allan Berrocal, Katarzyna Wac, “mQoL: Methodology for Assessing and Modeling Human Aspects in Interactive, Mobile, Wearable and Ubiquitous Computing in Situ”, International Transdisciplinarity Conference (ITD) 2021, September 2021.
Video: https://youtu.be/dIxOnqd5g8E
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Manea, V., Wac, K., (2018). mQoL: Mobile Quality of Life Lab: From Behavior Change to QoL, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with ACM UBICOMP, Singapore, October 2018.
Katarzyna Wac, From Quantified Self to Quality of Life, Book Chapter in "Digital Health", Health Informatics, Springer Nature, p. 83-108, Dordrecht, The Netherlands, 2018.
The talk details:
Katarzyna Wac, “Quality of Life Technologies: From Cure to Care”, Société Suisse des Pharmaciens Hospitaliers (GSASA), November 2018, Switzerland
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Manea, V., & Wac, K. (2020). Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method. Journal of Personalized Medicine, 10(4), 203.
The talk details:
Igor Matias, “Co-calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method”, Society for Ambulatory Assessment Conference 2021, July 2021.
Video: https://youtu.be/Ht9n2AlIjDs
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Manea, V., & Wac, K. (2020). Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method. Journal of Personalized Medicine, 10(4), 203.
The talk details:
Katarzyna Wac, "coQoL Approach", International Society for Quality of Life Research (ISOQOL) Conference, PLENARY SESSION: “Video killed the radio star”: How technology is changing the way we collect, analyze and interpret patient-relevant data, October 2020
Video: https://youtu.be/9c5lyD4gQD4
Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland.
The talk details:
Katarzyna Wac, “Multimodal Machine Learning for Quality of Life Assessment: Throwing Data at a Problem?”, Keynote at the ZHAW Digital Health Lab Day, September 2021, Winterthur, Switzerland
Video: https://www.zhaw.ch/de/forschung/departementsuebergreifende-kooperationen/digital-health-lab/3-digital-health-lab-day/
Related Video: https://www.youtube.com/watch?v=WbjSiPT8scI
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Berrocal, A., Manea, V., De Masi, A., Wac, K., mQoL Lab: Step-By-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable And Ubiquitous Devices, 17th International Conference On Mobile Systems And Pervasive Computing (MobiSPC), August 2020.
The talk details:
Alexandre De Masi, Katarzyna Wac, Getting Most out of your SENSORS: Mixed-Methods Research Methodology Enabling Identification, Modelling and Predicting Human Aspects of Mobile Sensing “In the Wild”, 19th IEEE Conference on Sensors (IEEE SENSORS’20), October 2020.
Thank You for referencing this work, if you find it useful!
Citation of a related scientific book:
Wac, K., Wulfovich, S. (2021). Quantifying Quality of Life, Series: Health Informatics, Springer Nature, Cham, Switzerland. The talk details:
Katarzyna Wac, “Remote quality of life assessment: ‘What is always speaking silently is the body'”. Digital Health Connect Conference, Sion, Switzerland
Video: https://www.digitalhealthconnect.ch/en/
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Berrocal, A., Manea, V., De Masi, A., Wac, K. mQoL-Lab: Step-by-Step Creation of a Flexible Platform to Conduct Studies Using Interactive, Mobile, Wearable and Ubiquitous Devices, MobiSPC 2020
The talk details:
Alexandre De Masi, Igor Matias, Vlad Manea, Allan Berrocal, Katarzyna Wac, “mQoL: Methodology for Assessing and Modeling Human Aspects in Interactive, Mobile, Wearable and Ubiquitous Computing in Situ”, International Transdisciplinarity Conference (ITD) 2021, September 2021.
Video: https://youtu.be/dIxOnqd5g8E
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Manea, V., Wac, K., (2018). mQoL: Mobile Quality of Life Lab: From Behavior Change to QoL, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with ACM UBICOMP, Singapore, October 2018.
Katarzyna Wac, From Quantified Self to Quality of Life, Book Chapter in "Digital Health", Health Informatics, Springer Nature, p. 83-108, Dordrecht, The Netherlands, 2018.
The talk details:
Katarzyna Wac, “Quality of Life Technologies: From Cure to Care”, Société Suisse des Pharmaciens Hospitaliers (GSASA), November 2018, Switzerland
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Manea, V., & Wac, K. (2020). Co-Calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method. Journal of Personalized Medicine, 10(4), 203.
The talk details:
Igor Matias, “Co-calibrating Physical and Psychological Outcomes and Consumer Wearable Activity Outcomes in Older Adults: An Evaluation of the coQoL Method”, Society for Ambulatory Assessment Conference 2021, July 2021.
Video: https://youtu.be/Ht9n2AlIjDs
Realize preventive medicine through predictive risk profiling, determining baseline markers of wellness and variability, and engaging in personalized pre-clinical interventions
Support Dementia: using wearable assistive technology and analysing real-time data (Fehmida Mohamedali and Nasser Matoorian)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
The university-wide AI Institute of UofSC (AIISC) is administratively part of the CEC with Dean Hossein Haj-Harriri as its chief patron. This presentation give a quick overview of the CEC.
