Mobilizing Mhealth Innovation for Real-World Evidence Generation
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Public Health Informatics Profile Toolkit
Public Health Informatics Profile Toolkit Developing a Public Health Informatics Profile: A Toolkit for State and Local Health Departments to Assess their Informatics Capacity Developed By: The Minnesota Department of Health Supported by: The Public Health Informatics Institute & The Robert Wood Johnson Foundation Public Health Informatics Profile Toolkit Acknowledgements Developing a Public Health Informatics Profile: a Toolkit for State and Local Health Departments to Assess their Informatics Capacity The project was supported by the Robert Wood Johnson Foundation through an InformationLinks Grant to the Public Health Informatics Institute. The initial project (the Public Health Informatics Profile Assessment) was also funded by Robert Wood Johnson Foundation through their Common Ground grant program. The authors of the Public Health Informatics Toolkit wish to thank the many members of the Minnesota Department of Health for their time, their expertise, and for their contributions to the original Public Health Informatics Profile Assessment. Jennifer Ellsworth Fritz, Priya Rajamani, Martin LaVenture Minnesota Department of Health Principal Developers Bill Brand, Debra Robic Public Health Informatics Institute Editorial Reviewers For More Information Public Health Informatics Institute Visit www.phii.org Call toll-free (866)815-9704 E-mail [email protected] Minnesota Department of Health Visit www.health.state.mn.us/ehealth Call Priya Rajamani @ 651-201-4119 E-mail [email protected] Call Jennifer Ellsworth Fritz @ 651-201-3662 E-mail -
Benefits and Barriers for Adoption of Personal Health Records Brittany Vance Marshall University, [email protected]
Marshall University Marshall Digital Scholar Management Faculty Research Management, Marketing and MIS Spring 3-2015 Benefits and Barriers for Adoption of Personal Health Records Brittany Vance Marshall University, [email protected] Brent Tomblin Marshall University, [email protected] Jena Studney Marshall University Alberto Coustasse Marshall University, [email protected] Follow this and additional works at: http://mds.marshall.edu/mgmt_faculty Part of the Health and Medical Administration Commons, Health Information Technology Commons, and the Management Information Systems Commons Recommended Citation Vance, B., Tomblin, B., Studeny, J., & Coustasse A., (2015, March). Benefits nda barriers for adoption of personal health records. Paper presented at the 2015 Business and Health Administration Association Annual Conference, at the 51st Annual Midwest Business Administration Association International Conference, Chicago, IL. This Article is brought to you for free and open access by the Management, Marketing and MIS at Marshall Digital Scholar. It has been accepted for inclusion in Management Faculty Research by an authorized administrator of Marshall Digital Scholar. For more information, please contact [email protected]. BENEFITS AND BARRIERS FOR ADOPTION OF PERSONAL HEALTH RECORDS Brittany Vance, MS Alumni College of Business Marshall University Graduate College 100 Angus E. Peyton Drive South Charleston, WV 25303 Brent Tomblin, MS Alumni College of Business Marshall University Graduate College 100 Angus E. Peyton Drive South Charleston, WV 25303 Jana Studeny, RN-BC, MSHI, Alumni Healthcare Informatics Program College of Health Professions Marshall University One John Marshall Drive Huntington, WV 25755 [email protected] Alberto Coustasse, DrPH, MD, MBA, MPH – CONTACT AUTHOR Associate Professor College of Business Marshall University Graduate College 100 Angus E. -
Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril Michael Matheny, Sonoo Thadaney Israni, Mahnoor Ahmed, and Danielle Whicher, Editors WASHINGTON, DC NAM.EDU PREPUBLICATION COPY - Uncorrected Proofs NATIONAL ACADEMY OF MEDICINE • 500 Fifth Street, NW • WASHINGTON, DC 20001 NOTICE: This publication has undergone peer review according to procedures established by the National Academy of Medicine (NAM). Publication by the NAM worthy of public attention, but does not constitute endorsement of conclusions and recommendationssignifies that it is the by productthe NAM. of The a carefully views presented considered in processthis publication and is a contributionare those of individual contributors and do not represent formal consensus positions of the authors’ organizations; the NAM; or the National Academies of Sciences, Engineering, and Medicine. Library of Congress Cataloging-in-Publication Data to Come Copyright 2019 by the National Academy of Sciences. All rights reserved. Printed in the United States of America. Suggested citation: Matheny, M., S. Thadaney Israni, M. Ahmed, and D. Whicher, Editors. 