This document discusses how broadband technologies can enable personalized and participatory medicine. It describes how broadband can help improve youth mental health services and aged care through remote monitoring, telehealth, and electronic health records. The convergence of medicine and digital technologies is creating an information ecosystem that will facilitate more efficient preventative, diagnostic and therapeutic solutions where citizens have access to their genetic and health data. High-capacity broadband networks that transmit large volumes of data will be important for concepts like personalized medicine and participatory health to become feasible.
Presentation by Prof. Fernando Martin-Sanchez at the "Carlton Connect" Interdisciplinary conference in Melbourne, 2012.
http://www.carltonconnect.com.au/Conference/Conference.html
Health and Biomedical informatics aims to use information processing for preventative medicine. The presentation outlines current challenges in medicine including the need for earlier diagnosis, personalized therapies, and improved disease classification. It presents a vision called "Health by Equation" which uses an informatics system to calculate an individual's health profile based on genetic and environmental factors to guide prevention and treatment recommendations. Opportunities from health informatics and technology include measuring an individual's genome, phenome, and exposome over lifetime through various sensors. This enables concepts like personalized medicine, participatory health through social media, and crowdsourced clinical trials.
Using Mobile Technologies in Health Research at NIHyan_stanford
The document discusses mHealth and the NIH's role in supporting related research. It defines mHealth as using mobile technologies like phones and sensors to improve healthcare. The NIH funds mHealth through grants and initiatives. Challenges include the fast pace of tech versus slow funding cycles. The NIH hosts workshops and trainings to foster collaboration and develop evidence-based mHealth research.
Realize preventive medicine through predictive risk profiling, determining baseline markers of wellness and variability, and engaging in personalized pre-clinical interventions
This document discusses the opportunity for transformation in healthcare through a P4 (Predictive, Preventive, Personalized, and Participatory) approach. It notes that the current healthcare system spends most of its resources on treating preventable chronic diseases. It proposes using complex systems approaches and personalized medicine to shift focus toward prevention, wellness, and patient engagement. The document outlines pilot projects at Ohio State applying a P4 approach to wellness and care coordination for chronic conditions.
In June this year, Prof Martin-Sanchez traveled to Heidelberg, Germany to represent HBIR and University of Melbourne participating in a three day scientific symposium "Biomedical Informatics: Confluence of Multiple Disciplines”.
These are the slides from the presentation he gave to the symposium.
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
Presentation by Prof. Fernando Martin-Sanchez at the "Carlton Connect" Interdisciplinary conference in Melbourne, 2012.
http://www.carltonconnect.com.au/Conference/Conference.html
Health and Biomedical informatics aims to use information processing for preventative medicine. The presentation outlines current challenges in medicine including the need for earlier diagnosis, personalized therapies, and improved disease classification. It presents a vision called "Health by Equation" which uses an informatics system to calculate an individual's health profile based on genetic and environmental factors to guide prevention and treatment recommendations. Opportunities from health informatics and technology include measuring an individual's genome, phenome, and exposome over lifetime through various sensors. This enables concepts like personalized medicine, participatory health through social media, and crowdsourced clinical trials.
Using Mobile Technologies in Health Research at NIHyan_stanford
The document discusses mHealth and the NIH's role in supporting related research. It defines mHealth as using mobile technologies like phones and sensors to improve healthcare. The NIH funds mHealth through grants and initiatives. Challenges include the fast pace of tech versus slow funding cycles. The NIH hosts workshops and trainings to foster collaboration and develop evidence-based mHealth research.
Realize preventive medicine through predictive risk profiling, determining baseline markers of wellness and variability, and engaging in personalized pre-clinical interventions
This document discusses the opportunity for transformation in healthcare through a P4 (Predictive, Preventive, Personalized, and Participatory) approach. It notes that the current healthcare system spends most of its resources on treating preventable chronic diseases. It proposes using complex systems approaches and personalized medicine to shift focus toward prevention, wellness, and patient engagement. The document outlines pilot projects at Ohio State applying a P4 approach to wellness and care coordination for chronic conditions.
In June this year, Prof Martin-Sanchez traveled to Heidelberg, Germany to represent HBIR and University of Melbourne participating in a three day scientific symposium "Biomedical Informatics: Confluence of Multiple Disciplines”.
These are the slides from the presentation he gave to the symposium.
Is the increasing availability of automated image analysis a possibility to strengthen the application of diffusion-MRI as a biometric parameter, and to enhance the future of image biobanks? Or is this evolution threatening the position of radiologists as medical doctors. Is a redefinition of radiologist as computer technicians inevitable?
From Digitally Enabled Genomic Medicineto Personalized HealthcareLarry Smarr
The document discusses the future of personalized healthcare through digital health technologies and genomic medicine. It describes how continuous monitoring of various biological sensors can capture temporal data on factors like physical activity, diet, sleep, environmental exposures and more. This comprehensive data combined with clinical records, genetic information, and microbial metagenomic analysis can enable true preventative medicine through early detection, feedback loops, and tuning of lifestyle and medical factors.
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...Amit Sheth
Keynote at On the Move conference, October 2011, Greece.