Sepsis is one of the top causes of inpatient mortality and rapid detection presents numerous challenges. In March, 2016, an interdisciplinary team consisting of top clinicians, data scientists and machine learning experts at a large academic medical center (AMC) embarked on an innovation pilot to develop a novel machine learning model to detect sepsis. A computable sepsis definition and deep learning model were developed using a curated dataset capturing over 43,000 inpatient admissions between October 1, 2014 and December 31, 2015. Ten computable sepsis definitions were compared and our clinicians agreed on the following: >= 2 SIRS criteria, blood culture order, and end organ damage. This sepsis phenotype identified patients early in the hospital course: 38% of cases occur an average of 1.3 hours after presentation to the ED and 42% of cases occur an average of 15 hours after hospital admission. At 4 hours prior to sepsis, the best deep learning model generated 1.4 false alarms per true alarm at a sensitivity of 80%, compared to 3.2 false alarms per true alarm for National Early Warning System (NEWS).
Purpose
Sepsis Watch detects sepsis early, guides completion of appropriate treatment, and supports front-line providers with minimal interruption of clinical workflows. Key Performance Indicators include emergency department (ED) length of stay, hospital length of stay, inpatient mortality, intensive care unit requirement, and time to antibiotics for patients who develop sepsis.
Description
The core technology components of Sepsis Watch are web services to extract electronic health record (EHR) data in real-time, a data pipeline to normalize features, a computable sepsis definition, a deep learning sepsis prediction model, a web application (Figure 1), an automated report that calculates KPI performance, and a model input and output monitoring tool. A suite of education, training, communication, and workflow materials were also prepared with nurse educators and are hosted on an intranet training site. After a three-month silent period, Sepsis Watch was deployed in the ED of the 1,000 bed flagship hospital on November 5, 2018.
Conclusions
Sepsis Watch is the first deployment of deep learning model in real-time to detect sepsis integrated with an EHR. The tool is used by Rapid Response Team (RRT) nurses to provide proactive support to ED providers to identify and manage sepsis. A six-month clinical trial will be completed in May 2019 to rigorously assess the clinical and operational impact of the program.
Validation of Clinical Artificial Intelligence: Where We Are and Where We Are...Sean Manion PhD
This is the deck from a presentation I gave to the Pittsburgh Industrial Statisticians Association (PISA) for their PISA23 event in a session on Artificial Intelligence and Machine Learning.
The deck itself is not intended to be stand alone without the accompanying verbal presentation, however many of the slides contain key elements with references, and my contact information is available at the end if anyone has questions.
Quality of Life Technologies Lab
University of Copenhagen
University of Geneva
In this presentation we describe challenges and opportunities of technology in the context of quantitatively evaluating behaviors, risks, and Quality of Life towards helping people live longer, healthier, and happier.
Quality of Life (QoL) technologies lab vision is to be a leading academic laboratory recognized for inter-disciplinary education, research, design and development aimed at improving Quality of Life of individuals throughout their lives.
The lab mission is to design, develop and evaluate emerging mobile technologies with the goal of assessing individuals’ life quality as it unfolds naturally over time and in context, and improving it at all stages of life.
This presentation has been delivered at the Frederiksberg Toastmasters public speaking club in Copenhagen, Denmark.
+ See a version in Spanish here: https://www.slideshare.net/vlad.manea/calidad-de-vida-a-su-alcance-156831152
+ See a version in Danish here: https://www.slideshare.net/vlad.manea/livskvalitet-lige-ved-hnden
Reference our work in your scientific article:
Katarzyna Wac, From Quantified Self to Quality of Life, Book Chapter in "Digital Health", Health Informatics, Springer Nature, p. 83-108, Dordrecht, The Netherlands, 2018.
Help our research (and your better living) by enrolling in the Quality of Life Living Lab:
https://www.qualityoflifetechnologies.com/living-lab/
Realize preventive medicine through predictive risk profiling, determining baseline markers of wellness and variability, and engaging in personalized pre-clinical interventions
Support Dementia: using wearable assistive technology and analysing real-time data (Fehmida Mohamedali and Nasser Matoorian)
Interactive Technologies and Games (ITAG) Conference 2016
Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT
The university-wide AI Institute of UofSC (AIISC) is administratively part of the CEC with Dean Hossein Haj-Harriri as its chief patron. This presentation give a quick overview of the CEC.