2019. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. NAM Special Publication. Washington, DC: National Academy of Medicine. PREPUBLICATION COPY - Uncorrected Proofs “Knowing is not enough; we must apply. Willing is not enough; we must do.” --GOETHE PREPUBLICATION COPY - Uncorrected Proofs ABOUT THE NATIONAL ACADEMY OF MEDICINE The National Academy of Medicine is one of three Academies constituting the Nation- al Academies of Sciences, Engineering, and Medicine (the National Academies). The Na- tional Academies provide independent, objective analysis and advice to the nation and conduct other activities to solve complex problems and inform public policy decisions. -
Health Data As a Global Public Good
Health Data Governance Summit Pre-read: Health Data as a Global Public Good 30 June 2021 Global public goods Health Data Governance Summit Pre-read: Health Data as a Global Public Good Health data as a global public good The ODI has been working with WHO for the past month, holding discussions with 23 stakeholders and analysing 56 documents "Starkly and powerfully, the COVID-19 pandemic illustrates how critical "Despite progress in recent years, high-quality data are not routinely data use, with a human face, is to protecting lives & livelihoods. The collected in all settings, major health challenges are not adequately crisis is a wake-up call. We must accelerate a shift in our data and monitored, and effective interventions are not directed to the right analytics abilities: To respond to COVID-19 and build back better, to people, at the right time and at the right place. This impacts policies drive the Decade of Action for the SDGs, to amplify climate action, to and programmes and consequently, the health of entire populations. promote gender equality, to protect human rights, to advance peace Similarly, in order to meet the shared SDG commitment to “leave no-one and security, and to accelerate UN Reform – for greater impact on the behind”, we need disaggregated data to ensure equitable health ground." outcomes. This means we must strengthen comprehensive data systems, UN Secretary-General collaborate with other sectors, and apply innovative digital technologies to collect, analyse and use data to make informed decisions and deliver impact." WHO Director-General 2 Global public goods Health Data Governance Summit Pre-read: Health Data as a Global Public Good What are global public goods? "Global public goods are goods… whose benefits cross borders and are global in scope." - WHO Bulletin 2003 In traditional economic terms, public goods are have two key attributes: "Global public goods (GPGs) provide benefits to people in both rich and ● They are non-exclusionary: No one can be excluded from using poor countries. -
Big Data in Health Care
A Series by 2016 From predicting disease and identifying targeted therapies and cures, to improving our overall quality of life, big data is transforming the way health care decisions are made and care is delivered. Big Data in Health Care is a recently published series of articles that brings you the stories behind the research and celebrates researchers and organizations for their commitment to improving health care. Please accept this complimentary copy as our way of thanking you for your commitment to advancing medicine and improving patients’ lives. founding sponsor co-sponsor www.RealWorldHealthCare.org CONTENTS Big Data in Health Care Is Big Data Good for our Health? You Bet. Here’s Why. 3 Speaking with Dr. Phillip Bourne, National Institutes of Health. 8 Speaking with Dr. Hallie Prescott . 11 Closing the Healthcare Gap: The Critical Role of Non-Identified Information . 14 Real World Health Care Interview with Dr. Bonnie Westra. 17 Big Data Declares a War on Cancer . 21 Speaking with Dr. Clifford Hudis. 25 Is Big Data Good for our Health? You Bet. Here’s Why. By Cameron Warren and Merav Yuravlivker The term “Big Data” is increasingly used in our everyday lives. But each mention of it means something different, unique to what we use it for and how we interact with it. Big Data is not information. It’s the raw resource that people can use to discover new insights. Just as raw crude needs to be refined to run a car, Big Data needs to be refined to provide useful insights. In 2001, Doug Laney, who currently works for the analyst firm Gartner, defined this raw resource in terms of its three ubiquitous attributes, “the 3 V’s” – Volume, Velocity, and Variety. -
Artificial Intelligence: the Key to Unlocking Novel Real-World Data?
