Browse Articles

  • Comment
    | Open Access

    In 2019, the World Health Organization (WHO) released the first-ever evidence-based guidelines for digital health. The guideline provides nine recommendations on select digital health interventions that involve the use of a mobile phone or device. It also provides information on implementation considerations, quality and certainty of extant evidence, factors related to acceptability and feasibility of the intervention, and gaps in the evidence that can inform future research. Given the pivotal role digital health can play in supporting health systems, seen especially in light of the COVID-19 pandemic, these guidelines can help provide a roadmap for governments and policymakers in introducing and scaling up digital health interventions to support population health outcomes.

    • Alain Labrique
    • , Smisha Agarwal
    • , Tigest Tamrat
    •  & Garrett Mehl
  • Comment
    | Open Access

    To prevent the spread of COVID-19 and to continue responding to healthcare needs, hospitals are rapidly adopting telehealth and other digital health tools to deliver care remotely. Intelligent conversational agents and virtual assistants, such as chatbots and voice assistants, have been utilized to augment health service capacity to screen symptoms, deliver healthcare information, and reduce exposure. In this commentary, we examined the state of voice assistants (e.g., Google Assistant, Apple Siri, Amazon Alexa) as an emerging tool for remote healthcare delivery service and discussed the readiness of the health system and technology providers to adapt voice assistants as an alternative healthcare delivery modality during a health crisis and pandemic.

    • Emre Sezgin
    • , Yungui Huang
    • , Ujjwal Ramtekkar
    •  & Simon Lin
  • Perspective
    | Open Access

    • Nicola Rieke
    • , Jonny Hancox
    • , Wenqi Li
    • , Fausto Milletarì
    • , Holger R. Roth
    • , Shadi Albarqouni
    • , Spyridon Bakas
    • , Mathieu N. Galtier
    • , Bennett A. Landman
    • , Klaus Maier-Hein
    • , Sébastien Ourselin
    • , Micah Sheller
    • , Ronald M. Summers
    • , Andrew Trask
    • , Daguang Xu
    • , Maximilian Baust
    •  & M. Jorge Cardoso
  • Article
    | Open Access

    • Julie Redfern
    • , Genevieve Coorey
    • , John Mulley
    • , Anish Scaria
    • , Lis Neubeck
    • , Nashid Hafiz
    • , Chris Pitt
    • , Kristie Weir
    • , Joanna Forbes
    • , Sharon Parker
    • , Fiona Bampi
    • , Alison Coenen
    • , Gemma Enright
    • , Annette Wong
    • , Theresa Nguyen
    • , Mark Harris
    • , Nick Zwar
    • , Clara K. Chow
    • , Anthony Rodgers
    • , Emma Heeley
    • , Katie Panaretto
    • , Annie Lau
    • , Noel Hayman
    • , Tim Usherwood
    •  & David Peiris
  • Comment
    | Open Access

    The SARS-CoV-2 pandemic has challenged healthcare systems worldwide. Uncertainty of transmission, limitations of physical healthcare system infrastructure and supplies as well as workforce shortages require dynamic adaption of resource deployment to manage rapidly evolving care demands, ideally based on real time data for the entire population. Moreover, shut down of traditional face-to-face care infrastructure requires rapid deployment of virtual health care options to avoid collapse of health organizations. The Alberta Electronic Health Record Information System is one of the largest population based comprehensive electronic medical record (EMR) installations. Alberta’s long standing solid telehealth hardware-, training-, provider remuneration- and legislation infrastructure has enabled quick transition to virtual healthcare. Virtual health services including asynchronous secure clinical communications, real-time virtual care via messaging, telephony or video conferencing (telehealth) and ancillary functions like triage, scheduling, documentation and reporting, the previously established virtual hospital program with home monitoring, virtual health assessments, medication review, education and support for patients and families and coordination between family doctors, specialists and other health team members help to control viral transmission, protect healthcare personnel and save supplies. Moreover, rapid launch of online screening and triage tools to guide testing and isolation, online result sharing, infected patient and contact tracing including a smartphone exposure tracking application (ABTraceTogether), electronic best practice alerts and decision support tools, test and treatment order sets for standardized COVID-19 management, continuous access to population level real-time data to inform healthcare provider, public health and government decisions have become key factors in the management of a global crisis in Alberta.