Sepsis is one of the top causes of inpatient mortality and rapid detection presents numerous challenges. In March, 2016, an interdisciplinary team consisting of top clinicians, data scientists and machine learning experts at a large academic medical center (AMC) embarked on an innovation pilot to develop a novel machine learning model to detect sepsis. A computable sepsis definition and deep learning model were developed using a curated dataset capturing over 43,000 inpatient admissions between October 1, 2014 and December 31, 2015. Ten computable sepsis definitions were compared and our clinicians agreed on the following: >= 2 SIRS criteria, blood culture order, and end organ damage. This sepsis phenotype identified patients early in the hospital course: 38% of cases occur an average of 1.3 hours after presentation to the ED and 42% of cases occur an average of 15 hours after hospital admission. At 4 hours prior to sepsis, the best deep learning model generated 1.4 false alarms per true alarm at a sensitivity of 80%, compared to 3.2 false alarms per true alarm for National Early Warning System (NEWS).
Purpose
Sepsis Watch detects sepsis early, guides completion of appropriate treatment, and supports front-line providers with minimal interruption of clinical workflows. Key Performance Indicators include emergency department (ED) length of stay, hospital length of stay, inpatient mortality, intensive care unit requirement, and time to antibiotics for patients who develop sepsis.
Description
The core technology components of Sepsis Watch are web services to extract electronic health record (EHR) data in real-time, a data pipeline to normalize features, a computable sepsis definition, a deep learning sepsis prediction model, a web application (Figure 1), an automated report that calculates KPI performance, and a model input and output monitoring tool. A suite of education, training, communication, and workflow materials were also prepared with nurse educators and are hosted on an intranet training site. After a three-month silent period, Sepsis Watch was deployed in the ED of the 1,000 bed flagship hospital on November 5, 2018.
Conclusions
Sepsis Watch is the first deployment of deep learning model in real-time to detect sepsis integrated with an EHR. The tool is used by Rapid Response Team (RRT) nurses to provide proactive support to ED providers to identify and manage sepsis. A six-month clinical trial will be completed in May 2019 to rigorously assess the clinical and operational impact of the program.
Validation of Clinical Artificial Intelligence: Where We Are and Where We Are...Sean Manion PhD
This is the deck from a presentation I gave to the Pittsburgh Industrial Statisticians Association (PISA) for their PISA23 event in a session on Artificial Intelligence and Machine Learning.
The deck itself is not intended to be stand alone without the accompanying verbal presentation, however many of the slides contain key elements with references, and my contact information is available at the end if anyone has questions.
Quality of Life Technologies Lab
University of Copenhagen
University of Geneva
In this presentation we describe challenges and opportunities of technology in the context of quantitatively evaluating behaviors, risks, and Quality of Life towards helping people live longer, healthier, and happier.
Quality of Life (QoL) technologies lab vision is to be a leading academic laboratory recognized for inter-disciplinary education, research, design and development aimed at improving Quality of Life of individuals throughout their lives.
The lab mission is to design, develop and evaluate emerging mobile technologies with the goal of assessing individuals’ life quality as it unfolds naturally over time and in context, and improving it at all stages of life.
This presentation has been delivered at the Frederiksberg Toastmasters public speaking club in Copenhagen, Denmark.
+ See a version in Spanish here: https://www.slideshare.net/vlad.manea/calidad-de-vida-a-su-alcance-156831152
+ See a version in Danish here: https://www.slideshare.net/vlad.manea/livskvalitet-lige-ved-hnden
Reference our work in your scientific article:
Katarzyna Wac, From Quantified Self to Quality of Life, Book Chapter in "Digital Health", Health Informatics, Springer Nature, p. 83-108, Dordrecht, The Netherlands, 2018.
Help our research (and your better living) by enrolling in the Quality of Life Living Lab:
https://www.qualityoflifetechnologies.com/living-lab/
Stanford University Neuroradiology Talks: Personalizing and Customizing AI Ex...Instituto Superior Técnico
Invited to present the work under development by Institute for Systems and Robotics (ISR-Lisboa) and Interactive Technologies Institute (ITI), the one-hour presentation and discussion were held in the 27th of October 2022. The work was presented remotely to the Department of Rad/Neuroimaging and Neurointervention at Stanford University in California. For this talk, I was invited to present our team, project, and work to the research team of Prof. Greg Zaharchuk. In the end, the presentation proposes and discusses how personalizing and customizing the answers coming from the AI outputs can positively affect the clinical workflow. Moreover, we present how those strategies are promoting the unbiased behavior of clinicians while improving the clinical workflow.
May 2021 snapshot of some of the Research and Collaborations in dHealth/personalized health, public health, epidemiology, biomedicine at the AI Institute of the University of South Carolina [AIISC]
Life expectancy has increased greatly over the past 100 years. Increased wealth, sanitation, and access to pharmaceutical innovation have contributed to our health, allowing us to live longer and healthier lives. We are on a tipping-point of healthcare mostly related to new technologies - big data and genomics, robotics, immunotherapy, remote monitoring, 3D printing, among others, will bring forth a new era in health and standards of care.
Leverage machine learning and new technologies to enhance rwe generation and ...Athula Herath
My personal activities on automating evidence synthesis and real world data derived evidence for automated treatment guidelines compilation for precision medicine.