Abstract:
Traditionally, we had to artificially simplify the complexity and richness of the real world to constrained computer models and languages for more efficient computation. Today, devices, sensors, human-in-the-loop participation and social interactions enable something more than a “human instructs machine” paradigm. Web as a system for information sharing is being replaced by pervasive computing with mobile, social, sensor and devices dominated interactions. Correspondingly, computing is moving from targeted tasks focused on improving efficiency and productivity to a vastly richer context that support events and situational awareness, and enrich human experiences encompassing recognition of rich sets of relationships, events and situational awareness with spatio-temporal-thematic elements, and socio-cultural-behavioral facets. Such progress positions us for what I call an emerging era of “computing for human experience” (CHE). Four of the key enablers of CHE are: (a) bridging the physical/digital (cyber) divide, (b) elevating levels of abstractions and utilizing vast background knowledge to enable integration of machine and human perception, (c) convert raw data and observations, ranging from sensors to social media, into understanding of events and situations that are meaningful to humans, and (d) doing all of the above at massive scale covering the Web and pervasive computing supported humanity. Semantic Web (conceptual models/ontologies and background knowledge, annotations, and reasoning) techniques and technologies play a central role in important tasks such as building context, integrating online and offline interactions, and help enhance human experience in their natural environment.
In this talk I will discuss early enablers of CHE including semantics-empowered social networking and sensor Web, and computation of higher level abstractions from raw and phenomenological data. An article in IEEE Internet Computing provides background information: http://bit.ly/HumanExperience
Keynote at: https://www.springer.com/us/book/9783642251054
Event Date: Oct 18, 2011
Crowdsourcing applied to knowledge management in translational research: the ...SC CTSI at USC and CHLA
Date: November 8th, 2018
Speaker: Andrew Su, PhD, Professor, Department of Integrative, Structural and Computational Biology, The Scripps Research Institute
Overview: Crowdsourcing involves the engagement of large communities of individuals to collaboratively accomplish tasks at massive scale. These tasks could be online or offline, paid or for free. But how can crowdsourcing science help your research? This webinar will describe two crowdsourcing projects for translational research, both of which aim to better organize biomedical information so that it can be more easily accessed, integrated, and queried:
First, the goal of the Gene Wiki project is to create a community-maintained knowledge base of all relationships between biological entities, including genes, diseases, drugs, pathways, and variants. This project draws on the collective efforts of informatics researchers from a wide range of disciplines, including bioinformatics, cheminformatics, and medical informatics.
Second, the Mark2Cure project partners with the citizen scientist community to extract structured content from biomedical abstracts with an emphasis on rare disease. Although citizen scientists do not have any specialized expertise, after receiving proper training, Mark2Cure has shown that in aggregate they perform bio-curation at an accuracy comparable to professional scientists.
This document provides an overview of a teacher guide for an activity on the bioethics of gene therapy. The activity uses three case studies of past gene therapy trials to explore ethical issues: a successful case, an unsuccessful case, and a mixed case. Students use an ethical decision-making model to examine each case from the perspective of stakeholders like patients, researchers, and companies. The goal is for students to consider ethical questions about risks, outcomes, and whether experimental gene therapy trials should continue given their challenges.
There are only around 500 geneticists and 2,400 genetic counselors in the U.S. to help integrate genomic medicine into patient care. DNA Direct aims to address this shortage and other barriers through technology solutions that provide education, decision support, and expert guidance to patients, providers, payors, and medical centers. Their programs have shown success in improving patient compliance with genetic screening and understanding of test results.
Home health care & long-term conditions: How to succeed with personal health ...Mohammad Al-Ubaydli
Dr Mohammad Al-Ubaydli (CEO of Patients Know Best)
Dr Al-Ubaydli is author of the book "Personal health records: A guide for clinicians", in which he surveyed the different ways in which patients can work with their clinical team using software. A new generation of tools allows patients to manage their health and Mohammad will cover some of these in his talk, including products by large US companies like Google and Microsoft, as well as the UK software industry. He will also describe the experiences of his own company, Patients Know Best, which integrates its patient-controlled medical records platform into the NHS secure network.
Towards Digitally Enabled Genomic Medicine: the Patient of The FutureLarry Smarr
12.02.22
Invited Speaker
Hacking Life
TTI/Vanguard Conference
Title: Towards Digitally Enabled Genomic Medicine: the Patient of The Future
San Jose, CA
CHIC is a nonprofit collaborative in Northeastern Minnesota that provides regional access to health care information through technology and partnerships. Its mission is to help members improve care and save costs. CHIC programs include emergency preparedness coordination, a health information exchange called HIE-Bridge that allows quick access to patient records, an immunization registry, and helping providers apply for telehealth funding. CHIC aims to build bridges to quality health care through these collaborative programs.
(1) The system segments histopathology images into epithelial and stromal regions and identifies nuclei.
(2) It constructs a rich set of quantitative features describing the relationships between different image objects.
(3) Using the features, a predictive model is built from images of patients with known 5-year survival outcomes. This model can then predict survival probabilities for new unlabeled images.
The document discusses the use of computers and technology in medicine. It covers topics like telemedicine, different medical imaging technologies like MRI, CT scans, mammography and ultrasound. It also discusses the importance of telemedicine in increasing access to healthcare and reducing costs, as well as some issues regarding telemedicine like the need for appropriate infrastructure and privacy concerns.
The document discusses the transition from personalized medicine to personal health. It notes the current challenges in medicine including the need for earlier diagnosis, more personalized therapies, and improved disease classification. Personalized medicine uses an individual's genetic and molecular profile to guide risk assessment and treatment. However, personal health empowers patients by providing them access to their own health data through technologies like sensors, apps, and personal health records. This allows patients to better monitor and manage their health. Realizing personal health will require overcoming challenges regarding privacy, security, and ensuring equitable access to technologies and data interpretation.