Artificial Intelligence: The Key to Unlocking Novel Real-World Data? While Artificial intelligence stands to make significant contributions to clinical research due to its unparalleled ability to translate unstructured data into real-world evidence (RWE), significant challenges remain in achieving regulatory-grade evidence. By Michele Cleary 16 | March/April 2019 Value & Outcomes Spotlight Artificial intelligence (AI) is revolutionizing healthcare services. From improving disease detection to supporting treatment decision making, AI has become ubiquitous in care delivery. Now AI is poised to transform the drug and device development process, helping researchers refine the approval process and significantly cutting both the time and the expense needed to bring products to market. While AI has long been used to facilitate recruitment of study subjects, optimize study design, and support patient adherence to study protocols, AI’s greatest contribution to clinical research may still be on the horizon— unlocking the data richness that lies within the mountains of novel real- world data (RWD) sources. This article explores how AI may improve clinical research through its ability to better translate RWD into real-world evidence (RWE), thus providing more valid evidence of clinical benefits and risks. Dan Riskin, MD, of Verantos, Rich Glinklich, MD of OM1, and Sebastian Schneeweiss, MD of Aetion all shared their valuable insights into how AI is transforming clinical research. THE SEARCH FOR REGULATORY-GRADE DATA With innovations in digital data, HEOR researchers are facing explosive growth in novel RWD sources. But as researchers move from traditional RWD sources (eg, registries and claims data) to these novel data sources, unstructured data present a significant opportunity and challenge. -
Get the Facts About Telehealth
Get the Facts About Telehealth The COVID-19 pandemic has demonstrated that telehealth is a viable option for providing convenient, accessible and seamless care for patients. Myth #1: FACT: Data shows older patients are very comfortable with telehealth. Telehealth is In a survey conducted by Sutter Health, disease. More than 20% of Tera patients less feasible 52% of people aged 65 and older are aged 65 and above, allowing us reported having used telehealth during to quickly learn that telemedicine is for senior the pandemic and 93% of these patients welcome across any age range, disease citizens. reported having a positive experience. state and socioeconomic group. The key In addition, Sutter’s Tera Practice, factor for acceptance came down to a virtual-first medical practice that individuals experiencing firsthand Tera’s offers a whole ecosystem of healthcare convenience and responsiveness, and the support, has purposefully enrolled rapport they were able to build with their seniors who have at least one chronic personal care team virtually. OF SENIORS WHO USED TELEHEALTH 93% REPORTED A POSITIVE EXPERIENCE FACT: While more work needs to be done, Myth #2: telehealth is already improving critical access to care in rural and underserved communities. Telehealth Additional investments in technology, broadband and access are will amplify necessary to prevent the deepening of inequities and ensuring health widespread availability. Providers and policymakers must continue to work together to ensure the benefits of virtual care extend to our inequities. most vulnerable patients and that no community is left behind. Sutter serves millions of Medi-Cal patients in Northern California so this is a priority for our system. -
Using Real World Data in Pharmacoeconomic Evaluations
Using Real World Data in Pharmacoeconomic Evaluations: Challenges, Opportunities, and Approaches Andreas M. Pleil, PhD Senior Director Worldwide Medical and Outcomes Research Pfizer, Inc., La Jolla, CA Slide:2 Disclaimer and Acknowledgements The opinions expressed by the presenter are his and may not reflect the opinions or position of Pfizer, Inc., it’s Board or Management Many thanks to Lou Garrison for allowing me to steal many of these slides shamelessly Slide:3 Today’s Agenda Overview of the Landscape Where do data come from Report from the Task Force What was their charge Who did the charging What did they conclude Discussion points Is the RWTF sufficient Are there gaps in our knowledge What should we do next Applications from your world Group Project Slide:4 Where do “Data” Come From? Pre-clinical studies Provides a first assessment of the expected safety and efficacy of a compound using proven animal models Early Phase Clinical trials IND Safety focus and the beginnings of efficacy, dose ranging, and tolerability Pivotal Clinical trials Demonstrate safety and efficacy in well controlled (generally masked) randomized studies sufficient for market authorization NDA Filed Phase IIIB Expanded trials in different use situations NDA Approved or populations Phase IV Post marketing safety or “new” indications Real World Data Evaluations of safety, effectiveness and outcomes in “routine” clinical practice Slide:5 Who uses “Data”? Companies making internal decision regarding drug development Regulators responsible -
Generating Real-World Evidence by Strengthening Real-World Data Sources
Generating Real-World Evidence by Strengthening Real-World Data Sources Using “real-world evidence” to bring new “Every day, health care professionals are updating patients’ treatments to patients as part of the 21st electronic health records with data on clinical outcomes Century Cures Act (the “Cures Act”) is a key resulting from medical interventions used in routine clinical priority for the Department of Health and practice. As our experience with new medical products Human Services (HHS). Specifically, the Cures expands, our knowledge about how to best maximize their Act places focus on the use of real-world data benefits and minimize potential risks sharpens with each data to support regulatory decision-making, including point we gather. Every clinical use of a product produces data the approval of new indications for existing that can help better inform us about its safety and efficacy.” drugs in order to make drug development faster jacqueline corrigan-curay, md, jd and more efficient. As such, the Cures Act director of the office of medical policy in fda’s center for drug evaluation and research has tasked the Food and Drug Administration (FDA) to develop a framework and guidance for evaluating real-world evidence in the context of drug regulation.1 Under the FDA’s framework, real-world evidence (RWE) is generated by different study designs or analyses, including but not limited to, randomized trials like large simple trials, pragmatic trials, and observational studies. RWE is the clinical evidence about the use, potential benefits, and potential risks of a medical product based on an analysis of real-world data (RWD). -