What are scalable best practices to spread smart health? SharpBrains
Maximizing health and well-being requires quality decision-making and positive lifestyles across millions, if not billions, of individual decision-makers. How can we accelerate the adoption of smart health behaviors in scalable and systematic ways, ensuring benefits at both the individual and population levels, and empowering consumers, patients and professionals?
- Chair: Jayne Plunkett, Head of Casualty Reinsurance at Swiss Re, YGL Class of 2010
- Misha Pavel, Program Director of Smart and Connected Health at the National Science Foundation
- Dharma Singh Khalsa, President of the Alzheimer’s Research and Prevention Foundation
- Josh Wright, Managing Director of ideas42
This session took place at the 2013 SharpBrains Virtual Summit: http://sharpbrains.com/summit-2013/agenda/
Kno.e.sis Approach to Impactful Research & Training for Exceptional CareersAmit Sheth
Abstract
Kno.e.sis (http://knoesis.org) is a world-class research center that uses semantic, cognitive, and perceptual computing for gathering insights from physical/IoT, cyber/Web, and social and enterprise (e.g., clinical) big data. We innovate and employ semantic web, machine learning, NLP/IR, data mining, network science and highly scalable computing techniques. Our highly interdisciplinary research impacts health and clinical applications, biomedical and translational research, epidemiology, cognitive science, social good, policy, development, etc. A majority of our $12+ million in active funds come from the NSF and NIH. In this talk, I will provide an overview of some of our major research projects.
Kno.e.sis is highly successful in its primary mission of exceptional student outcomes: our students have exceptional publication and real-world impact and our PhDs compete with their counterparts from top 10 schools for initial jobs in research universities, top industry research labs, and highly competitive companies. A key reason for Kno.e.sis' success is its unique work culture involving teamwork to solve complex problems. Practically all our work involves real-world challenges, real-world data, interdisciplinary collaborators, path-breaking research to solve challenges, real-world deployments, real-world use, and measurable real-world impact.
In this talk, I will also seek to discuss our choice of research topics and our unique ecosystem that prepares our students for exceptional careers.
Similar to Treated by Computers?- a futuristic perspective of health care (20)
Igor Matias, “Data overdose: side effects and metabolism of big data in the 21st century”, AI and Big Data in Healthcare, The University of Oklahoma Health Sciences Center, USA. 1 February 2023.
Thank You for referencing this work, if you find it useful!
Citation of a related scientific paper:
Manea, V., Wac, K., (2018). mQoL: Mobile Quality of Life Lab: From Behavior Change to QoL, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with ACM UBICOMP, Singapore, October 2018.
The talk details:
Vlad Manea, Can Data Decide Your Health?, Future Healthcare? Human & IT & Legal Perspectives Seminar, Copenhagen, Denmark
Thank You for referencing this work, if you find it useful!
Allan Berrocal, Katarzyna Wac, Peer-vasive Computing: Leveraging Peers to Enhance the Accuracy of Self-Reports in Mobile Human Studies, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with UBICOMP, Singapore, October 2018.
Thank You for referencing this work, if you find it useful!
Berrocal, A., Wac, K., (2018). Peer-ceived Well-Being: Exploring the Value of Peers for Human Stress Assessment in-Situ, ACM UBICOMP/ISWC Doctoral Colloquium, Singapore, October 2018.
Thank You for referencing this work, if you find it useful!
Vlad Manea, Katarzyna Wac, mQoL: Mobile Quality of Life Lab:
From Behavior Change to QoL, Mobile Human Contributions: Opportunities and Challenges (MHC) Workshop in conjunction with UBICOMP, Singapore, October 2018.
This talk has been presented in November 2017:
Katarzyna Wac, Connecté Pour Mieux Vivre, Careers and Opportunities Network Cafe titled "Nouvelles Technologies de l'information: En Finir Avec Les Préjugés", University of Geneva, Switzerland, Nov 2017.
Thank You for referencing this work, if you find it useful!
Reference/Citation for a latest scientific paper related to this talk:
Katarzyna Wac, From Quantified Self to Quality of Life, Book Chapter in "Digital Health", Health Informatics, Springer Nature, p. 83-108, Dordrecht, The Netherlands, 2018.
Thank You for referencing this work, if you find it useful!
Reference/Citation to a book chapter: Katarzyna Wac, From Quantified Self to Quality of Life, Book Chapter in "Digital Health - Scaling Healthcare to the World", Health Informatics, Springer Nature, Dordrecht, The Netherlands, 2017
Note: The chapter has been presented along the following seminar:
Katarzyna Wac, From Quantified Self to Quality of Life, Research Seminar: Tracking Experience – Enhancing Lives?, University of Copenhagen, Denmark, Apr 2017.
http://ccc.ku.dk/calendar/2017/tracking-experience-enhancing-lives/
Mental Health Care Technologies: Context-Aware Stress Assessment and Stress Coping
Thank You for referencing this work, if you find it useful!
Reference/Citation: Allan Berrocal, Mental Health Care Technologies: Context-Aware Stress Assessment and Stress Coping, CUSO PhD school 2017.