The rise of the 'ePatient': how it is affecting clinical practice and research
The document discusses how engaged patients, or "ePatients", who actively gather their own health data and conduct their own research are affecting clinical practice and research. It describes how ePatients are empowered through personal health records, diagnostic testing, genomic data, and self-monitoring devices. This shift towards participatory health challenges traditional clinician-led models and will require changes in areas like privacy, education, and how data is integrated into care.
The document discusses integrating genomics data and evidence-based medicine into electronic health records (EHRs) for precision healthcare. It notes the gap between what is known and what is done in healthcare. Integrating genomics could help do the right thing for each patient through pharmacogenomics. However, challenges include representing huge volumes of molecular data in a usable way in EHRs. A three step approach is proposed: 1) get genomic data into EHRs in a structured format, 2) use that data for clinical decision support, 3) evaluate outcomes and continually improve the system.
Presentation given by Prof Fernando J Martin-Sanchez at the HISA (Health Informatics Society Australia) event "A Leap into E-Health" - see http://www.hisa.org.au/events/event_details.asp?id=211738 for further details - on 29th February 2012.
UCSF Informatics Day 2014 - Keith R. Yamamoto, "Precision Medicine"CTSI at UCSF
Keith R. Yamamoto, PhD — Opening Remarks – Precision Medicine
Vice Chancellor for Research
Executive Vice Dean of the School of Medicine
Professor of Cellular and Molecular Pharmacology
UCSF
1. The document discusses using heterogeneous biological data to advance scientific discovery by overcoming complexity.
2. It describes how new technologies now allow generation of massive human "omics" data and emerging network modeling approaches for diseases.
3. Integrating this data through cloud computing infrastructure can enable a generative open approach to solving biomedical problems.
1) The document discusses the Future Health department at KU Leuven which conducts research in health decision support for professionals, patients, and policymakers.
2) The department takes an interdisciplinary approach and focuses on using data mining, IT, and software design to extract appropriate information from clinical, biomedical, and other health data sources to provide decision support.
3) The goal is to enable better, more cost effective healthcare by providing personalized decision support that is evidence-based, user-centered, and looks ahead to future needs.
Talk entitled "from the Virtual Human to a Digital Me" presented at the Virtual Physiological Human 2012 Conference held at IET Savoy, Savoy Place, London, 18-20 September 2012.
This document discusses harnessing the power of teams and networks to build better models of disease in real time. It notes that new technologies now allow the generation of massive amounts of human omics data and emerging network modeling approaches for diseases. Cloud computing infrastructure allows a generative open approach to biomedical problem solving. A nascent movement aims to give patients more control over their sensitive health information to facilitate sharing. Open social media also allows experts and citizens to collaborate to solve biomedical problems. The overall opportunity is to conduct more open, collaborative biomedical research involving diverse teams.
This document discusses integrated health monitoring and precision medicine. It defines precision medicine as using big data, clinical, molecular, environmental, and behavioral information to understand disease and improve prevention and treatment outcomes for patients. Integrated health monitoring combines data from various sources like personal health records, sensors, genomics, and environmental exposures to develop a dynamic model of a patient's health over time. Health informatics plays a key role in building systems to integrate these diverse data sources and enable precision medicine approaches.
From Digitally Enabled Genomic Medicineto Personalized HealthcareLarry Smarr
The document discusses the future of personalized healthcare through digital health technologies and genomic medicine. It describes how continuous monitoring of various biological sensors can capture temporal data on factors like physical activity, diet, sleep, environmental exposures and more. This comprehensive data combined with clinical records, genetic information, and microbial metagenomic analysis can enable true preventative medicine through early detection, feedback loops, and tuning of lifestyle and medical factors.
Computing for Human Experience: Semantics empowered Cyber-Physical, Social an...Amit Sheth
Keynote at On the Move conference, October 2011, Greece.
Abstract:
Traditionally, we had to artificially simplify the complexity and richness of the real world to constrained computer models and languages for more efficient computation. Today, devices, sensors, human-in-the-loop participation and social interactions enable something more than a “human instructs machine” paradigm. Web as a system for information sharing is being replaced by pervasive computing with mobile, social, sensor and devices dominated interactions. Correspondingly, computing is moving from targeted tasks focused on improving efficiency and productivity to a vastly richer context that support events and situational awareness, and enrich human experiences encompassing recognition of rich sets of relationships, events and situational awareness with spatio-temporal-thematic elements, and socio-cultural-behavioral facets. Such progress positions us for what I call an emerging era of “computing for human experience” (CHE). Four of the key enablers of CHE are: (a) bridging the physical/digital (cyber) divide, (b) elevating levels of abstractions and utilizing vast background knowledge to enable integration of machine and human perception, (c) convert raw data and observations, ranging from sensors to social media, into understanding of events and situations that are meaningful to humans, and (d) doing all of the above at massive scale covering the Web and pervasive computing supported humanity. Semantic Web (conceptual models/ontologies and background knowledge, annotations, and reasoning) techniques and technologies play a central role in important tasks such as building context, integrating online and offline interactions, and help enhance human experience in their natural environment.