    • Daniel C. Baumgart
  • Article
    | Open Access

    • Erping Long
    • , Jingjing Chen
    • , Xiaohang Wu
    • , Zhenzhen Liu
    • , Liming Wang
    • , Jiewei Jiang
    • , Wangting Li
    • , Yi Zhu
    • , Chuan Chen
    • , Zhuoling Lin
    • , Jing Li
    • , Xiaoyan Li
    • , Hui Chen
    • , Chong Guo
    • , Lanqin Zhao
    • , Daoyao Nie
    • , Xinhua Liu
    • , Xin Liu
    • , Zhe Dong
    • , Bo Yun
    • , Wenbin Wei
    • , Fan Xu
    • , Jian Lv
    • , Min Li
    • , Shiqi Ling
    • , Lei Zhong
    • , Junhong Chen
    • , Qishan Zheng
    • , Li Zhang
    • , Yi Xiang
    • , Gang Tan
    • , Kai Huang
    • , Yifan Xiang
    • , Duoru Lin
    • , Xulin Zhang
    • , Meimei Dongye
    • , Dongni Wang
    • , Weirong Chen
    • , Xiyang Liu
    • , Haotian Lin
    •  & Yizhi Liu
  • Comment
    | Open Access

    Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly driven by the emergence of increasingly accurate machine learning models. However, the promise of AI delivering scalable and sustained value for patient care in the real world setting has yet to be realized. In order to safely and effectively bring AI into use in healthcare, there needs to be a concerted effort around not just the creation, but also the delivery of AI. This AI “delivery science” will require a broader set of tools, such as design thinking, process improvement, and implementation science, as well as a broader definition of what AI will look like in practice, which includes not just machine learning models and their predictions, but also the new systems for care delivery that they enable. The careful design, implementation, and evaluation of these AI enabled systems will be important in the effort to understand how AI can improve healthcare.

    • Ron C. Li
    • , Steven M. Asch
    •  & Nigam H. Shah
  • Article
    | Open Access

    • Gabriel A. Brat
    • , Griffin M. Weber
    • , Nils Gehlenborg
    • , Paul Avillach
    • , Nathan P. Palmer
    • , Luca Chiovato
    • , James Cimino
    • , Lemuel R. Waitman
    • , Gilbert S. Omenn
    • , Alberto Malovini
    • , Jason H. Moore
    • , Brett K. Beaulieu-Jones
    • , Valentina Tibollo
    • , Shawn N. Murphy
    • , Sehi L’ Yi
    • , Mark S. Keller
    • , Riccardo Bellazzi
    • , David A. Hanauer
    • , Arnaud Serret-Larmande
    • , Alba Gutierrez-Sacristan
    • , John J. Holmes
    • , Douglas S. Bell
    • , Kenneth D. Mandl
    • , Robert W. Follett
    • , Jeffrey G. Klann
    • , Douglas A. Murad
    • , Luigia Scudeller
    • , Mauro Bucalo
    • , Katie Kirchoff
    • , Jean Craig
    • , Jihad Obeid
    • , Vianney Jouhet
    • , Romain Griffier
    • , Sebastien Cossin
    • , Bertrand Moal
    • , Lav P. Patel
    • , Antonio Bellasi
    • , Hans U. Prokosch
    • , Detlef Kraska
    • , Piotr Sliz
    • , Amelia L. M. Tan
    • , Kee Yuan Ngiam
    • , Alberto Zambelli
    • , Danielle L. Mowery
    • , Emily Schiver
    • , Batsal Devkota
    • , Robert L. Bradford
    • , Mohamad Daniar
    • , Christel Daniel
    • , Vincent Benoit
    • , Romain Bey
    • , Nicolas Paris
    • , Patricia Serre
    • , Nina Orlova
    • , Julien Dubiel
    • , Martin Hilka
    • , Anne Sophie Jannot
    • , Stephane Breant
    • , Judith Leblanc
    • , Nicolas Griffon
    • , Anita Burgun
    • , Melodie Bernaux
    • , Arnaud Sandrin
    • , Elisa Salamanca
    • , Sylvie Cormont
    • , Thomas Ganslandt
    • , Tobias Gradinger
    • , Julien Champ
    • , Martin Boeker
    • , Patricia Martel
    • , Loic Esteve
    • , Alexandre Gramfort
    • , Olivier Grisel
    • , Damien Leprovost
    • , Thomas Moreau
    • , Gael Varoquaux
    • , Jill-Jênn Vie
    • , Demian Wassermann
    • , Arthur Mensch
    • , Charlotte Caucheteux
    • , Christian Haverkamp
    • , Guillaume Lemaitre
    • , Silvano Bosari
    • , Ian D. Krantz
    • , Andrew South
    • , Tianxi Cai
    •  & Isaac S. Kohane
  • Comment
    | Open Access