Additional Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Talk given:
Katarzyna Wac, Innovations for Global Health Challenges (Panel), ITU-WHO Policy Dialogue on Digital Health for “Healthy Lives and Wellbeing for All (SDG3)” in parallel to the World Health Assembly, Geneva, Switzerland, May 2016.
Please reference this work if you find it useful as follows (related paper):
Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Thank You for referencing this work, if you find it useful!
Reference/Citation to a paper: Alexandre de Masi, Katarzyna Wac, MIQModel: Predictive Model for Mobile Internet, International Workshop on Traffic Monitoring and Analysis (TMA), PhD Poster Session, Brussels, Belgium, April 2016.
Keynote at the 21st Congress of the European Association of Hospital Pharmacists (EAHP), 2016. Thank You for referencing this work, if you find it useful!
Reference/Citation for a latest scientific paper related to this talk: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
FULL VIDEO OF THE TALK: http://quadia.webtvframework.com/farma_actueel/_app/presentation/?offset=0&id=1371946
Slides/Videos URLs:
Slide 66 http://www.youtube.com/watch?v=r9bLZFxTb_0
Slide 98 http://www.youtube.com/watch?v=-hhOtjdkU34
Slide 117 http://www.youtube.com/watch?v=9mHnCnX4al0
Slide 120 http://www.youtube.com/watch?v=pw68x_4L2NQ
Slide 139 DELL video: Confidential, Restricted Use Rights
Thank You for referencing this work, if you find it useful!
Reference/Citation: Katarzyna Wac, Jenny-Margrethe Vej, Kimie Bodin Ryager, Quality of Life Technologies: From Fundamentals of Mobile Computing to Patterns of Sleep and Happiness (Poster), 5th EAI International Symposium on Pervasive Computing Paradigms for Mental Health (MindCare), Milan, Italy, September 2015.
Additional Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Thank You for referencing this work, if you find it useful!
Reference/Citation to a paper: Katarzyna Wac, Gerardo Pinar, Mattia Gustarini, Jerome Marchanoff, More Mobile & Not so Well-connected yet: Users' Mobility Inference Model and 6 Month Field Study, 7th International Congress on Ultra Modern Telecommunications and Control Systems (ICUMT 2015), Brno, Czech Republic, October 2015.
Reference/Citation to a poster: Daniel Weibel, Katarzyna Wac, Towards a Predictive Model for Mobile Internet Quality, International Workshop on Traffic Monitoring and Analysis (TMA), PhD Poster Session, Barcelona, Spain, May 2015.
Thank You for referencing this work, if you find it useful!
Reference/Citation: Matteo Ciman, Katarzyna Wac, Ombretta Gaggi, iSenseStress: Assessing Stress Through Human-Smartphone Interaction Analysis, 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth), Istanbul, Turkey, May 2015.
Thank You for referencing this work, if you find it useful!
Reference/Citation: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Presented at the XIV Congrès de la Société Francophone d'Analyse du Mouvement chez l'Enfant et l'Adulte (SOFAMEA), Geneva, Switzerland, Feb 2015. See Video of recording: https://mediaserver.unige.ch/play/87916
Thank You for referencing this work, if you find it useful!
Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Reference/Citation: Katarzyna Wac, Smartphone Based Ambulatory Assessment of Health Risks: Literature Review, Poster at Society for Ambulatory Assessment 2013 Conference (SAA 2013), Amsterdam, the Netherlands, June 2013.
Katarzyna Wac, Smartphone as a Personal, Pervasive Health Informatics Services Platform: Literature Review, IMIA Yearbook 2012: Personal Health Informatics, 7(1), pp.83-93.
Thank You for referencing this work, if you find it useful!
Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Reference/Citation for the talk: Katarzyna Wac, Smartphone as a Personal Pervasive Health Information Services Platform, The International Conference on Using Advanced Technologies for Elderly Care: A New Paradigm (ElderlyTech 2013), Tel Aviv, Israel, Feb 2013
Reference/Citation for a scientific paper: Katarzyna Wac, Smartphone as a Personal, Pervasive Health Informatics Services Platform: Literature Review, IMIA Yearbook 2012: Personal Health Informatics, 7(1), pp.83-93.
Thank You for referencing this work, if you find it useful!
Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Reference/Citation to the talk: Katarzyna Wac, Muhammad Ullah, Markus Fiedler, Richard Bults, QoS and QoE for medical applications, European Telecommunications Standards Institute workshop on "Quality of Service - Quality of Experience", ETSI, France, Sep 2010
Reference/Citation to a scientific paper: Katarzyna Wac, Smartphone as a Personal, Pervasive Health Informatics Services Platform: Literature Review, IMIA Yearbook 2012: Personal Health Informatics, 7(1), pp.83-93.
Thank You for referencing this work, if you find it useful!