In this talk I will discuss early enablers of CHE including semantics-empowered social networking and sensor Web, and computation of higher level abstractions from raw and phenomenological data. An article in IEEE Internet Computing provides background information: http://bit.ly/HumanExperience
Keynote at: https://www.springer.com/us/book/9783642251054
Event Date: Oct 18, 2011
Crowdsourcing applied to knowledge management in translational research: the ...SC CTSI at USC and CHLA
Date: November 8th, 2018
Speaker: Andrew Su, PhD, Professor, Department of Integrative, Structural and Computational Biology, The Scripps Research Institute
Overview: Crowdsourcing involves the engagement of large communities of individuals to collaboratively accomplish tasks at massive scale. These tasks could be online or offline, paid or for free. But how can crowdsourcing science help your research? This webinar will describe two crowdsourcing projects for translational research, both of which aim to better organize biomedical information so that it can be more easily accessed, integrated, and queried:
First, the goal of the Gene Wiki project is to create a community-maintained knowledge base of all relationships between biological entities, including genes, diseases, drugs, pathways, and variants. This project draws on the collective efforts of informatics researchers from a wide range of disciplines, including bioinformatics, cheminformatics, and medical informatics.
Second, the Mark2Cure project partners with the citizen scientist community to extract structured content from biomedical abstracts with an emphasis on rare disease. Although citizen scientists do not have any specialized expertise, after receiving proper training, Mark2Cure has shown that in aggregate they perform bio-curation at an accuracy comparable to professional scientists.
This document provides an overview of a teacher guide for an activity on the bioethics of gene therapy. The activity uses three case studies of past gene therapy trials to explore ethical issues: a successful case, an unsuccessful case, and a mixed case. Students use an ethical decision-making model to examine each case from the perspective of stakeholders like patients, researchers, and companies. The goal is for students to consider ethical questions about risks, outcomes, and whether experimental gene therapy trials should continue given their challenges.
There are only around 500 geneticists and 2,400 genetic counselors in the U.S. to help integrate genomic medicine into patient care. DNA Direct aims to address this shortage and other barriers through technology solutions that provide education, decision support, and expert guidance to patients, providers, payors, and medical centers. Their programs have shown success in improving patient compliance with genetic screening and understanding of test results.
Home health care & long-term conditions: How to succeed with personal health ...Mohammad Al-Ubaydli
Dr Mohammad Al-Ubaydli (CEO of Patients Know Best)
Dr Al-Ubaydli is author of the book "Personal health records: A guide for clinicians", in which he surveyed the different ways in which patients can work with their clinical team using software. A new generation of tools allows patients to manage their health and Mohammad will cover some of these in his talk, including products by large US companies like Google and Microsoft, as well as the UK software industry. He will also describe the experiences of his own company, Patients Know Best, which integrates its patient-controlled medical records platform into the NHS secure network.
Towards Digitally Enabled Genomic Medicine: the Patient of The FutureLarry Smarr
12.02.22
Invited Speaker
Hacking Life
TTI/Vanguard Conference
Title: Towards Digitally Enabled Genomic Medicine: the Patient of The Future
San Jose, CA
CHIC is a nonprofit collaborative in Northeastern Minnesota that provides regional access to health care information through technology and partnerships. Its mission is to help members improve care and save costs. CHIC programs include emergency preparedness coordination, a health information exchange called HIE-Bridge that allows quick access to patient records, an immunization registry, and helping providers apply for telehealth funding. CHIC aims to build bridges to quality health care through these collaborative programs.
(1) The system segments histopathology images into epithelial and stromal regions and identifies nuclei.
(2) It constructs a rich set of quantitative features describing the relationships between different image objects.
(3) Using the features, a predictive model is built from images of patients with known 5-year survival outcomes. This model can then predict survival probabilities for new unlabeled images.
The document discusses the use of computers and technology in medicine. It covers topics like telemedicine, different medical imaging technologies like MRI, CT scans, mammography and ultrasound. It also discusses the importance of telemedicine in increasing access to healthcare and reducing costs, as well as some issues regarding telemedicine like the need for appropriate infrastructure and privacy concerns.
The document discusses the transition from personalized medicine to personal health. It notes the current challenges in medicine including the need for earlier diagnosis, more personalized therapies, and improved disease classification. Personalized medicine uses an individual's genetic and molecular profile to guide risk assessment and treatment. However, personal health empowers patients by providing them access to their own health data through technologies like sensors, apps, and personal health records. This allows patients to better monitor and manage their health. Realizing personal health will require overcoming challenges regarding privacy, security, and ensuring equitable access to technologies and data interpretation.
The rise of the 'ePatient': how it is affecting clinical practice and research
The document discusses how engaged patients, or "ePatients", who actively gather their own health data and conduct their own research are affecting clinical practice and research. It describes how ePatients are empowered through personal health records, diagnostic testing, genomic data, and self-monitoring devices. This shift towards participatory health challenges traditional clinician-led models and will require changes in areas like privacy, education, and how data is integrated into care.
The document discusses integrating genomics data and evidence-based medicine into electronic health records (EHRs) for precision healthcare. It notes the gap between what is known and what is done in healthcare. Integrating genomics could help do the right thing for each patient through pharmacogenomics. However, challenges include representing huge volumes of molecular data in a usable way in EHRs. A three step approach is proposed: 1) get genomic data into EHRs in a structured format, 2) use that data for clinical decision support, 3) evaluate outcomes and continually improve the system.