Perspectives on the Future of Personal Health Records
Perspectives on the Future of Personal Health Records June 2007 Perspectives on the Future of Personal Health Records Prepared for California HealthCare Foundation by Christopher J. Gearon Contributing Writers Michael Barrett, J.D. Patricia Flatley Brennan, R.N., Ph.D. David Kibbe, M.D., M.B.A. David Lansky, Ph.D. Jeremy Nobel, M.D., M.P.H. Daniel Sands, M.D., M.P.H. June 2007 About the Author Christopher J. Gearon is a freelance health and business writer in Silver Spring, Maryland. About the Foundation The California HealthCare Foundation, based in Oakland, is an independent philanthropy committed to improving California’s health care delivery and financing systems. Formed in 1996, our goal is to ensure that all Californians have access to affordable, quality health care. For more information about CHCF, visit us online at www.chcf.org. ISBN 1-933795-28-X ©2007 California HealthCare Foundation Contents 2 I Introduction 3 II. Background The PHR Market Business Models 6 III. Six Perspectives The Big-Picture Perspective The Consumer Perspective The Physician Perspective The Clinical Technology Perspective The Employer Perspective The Public Health Perspective 25 Endnotes I. Introduction The Internet and other information technologies have transformed American life in the last decade, empowering consumers and the way they work, bank, shop, and travel. However, a similar, long-anticipated transformation in health care has been elusive. Recent interest in a new kind of computerized medical record designed for consumers rather than health care providers could help speed this transformation. As a patient-centric hub of information and tools, personal health records (PHRs) have the potential to make the delivery of health care services more efficient and accessible, less costly, and safer. -
Security and Privacy for Mhealth and Uhealth Systems: a Systematic Mapping Study
Preprint submitted to IEEE Access. Date of current version June 22, 2020. Digital Object Identifier 10.1109/ACCESS.2017.DOI Security and Privacy for mHealth and uHealth Systems: a Systematic Mapping Study LEONARDO HORN IWAYA1,2, AAKASH AHMAD3, AND M. ALI BABAR.1,2 1Centre for Research on Engineering Software Technologies, The University of Adelaide, Adelaide, SA, Australia 2Cyber Security Cooperative Research Centre (CSCRC), Australia 3College of Computer Science and Engineering, University of Ha’il, Saudi Arabia Corresponding author: Leonardo Horn Iwaya (e-mail: [email protected]). The work has been supported by the Cyber Security Cooperative Research Centre (CSCRC) whose activities are partially funded by the Australian Government’s Cooperative Research Centres Programme. ABSTRACT An increased adoption of mobile health (mHealth) and ubiquitous health (uHealth) systems empower users with handheld devices and embedded sensors for a broad range of healthcare services. However, m/uHealth systems face significant challenges related to data security and privacy that must be addressed to increase the pervasiveness of such systems. This study aims to systematically identify, classify, compare, and evaluate state-of-the-art on security and privacy of m/uHealth systems. We conducted a systematic mapping study (SMS) based on 365 qualitatively selected studies to (i) classify the types, frequency, and demography of published research and (ii) synthesize and categorize research themes, (iii) recurring challenges, (iv) prominent solutions (i.e., research outcomes) and their (v) reported evaluations (i.e., practical validations). Results suggest that the existing research on security and privacy of m/uHealth systems primarily focuses on select group of control families (compliant with NIST800-53), protection of systems and information, access control, authentication, individual participation, and privacy authorisation. -
Assessment of the EU Member States' Rules on Health Data in the Light
DG Health and Food Safety Assessment of the EU Member States’ rules on health data in the light of GDPR Specific Contract No SC 2019 70 02 in the context of the Single Framework Contract Chafea/2018/Health/03 Health and Food Safety Further information on the Health and Food Safety Directorate-General is available on the internet at: http://ec.europa.eu/dgs/health_food-safety/index_en.htm The European Commission is not liable for any consequence stemming from the reuse of this publication. Luxembourg: Publications Office of the European Union, 2021 © European Union, 2021 Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). PDF ISBN 978-92-9478-785-9 doi:10.2818/546193 EB-01-21-045-EN-N EUROPEAN COMMISSION Assessment of the EU Member States’ rules on health data in the light of GDPR Specific Contract No SC 2019 70 02 in the context of the Single Framework Contract Chafea/2018/Health/03 Written by Johan Hansen1, Petra Wilson2, Eline Verhoeven1, Madelon Kroneman1, Mary Kirwan3, Robert Verheij1,4, Evert-Ben van Veen5 (on behalf of the EUHealthSupport consortium) 1 Nivel, Netherlands institute for health services research, 2 Health Connect Partners, 3 Royal College of Surgeons in Ireland, 4 Tilburg University, 5 MLC Foundation Contributors: Peter Achterberg, Jeroen Kusters, Laura Schackmann (main report), Isabelle Andoulsi, Petronille Bogaert, Herman van Oyen, Melissa Van Bossuyt, Beert Vanden Eynde, Marie- Eve Lerat (BE),