Reference/Citation for a latest scientific paper: Katarzyna Wac, Maddalena Fiordelli, Mattia Gustarini, Homero Rivas, Quality of Life Technologies: Experiences from the Field and Key Research Challenges, IEEE Internet Computing, Special Issue: Personalized Digital Health, July/August 2015.
Reference/Citation to the talk: Katarzyna Wac, mHealth Services Science - Science in the Service of Life Quality, ITU Experts Group Meeting held within the framework of the ITU European Regional Initiative on ICT Applications, including e-Health: "m-Health: Towards Better Care, Cure and Prevention in Europe", Switzerland, Sep 2012
Reference/Citation to a scientific paper: Katarzyna Wac, Smartphone as a Personal, Pervasive Health Informatics Services Platform: Literature Review, IMIA Yearbook 2012: Personal Health Informatics, 7(1), pp.83-93.
Thank You for referencing this work, if you find it useful!
Reference/Citation: Selim Ickin, Katarzyna Wac, Markus Fiedler, Lucjan Janowski, Jin-Huyk Hong, Anind K. Dey, Factors Influencing Quality of Experience of Commonly-Used Mobile Applications. IEEE Communications Magazine (IEEE COMMAG), Special Issue on QoE Management in Emerging Multimedia Services, 50(4): 48-56, IEEE Press, April 2012.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
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Treated by Computers?- a futuristic perspective of health care
1. Quality of Life Technologies Lab
University of Geneva, Switzerland
www.qol.unige.ch
Treated by Computers?
- a futuristic perspective of health care
Prof. Katarzyna Wac
2021
with the assistance of
4. A Patient (female, 69)
Type 2 Diabetes (1992)
Heart attack (2014)
Hip fracture & replacement (2016)
Loves cooking
Much (too much) food (carbs)
Clinical Case
5. Not the Only One
Naghavi, M., Abajobir, A. A., Abbafati, C., Abbas, K. M., Abd-Allah, F., Abera, S. F., ... & Ahmadi, A. (2017). Global, Regional, And National Age-Sex Specific Mortality For 264 Causes Of Death, 1980–2016: A Systematic Analysis For The Global Burden Of
Disease Study 2016. The Lancet, 390 (10100), 1151-1210.
Mokdad, A. H., Marks, J. S., Stroup, D. F., & Gerberding, J. L. (2004). Actual Causes of Death in the United States, 2000. Jama, 291 (10), 1238-1245.
Behavioural patterns Deaths
Tobacco intake 18.1%
Poor diet
Physical inactivity
16.6%
Alcohol consumption 3.5%
...
{
8. ‘Behaviour marker’, ‘Behaviome’,
(↗ 📱‘Digital Biomarkers’)
If you can’t measure it,
you can’t improve it.
William Thomson, 1st Baron Kelvin, 1824-1907
10. Dey, A. K., Wac, K., Ferreira, D., Tassini, K., Hong, J. H., & Ramos, J. (2011). Getting closer: An Empirical Investigation Of The Proximity Of User To Their Smart Phones. In Proceedings of the ACM UBICOMP.
Liquid State. The Rise of mHealth Apps: A market snapshot. liquid-state.com/mhealth-apps-market-snapshot (2018)
The dawn of digital medicine, The Economist Group Limited, December 2020
Smartphone
88%
of the time
next to us
300’000+ health apps
200 added daily
11. Wearables database vandrico.com/wearables.html (431 wearables as of 12.2.2021)
Wac, K. (2018). From Quantified Self to Quality of Life, Chapter in: Digital Health: Scaling Healthcare to the World, Series: Health Informatics, Springer Nature, Dordrecht, the Netherlands.
Annotated 438 wearables dataset doi.org/10.6084/m9.figshare.9702122
Wearables
438 wearables (2018)
biomedicine
information technology
miniaturization (incl. battery)
wirelessness
mobility
connectivity
13. Patient Today
the right patient
the right indication
the right treatment (e.g., medicine)
the right dose
the right route
the right time
the right response
the right evaluation
the right information
the right documentation
exponential
technologies
14. Patient Tomorrow = Primary Point of Care
team member
‘augmented
reasoning’
Health care = personalized, predictive, preventative, participatory
Patient = expert in own health, equipped, enabled, engaged, empowered
16. FDA Approvals
72 example approvals (6 for treatment)
61: 510(k) premarket notific. (moderate risk, class II)
9: de novo pathway (‘reasonable assurance of safety and effectiveness’)
2: premarket approval (PMA) (high risk, class III)
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020). The State Of Artificial Intelligence-Based FDA-Approved Medical Devices And Algorithms: An Online Database, NPJ digital medicine, 3(1), 1-8.