Presentation given by Prof Fernando J Martin-Sanchez at the HISA (Health Informatics Society Australia) event "A Leap into E-Health" - see http://www.hisa.org.au/events/event_details.asp?id=211738 for further details - on 29th February 2012.
UCSF Informatics Day 2014 - Keith R. Yamamoto, "Precision Medicine"CTSI at UCSF
Keith R. Yamamoto, PhD — Opening Remarks – Precision Medicine
Vice Chancellor for Research
Executive Vice Dean of the School of Medicine
Professor of Cellular and Molecular Pharmacology
UCSF
1. The document discusses using heterogeneous biological data to advance scientific discovery by overcoming complexity.
2. It describes how new technologies now allow generation of massive human "omics" data and emerging network modeling approaches for diseases.
3. Integrating this data through cloud computing infrastructure can enable a generative open approach to solving biomedical problems.
1) The document discusses the Future Health department at KU Leuven which conducts research in health decision support for professionals, patients, and policymakers.
2) The department takes an interdisciplinary approach and focuses on using data mining, IT, and software design to extract appropriate information from clinical, biomedical, and other health data sources to provide decision support.
3) The goal is to enable better, more cost effective healthcare by providing personalized decision support that is evidence-based, user-centered, and looks ahead to future needs.
Talk entitled "from the Virtual Human to a Digital Me" presented at the Virtual Physiological Human 2012 Conference held at IET Savoy, Savoy Place, London, 18-20 September 2012.
This document discusses harnessing the power of teams and networks to build better models of disease in real time. It notes that new technologies now allow the generation of massive amounts of human omics data and emerging network modeling approaches for diseases. Cloud computing infrastructure allows a generative open approach to biomedical problem solving. A nascent movement aims to give patients more control over their sensitive health information to facilitate sharing. Open social media also allows experts and citizens to collaborate to solve biomedical problems. The overall opportunity is to conduct more open, collaborative biomedical research involving diverse teams.
This document discusses integrated health monitoring and precision medicine. It defines precision medicine as using big data, clinical, molecular, environmental, and behavioral information to understand disease and improve prevention and treatment outcomes for patients. Integrated health monitoring combines data from various sources like personal health records, sensors, genomics, and environmental exposures to develop a dynamic model of a patient's health over time. Health informatics plays a key role in building systems to integrate these diverse data sources and enable precision medicine approaches.
Presentation given at Health Informatics and Knowledge Management conference
(http://publichealth.curtin.edu.au/HIKM/), as part of Australasian Computer Science Week 2012.
http://www.cs.rmit.edu.au/acsw2012/
The Personalized Health Risk Profile: A New Tool for Safety and Occupational ...Richard Hartman, Ph.D.
The presentation introduces the concept of a Personalized Health Risk Profile (PHRP) as a new tool for occupational health and safety professionals. A PHRP would integrate data from workplace exposures, lifestyle factors, medical history, genetics, and sensors to calculate individualized health risk indices. This would allow identifying risks at a molecular level before disease onset and enabling targeted interventions. The presentation provides an example application assessing noise-induced hearing loss risk and argues PHRPs could help rethink exposure limits, worker groupings, and demonstrate cost savings through improved prevention of illness and injury.
Panel: FROM SMALL TO BIG TO RICH DATA: Dealing with new sources of data in Biomedicine Precision and Participatory Medicine
Fernando J. Martin-Sanchez, Professor and Chair of Health Informatics at Melbourne Medical School, discusses new sources of data in biomedicine including small, big, and rich data. He describes how small data connects people with meaningful insights from big data to be understandable for everyday tasks. Martin-Sanchez also discusses precision medicine, participatory health, and how convergence between the two can help integrate multiple data sources including genomics, the exposome, and digital health to improve disease prevention and treatment outcomes.
The document discusses the Human Genome Project, an international research effort begun in 1990 with the goals of mapping all of the genes in human DNA and determining the sequences of chemical base pairs that make up human DNA. It aimed to identify the approximately 30,000 genes in humans, determine the sequences of the 3 billion base pairs of human DNA, and address ethical issues. The project was originally expected to last 15 years but was completed earlier than expected, in 2003, due to rapid technological advances.
Digital technologies like wireless sensors, genomics, EHRs, mobile apps, and big data analytics can significantly help patients but cannot replace human compassion and advocacy. These technologies can improve patient engagement, access to information, and personalized care. However, the most effective patient advocates will still be human beings who can combine technology tools with qualities like empathy, communication skills, and devotion of time to help patients navigate the healthcare system.
Predictive Analytics and Machine Learning for Healthcare - DiabetesDr Purnendu Sekhar Das
Machine Learning on clinical datasets to predict the risk of chronic disease conditions like Type 2 Diabetes mellitus beforehand; as well as predicting outcomes like hospital readmission using EMR RWE data.
This document summarizes a presentation by Melanie Swan on personalized medicine and DIYgenomics. DIYgenomics conducts crowdsourced health studies that combine genetic data, health metrics, and lifestyle interventions. Their goal is predictive health profiling and personalized prevention. Studies examine relationships between genotypes, phenotypes, and outcomes. Data streams include genomes, sensors, profiles and are integrated for personalized insights. DIYgenomics operates open studies in areas like sleep, empathy, microbiome and diabesity prevention. The model aims to realize preventive healthcare through continuous self-tracking, peer collaboration and crowdsourced research.
Orange Healthcare provides communication solutions to help modernize healthcare infrastructure and connect medical professionals and patients. Personalized medicine is evolving from a focus on cancer to utilizing new technologies like big data, enabling access to information, and remote monitoring. In the future, DNA sequencing will cost $100, predictive medicine will be available for everyone, and patients will generate their own health data through mobile devices and the medical cloud. However, personalized medicine will require moving past the myths of the omniscient physician and patient-led healthcare revolution, and ensuring healthcare professionals can properly monitor and interpret the large amounts of data being collected.
The document discusses a conference on the role of telehealth in managing rural neuroemergencies. It provides context on the healthcare environment including reforms, technology advances, and challenges in access to care. Telehealth and health information technology are presented as part of the solution to close gaps in rural healthcare access through clinical consultations, education, and health information exchange. Examples of telehealth applications described include telestroke assessments, maternal-fetal medicine, teleradiology, and remote patient monitoring.
Similar to Personalised and Participatory Medicine Workshop15 may 2012 (20)
This document outlines a presentation on digital medicine and new challenges for health informatics. It discusses how digital technologies are converging with medicine and impacting patients through wearables, apps, direct-to-consumer services, and social networks. Precision medicine and participatory health are highlighted as key research areas. The role of biomedical informatics is examined in relation to social media, self-quantification, and exposome informatics. Research being conducted at HaBIC and potential frameworks for understanding quantified self data and its therapeutic benefits are summarized.
This document summarizes a presentation on new sources of big data for precision medicine. It discusses how new data sources like genomics, the human microbiome, epigenomics, and the exposome are generating large amounts of data. It then covers the evolution of precision medicine from concepts like personalized medicine and how strategic initiatives in the UK and US are supporting precision medicine research through funding programs and projects like the Cancer Genome Atlas, eMERGE, and exposome studies. The presentation raises the question of whether we are ready for precision medicine given these new data sources and research efforts.
The Health and Biomedical Informatics Centre (HaBIC) conducts activities in education, translational research informatics, e-health and participatory health research, informatics for precision medicine research, and engagement. Key activities include developing education strategies in health and biomedical informatics, providing expertise and tools to support health data collection and management for research, conducting e-health and participatory health research on topics like telehealth and self-quantification, facilitating precision medicine through genomic and clinical data integration, and engaging with partners in biomedical research institutes, hospitals, and universities.
1) The document discusses self-quantification systems and big data prospects and challenges from these systems. It describes the quantified self movement and tools people use to self-monitor health metrics and experiences.
2) Various types of self-monitoring devices, sensors, and services are presented. Challenges with self-quantification include privacy, security, education, and ensuring data is used for health improvement rather than risk profiling.
3) Opportunities include using self-tracking data to prevent disease, shift care from tertiary to primary settings, and generating data to further research when shared. Standards are needed for integrating self-data with electronic health records.
This document provides an overview of nursing informatics (NI). It defines NI as the science and practice that integrates nursing, information and knowledge, with management of information and communication technologies to promote health. The document discusses the evolution of NI as a clinical specialty since the 1970s. It also outlines some of the core skills and roles in NI, including systems analysis, project management, education, research, and technology development.
This document provides an overview of a new postgraduate elective subject on eHealth and Biomedical Informatics Systems. The subject introduces current approaches and future directions in eHealth and healthcare informatics. Topics covered include electronic health records, health portals, telehealth, and privacy/security standards. The subject aims to help students critically evaluate new health technologies, synthesize technical and social factors in informatics projects, and assess competency needs. Assessment consists of a knowledge test, critical appraisal assignment, and project report with presentation. The subject provides foundational knowledge in biomedicine, computing, and information science relevant to the field of health informatics.
The Master of Information Technology Specialisation in Health is a 1-2 year program that trains students in health IT to address the growing demand and shortage of skilled professionals. The program provides a foundation in core IT subjects as well as specialised subjects in areas like eHealth, biomedical informatics, and health data. Students learn from experts in IT and health and have opportunities for industry projects and placements in the biomedical precinct at the University of Melbourne.
This document outlines different areas of informatics and their scopes and methods. It shows that population health informatics focuses on entire populations and public health, while clinical informatics focuses on healthcare delivery and individual patients. Basic research informatics examines areas like bioinformatics, image informatics, and nanoinformatics at a fundamental level, while clinical research informatics applies these areas to research.
Health and biomedical informatics is the study of how information is acquired, stored, retrieved, and used for human health and involves the design of information systems to advance healthcare. The University of Melbourne's Health and Biomedical Informatics Research Unit conducts multidisciplinary research and teaching in this field from their location in the city's healthcare and research precinct. The Unit collaborates widely and welcomes involvement in their activities through research projects, courses, employment, and partnerships to advance human health.