Also at medicalfuturist.com/fda-approved-ai-based-algorithms
Note: Figure maps 29 Algorithms out of 72 in the online DB
17. 2016: Digital Stethoscope (510(k))
AI: none
Symptoms/
Behaviours:
self-reported
Vasudevan, R. S., Horiuchi, Y., Torriani, F. J., Cotter, B., Maisel, S. M., Dadwal, S. S., ... & Maisel, A. S. (2020). Persistent Value of the Stethoscope in the Age of COVID-19. The American journal of medicine. Also at stethio.com
18. 2019: ACR | LAB Urine Analysis Test (510(k))
AI-Assessment
ketones, leukocytes, nitrites,
glucose, protein, blood, specific
gravity, bilirubin, urobilinogen, pH
AI: computer vision for
color matching
Symptoms/Behaviours:
none
Leddy, J., Green, J. A., Yule, C., Molecavage, J., Coresh, J., & Chang, A. R. (2019). Improving proteinuria screening with mailed smartphone urinalysis testing in previously unscreened patients with hypertension: a randomized controlled trial. BMC
nephrology, 20(1), 1-7. Also at healthy.io
19. 2012, 2014, 2017, ...: Cardiology (510(k))
AI-Assessment
▪ continuous ECG (RootiCare)
▪ atrial fibrillation (BodyGuardian, AliveCor 6-leads)
▪ physical activity
AI: detect AF episodes
Symptoms/Behaviours: self-reported
www.rooticare.com
Teplitzky, B., McRoberts, M., Mehta, P. Ghanbari, H. Real world performance of atrial fibrillation detection from wearable patch ECG monitoring using deep learning. Poster presented at Heart Rhythm 40th Scientific Sessions May 8-11, 2019, San
Francisco, CA. Heart Rhythm Journal, Vol. 16 (5), S1 - S712, S-PO02-197. Also at preventicesolutions.com/hcp/body-guardian-heart
Sugrue, A., & Asirvatham, S. J. (2018). Highlights from Heart Rhythm 2018: Innovative Techniques. The Journal of Innovations in Cardiac Rhythm Management, 9(9), 3330. Also at alivecor.com
20. 2019: Cardiac Ultrasound Imaging (de novo)
AI-Assessment
▪ cardiac sonographer skills
AI: computer vision for image
acquisition and optimization,
image analysis
Schneider, M., Bartko, P., Geller, W., Dannenberg, V., König, A., Binder, C., ... & Binder, T. (2020). A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate
estimation of LVEF. The International Journal of Cardiovascular Imaging, 1-10. Also at captionhealth.com
21. 2018: Wearable for Seizure Monitoring (510(k))
AI-Assessment
▪ Electrodermal Activity (EDA)
▪ physical activity
AI: Detect tonic-clonic seizures
Symptoms/Behaviours: self-reported
Meritam, P., Ryvlin, P., & Beniczky, S. (2018). User‐based evaluation of applicability and usability of a wearable accelerometer device for detecting bilateral tonic–clonic seizures: A field study. Epilepsia, 59, 48-52. Also at empatica.com
22. 2020: Gaming for Children with ADHD (de novo)
AI-Assessment
▪ attention
▪ movements, game insights (goals)
AI: Adapt interaction pattern
attention, impulsivity, working memory, goal management, spatial
navigation, memory, and planning and organization
Symptoms/Behaviours: self-reported
Kollins, S. H., DeLoss, D. J., Cañadas, E., Lutz, J., Findling, R. L., Keefe, R. S., ... & Faraone, S. V. (2020). A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): a randomised controlled trial. The Lancet Digital
Health, 2(4), e168-e178. Also at akiliinteractive.com
23. 2019: Opioid Use Disorder (510(k))
AI-Assessment
▪ patterns of craving
▪ patterns of triggers
AI: none
Symptoms/Behaviours:
self-reported
Velez, F. F., Colman, S., Kauffman, L., Ruetsch, C., & Anastassopoulos, K. (2020). Real-world reduction in healthcare resource utilization following treatment of opioid use disorder with reSET-O, a novel prescription digital therapeutic. Expert Review
of Pharmacoeconomics & Outcomes Research, 1-8. Also at peartherapeutics.com
24. 2018: Managing Type 1 Diabetes (de novo)
AI-Assessment: behavioural patterns
AI: insulin dose adjustment
every 3 weeks
Symptoms/Behaviours: self-reported
glucose and insulin, activity, carbs
Nimri, R., Oron, T., Muller, I., Kraljevic, I., Alonso, M. M., Keskinen, P., ... & Phillip, M. (2020). Adjustment of Insulin Pump Settings in Type 1 Diabetes Management: Advisor Pro Device Compared to Physicians’ Recommendations. Journal of Diabetes
Science and Technology, 1932296820965561. Also at dreamed-diabetes.com
26. Nov 2019: Digital Healthcare Act (DVG: Digitale-Versorgung-Gesetz)
Digitalen Gesundheitsanwendungen (DiGA) Registry
Low Risk (class I or class IIa medical devices)
Evidence (12 m): safety, functionality, quality, data, protection/ security,
care effect (SGB V, § (2): (i) medical benefit (i.e., therapeutic effect)
or (ii) procedural/structural health care improvement
Gerke, S., Stern, A. D., & Minssen, T. (2020). Germany’s digital health reforms in the COVID-19 era: lessons and opportunities for other countries. NPJ digital medicine, 3(1), 1-6.