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Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
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Personalised and Participatory Medicine Workshop15 may 2012
1. Personalised
and
Par,cipatory
Medicine
Workshop,
15
May
2012
Fernando
Mar*n-‐Sanchez
Ins$tute
for
a
Broadband-‐Enabled
Society
&
Melbourne
Medical
School
2. Introduc$on
• Broadband
can
provide
many
opportuni$es
for
the
health
sector:
– Improving
youth
mental
health
and
aged
care
services
– Monitoring
health
condi$ons
– Enabling
shared
electronic
health
records
– Telehealth
• Convergence
with
other
technologies
towards
Digitally
Enabled
Personalized
and
Par$cipatory
Medicine
3. Aging
Well
– Mobile
and
broadband
technologies
for
ameliora,ng
social
isola,on
in
older
people
– Smart
Homes
for
the
Elderly
–
recent
developments
in
Korea
Youth
Mental
Health
− HORYZONS:
Online
Recovery
for
Youth
Onset
Psychosis
4. Telehealth
– Individual
Electronic
Health
Records
– The
Telestroke
Study
– Hap*c
Tele-‐Rehabilita/on
– Teleden/stry
– Virtual
visits:
Inves*ga*ng
the
acceptability
of
webcam
consulta*ons
for
young
adults’
sexual
health
– Wireless
broadband
monitoring
of
knee
osteoarthri/s
– Overcoming
geographical
barriers
for
community
health
– Interpreter
mediated
cogni*ve
assessments
using
video
conferencing
soFware
– SeeCare
IPTV:
Personalised
Health
Literacy
Demonstrator
– Mobile
Augmented
Reality
– Interpreter
mediated
cogni/ve
assessments
using
video
conferencing
soFware
– High
resolu*on
monitoring
of
atmospheric
pollutants
to
iden*fy
their
impact
on
popula*on
health
– Overcoming
geographical
barriers
for
community
health
– Using
video-‐conferencing
to
pilot
an
educa*on
and
clinical
support
package
for
rural
GPs
in
Mildura
5. Current
challenges
in
Medicine
• Need
of
earlier
diagnosis
• More
personalized
therapies
• Clinical
trials
and
the
development
of
new
drugs
need
to
be
faster
and
more
effec$ve
• Improve
disease
classifica$on
systems
• Risk
profiling,
disease
predic$on
and
preven$on
• Control
health
system
costs
• Ci$zens
should
take
more
responsibility
for
the
maintenance
of
their
own
health.
6. The Digitalization of Medicine
• Digital
revolu$on
in
other
domains
(banking,
insurance,
leisure,
government,…)
• The
incorpora$on
of
digital
systems
in
healthcare
is
lagging
behind
other
sectors:
– Reasons:
complexity,
privacy,
volume
of
data,
lack
of
demand
– It
has
greatly
affected
healthcare
at
the
hospital
or
research
centre
level.
– The
digital
revolu$on
has
not
yet
reached
medicine,
at
the
pa$ent/ci$zen
level
• BUT
THIS
IS
STARTING
TO
HAPPEN
NOW
!!!
7. Vision
• The
convergence
of
medicine
and
the
digital
revolu$on
will
produce
an
informa,on
ecosystem
that
will
facilitate
the
advent
of
safer
and
more
efficient
preven$ve,
diagnos$c
and
therapeu$c
solu$ons.
• The
ci$zen
will
have
access
to
her
gene,c
profile
and
clinical
record,
and
will
monitor
and
adjust
her
health
using
next
genera$on
sensors
and
social
networks
to
share
this
informa$on
with
peers,
clinicians
and
researchers.
8. High-‐capacity
Broadband
technologies
and
networks
• The
availability
of
ultra-‐high-‐speed,
high-‐capacity,
ubiquitous,
‘always-‐on’
broadband
connec$vity
will
contribute
to
the
development
of
an
integrated
digital
infrastructure
for
medicine,
reaching
the
ci,zen,
that
will
make
feasible
the
concepts
of
personalized
medicine
and
par$cipatory
health.
• Ultra
high
speed
broadband
networks
will
be
required
to
transmit
enormous
volumes
of
data
from
pa$ents’
homes
to
health
prac$$oners
and
vice
versa
in
a
$mely
manner,
and
to
enable
the
processing
of
this
deluge
of
data.
9. Collecting genome data
• Benchtop
Ion
Proton™
Sequencer
–
designed
to
sequence
the
en$re
human
genome
in
a
day
for
$1,000
14. Patient Data (sensors and imaging)
Sensors
Genomic Phenomic Environmental
Integrated Personal
EHR Health Record
Module 1 Health Profile GWAS
Assessment
Tables (weighted factors)
Modelling Risks
Diagnosis Personal
Health Profile
CDSS
Health Profile
Module 2 Improvement Trialbanks
Networks
Risk reduction Decision matrix, protocols
Follow-up Personalised
Therapy Health Recommendations
16. Defini$on
• Personalized
medicine
uses
an
individual's
gene*c
(and
molecular)
profile
and
individual
informa*on
about
environmental
exposures
to
guide
decisions
made
in
regard
to
(risk
profiling)
and
the
preven*on,
diagnosis,
and
treatment
of
disease.
(Adapted
from
F.
Collins,
Director
NIH)
17. Clinical
applica$ons
of
genomic
informa$on
• Pharmacogene$cs
–
Personalized
Medicine
Coali$on
-‐
72
drugs
in
2011
• Cys$c
fibrosis
–
successful
clinical
trial
for
a
specific
muta$on
• Iden$fica$on
of
metabolic
diseases
19. Self-‐genomics
-‐
Clinical
annota$on
of
individual
genomes
• Prof.
Quake
-‐
Stanford
-‐
-‐
Nature
gene$cs
paper
-‐
$50.000,
1
week,
Helicos.
Stanford
team
-‐
• Clinical annotation of genome from
“patient Zero”
– Drug
metabolism
– Rare
gene$c
variants
-‐
rare
diseases
– Common
gene$c
variants
-‐
Risk
of
complex
diseases
Ashley et al. The Lancet, Volume 375, Issue 9725, Pages 1525 - 1535, 1 May 2010
20. First
personal
longitudinal
OMICS
profiling
exercise
• Combined
analysis
of
genomic,
transcriptomic,
proteomic,
metabolomic
and
immunological
profiles
from
a
single
individual
(one
of
the
authors-‐
Prof.
Michael
Snyder),
over
a
14
month
period.
More
than
3
billion
measurements.
• He
contracted
two
mild
viral
infec$ons
in
the
data-‐gathering
period,
which
lem
their
molecular
signature
in
the
analyses.
• During
one
of
these
infec$ons,
his
blood
glucose
levels
began
to
approach
those
of
a
diabetes
sufferer.
Amer
changing
his
diet
and
exercise
habits,
glucose
level
returned
to
normal.
• This
study
shows
that
diseases
are
a
product
of
an
individual’s
gene$c
profile
as
well
as
interac$on
with
the
environment
and
that
disease
can
be
treated
based
on
molecular
informa$on.
(Chen
et
al,
Cell
148,
1293-‐1307
March
16
2012
)
22. Par$cipatory
Health
•
• From Web 1.0 – Use of internet to find health information to Web 2.0 –
web-based communities and services. NHS Social Care Model (NHS)
• A survey of 1,060 U.S. adults by the PwC Health Research Institute found
that a third of respondents are gravitating toward social media as a place
for discussions of health care.
• Pew Internet study – 27% of US internet users had tracked health data
online
• Care management, disease management, supported self-care, promoting
better health à Patients empowered, informed and involved in decision
making, prevention and learning
23. Par$cipatory
Health
self tracking devices
Social web
games
Participatory Health
mobile Internet of things
sensors PCEHR
24. PCEHR
• Quality = patients reviewing their own records - Shared
Medical Records
• MyHealth@Vanderbilt – information on prescriptions is
shared. Knowledge management team – consumers will have
convenient e-access to their medical records and genetic
profiles to social media & games
• Facebook
• Lifeline – support line for suicide
• Organ donor status
• Blood type – app will contact the user
26. Social
media
as
a
research
tool
• We
are
witnessing
a
transi$on
from
research
informa$on
systems
centralized
at
hospitals
and
clinical
research
centres
to
distributed
systems
that
reach
out
to
the
residence
of
any
ci$zen
/
pa$ent
who
opts
in.
• Clinical
Research
with
the
pa$ents,
not
on
the
pa$ents
• Examples
– 23andMe
–
Parkinson’s
Disease
–
PLoS
Gene$cs,
2
new
gene$c
associa$ons
– Pa$entsLikeMe
–
Nature
Biotech.
Self-‐reported
data
from
600
pa$ents
on
the
use
of
lithium
for
Amyotrophic
Lateral
Sclerosis
(ALS)
27. Crowdsourced
clinical
trials
• DIY science, Crowdsourced Health Research Studies,
Citizen science, Amateur Scientist, Self-
Experimentation
• Patients Like Me – 125.000 members. 1000 condition-
based communities –25 Papers published in PNAS, Nat
Biotech, JMIR, …
• 23andme – 23 and we –
• Acor, RevolutionHealth, Curetogether, Genomera,
Althea Health
28. • Self
tracking
/
self
quan$fying
/
self
monitoring
• The
belief
that
gathering
and
analysing
data
can
help
them
improve
their
lives!
• QS’ers
doubling
every
year.–
5524
members,
42
meetup
groups
• Larry
Smarr–
10years
quan$fying
his
body
– Weight
–
physical
ac$vity:
calories
burnt
(body
media)
–
Food
intake
–
Sleep
(Zeo)
–
blood
chemicals
(60
Markers)
–
cholesterol/triglycerides
/
Apo
B
/
Ω
–
6,
Ω
–
3/
C-‐reac$ve
protein
-‐
Ultrasound
–
(plaque
in
arteries)
–
stool
test
–
colonoscopy
–
DNA
–
Microbiome
• Fitbit
–
Sleep
–
Movement
• +9000
health
apps,
each
person
connected
to
140
devices,
9
billion
of
connected
devices
now,
24
billion
by
2020
• NODE
Sensor
Environment
29. Pa$ent
empowerment
Current NBN-enabled Driving forces: patient empowerment,
networks personalized medicine, social networks
EHR – Personally Citizens are able to maintain and control
Electronic Controlled EHR their own health information
Health Record
Gene-disease Personal Citizens ask for genetic analysis of their
association genomics DNA through the Internet and receive
studies reports on various aspects of their health
Clinical trials Crowdsourced The patient voluntarily shares information
clinical trials on treatments and evolution of his/her
illness with other patients
30. Barriers
• New
regulatory
framework
(new
models
of
clinical
trials)
• New
informa$cs
methods
to
compile
and
interpret
all
the
informa$on
• Educa$on
of
pa$ents
and
health
professionals
• Ethics,
data
security
and
confiden$ality
issues
• Wide
availability
of
clinical
decision
support
systems
at
the
point-‐of-‐care
• New
cost-‐effec$veness
assessment
and
financial
models
of
care
• Need
to
prove
clinical
effec$veness
before
DTC
services
are
offered.
31.
32. Thank
you
sms@unimelb.edu.au
www.healthinforma$cs.unimelb.edu.au
Twiuer:
@ibeshbir