Digitalen Gesundheitsanwendungen (DiGA) Registry, diga.bfarm.de/de/verzeichnis, 2021
10 apps (17 Feb 2021)
7: provisionally approved
3: permanently approved
27. M-sense Migräne: Migraine (provisional)
AI-Assessment
behavioural patterns
AI: attack classification
Symptoms/Behaviours:
self-reported attacks
Use: as needed, for life (?)
Roesch, A., Dahlem, M. A., Neeb, L., & Kurth, T. (2020). Validation of an algorithm for automated classification of migraine and tension-type headache attacks in an electronic headache diary. The Journal of Headache and Pain, 21(1), 1-10.
Also at m-sense.de
32. 2000: Help to Find a Vaccine
Aggregated, crowdsourced synchronized computational resources
Running simulations to find potential drug candidates
For cancer, ALS, Parkinson’s, COVID-19, ...
Liverpool, L. (2020). Put your computer to work. New Scientist, 248(3302), 51. Also at foldingathome.org
Zimmerman, M. I., Porter, J. R., Ward, M. D., Singh, S., Vithani, N., Meller, A., ... & Bowman, G. R. (2020). Citizen scientists create an exascale computer to combat COVID-19. BioRxiv.
33. 2004: Find me a Treatment
Wicks, P., Massagli, M., Frost, J., Brownstein, C., Okun, S., Vaughan, T., ... & Heywood, J. (2010). Sharing health data for better outcomes on PatientsLikeMe. Journal of medical Internet research, 12(2), e19. Also at: patientslikeme.com
Also for practitioners: Figure 1 Medical Case Sharing figure1.com and for individuals wanting to donate their datasets: openhumans.org
34. 2021: Design for My Needs
Young Onset Parkinson’s disease: Jimmy Choi (44, since 27)
Also project careables.org
35. 2015: Extreme DIY Treatment
T1DM: Dana Lewis (since age of 14)
▪ 2015: Open Artificial Pancreas System
▫ Feb 2021: 2211 active users
▪ FDA Approvals & Actions
▫ 2016: Artificial pancreas (Medtronics)
▫ 2018: FDA Premarket approval (PMA)
▫ Guardian Connect System(R)
▫ AI: predicting glucose 60 min ahead
▫ 2019: Patient Engagement Advisory
Committee
Asarani, N. A. M., Reynolds, A. N., Elbalshy, M., Burnside, M., de Bock, M., Lewis, D. M., & Wheeler, B. J. (2020). Efficacy, safety, and user experience of DIY or open-source artificial pancreas systems: a systematic review. Acta Diabetologica, 1-9.
Also at OpenAPS.org
37. Wac, K., Rivas, H., & Fiordelli, M. (2016). Use and misuse of mobile health information technologies for health self-management. Annals of Behavioral Medicine, 50 (Supplement 1).
Wac, K. et al., (2019). “I Try Harder”: The Design Implications for Mobile Apps and Wearables Contributing to Self-Efficacy of Patients with Chronic Conditions, Frontiers in Psychology, SI: Improving Wellbeing in Patients with Chronic Conditions.
What applications do you use,
that support your Quality of Life?
What is your current experience
of Quality of Life?
Human Factors
‘I just want my life back’ (S111)
38. Human Factors
Study details
N = 200 participants (US)
Affinity clustering of significant factors
Q: Do you use technologies (smartphone/wearable) for your own health/care?
Wac, K., Rivas, H., & Fiordelli, M. (2016). Use and misuse of mobile health information technologies for health self-management. Annals of Behavioral Medicine, 50 (Supplement 1).
Wac, K. et al., (2019). “I Try Harder”: The Design Implications for Mobile Apps and Wearables Contributing to Self-Efficacy of Patients with Chronic Conditions, Frontiers in Psychology, SI: Improving Wellbeing in Patients with Chronic Conditions.
42. Precision Medicine Readiness Principles. Portfolio Overview Call, January 29th, 2020. Barriers from World Economic Forum weforum.org
Precision Medicine Readiness Principles
Behavioural
44. What about my Mom?
Red: infrequent finger pricks, 3 / day
Freestyle Libre
45. New medication → worse sleep...
Change back to old meds
Mar
2017
May
2017
July
2017
What about my Mom?
August
2017
46. Exponential Technologies
‘Take Home’ Messages
Change patients (4E)
▪ experts in own health, equipped, enabled, engaged, empowered
Change health care (4P)
▪ personalized, predictive, preventative, participatory
Challenges are numerous, ‘systemic’ approach is needed
Future focus: behaviome & digital biomarkers
47. Quality of Life Technologies Lab
www.qol.unige.ch
Thank You
Prof. Katarzyna Wac and the QoL Team
Quality of Life, Center for Informatics, University of Geneva, Switzerland
katarzyna.wac@unige.ch
Images: unsplash.com and icons8.com
2021