    It has been proposed that telehealth may help to combat the epidemic of diabetes and other chronic diseases in the US. As a result of rapid technological advancement over the past decade, there has been an explosion in virtual diabetes management program offerings rooted in smartphone technology, connected devices for blood glucose monitoring, and remote coaching or support. Such offerings take many forms with unique features. We provide a care team-based classification system for connected diabetes care programs and highlight their strengths and limitations. We also include a framework for how the different classes of connected diabetes care may be deployed in a health system to promote improved population health.

    • Brian J. Levine
    • , Kelly L. Close
    •  & Robert A. Gabbay
  • Perspective
    | Open Access

    • O. T. Inan
    • , P. Tenaerts
    • , S. A. Prindiville
    • , H. R. Reynolds
    • , D. S. Dizon
    • , K. Cooper-Arnold
    • , M. Turakhia
    • , M. J. Pletcher
    • , K. L. Preston
    • , H. M. Krumholz
    • , B. M. Marlin
    • , K. D. Mandl
    • , P. Klasnja
    • , B. Spring
    • , E. Iturriaga
    • , R. Campo
    • , P. Desvigne-Nickens
    • , Y. Rosenberg
    • , S. R. Steinhubl
    •  & R. M. Califf
  • Comment
    | Open Access

    Digital health technology tools (DHTT) are technologies such as apps, smartphones, and wearables that remotely acquire health-related information from individuals. They have the potential advantages of objectivity and sensitivity of measurement, richness of high-frequency sensor data, and opportunity for passive collection of health-related data. Thus, DHTTs promise to provide patient phenotyping at an order of granularity several times greater than is possible with traditional clinical research tools. While the conceptual development of novel DHTTs is keeping pace with technological and analytical advancements, an as yet unaddressed gap is how to develop robust and meaningful outcome measures based on sensor data. Here, we describe two roadmaps which were developed to generate outcome measures based on DHTT data: one using a data-centric approach and the second a patient-centric approach. The data-centric approach to develop digital outcome measures summarizes those sensor features maximally sensitive to the concept of interest, exemplified with the quantification of disease progression. The patient-centric approach summarizes those sensor features that are optimally relevant to patients’ functioning in everyday life. Both roadmaps are exemplified for use in tracking disease progression in observational and clinical interventional studies, and with a DHTT designed to evaluate motor symptom severity and symptom experience in Parkinson’s disease. Use cases other than disease progression (e.g., case-finding) are considered summarily. DHTT research requires methods to summarize sensor data into meaningful outcome measures. It is hoped that the concepts outlined here will encourage a scientific discourse and eventual consensus on the creation of novel digital outcome measures for both basic clinical research and clinical drug development.

    • Kirsten I. Taylor
    • , Hannah Staunton
    • , Florian Lipsmeier
    • , David Nobbs
    •  & Michael Lindemann
  • Comment
    | Open Access

    It has been 30 years since the passage of the Americans with Disabilities Act and technological development has drastically changed the future for those with disabilities. As healthcare evolves toward promoting telehealth and patient-centered care, leaders must embrace persons with disabilities and caregivers as valued partners in design and implementation, not as passive “end-users”. We call for a new era of inclusive innovation, a term proposed in this publication to describe accessible technological design for all. The next 30 years of the ADA leading to year 2050, should reflect a new era of access, whereby digital health surmounts geographic, social, and economic barriers toward an inclusive virtual society.

    • Kimberly Noel
    •  & Brooke Ellison
  • Comment
    | Open Access

    With emerging innovations in artificial intelligence (AI) poised to substantially impact medical practice, interest in training current and future physicians about the technology is growing. Alongside comes the question of what, precisely, should medical students be taught. While competencies for the clinical usage of AI are broadly similar to those for any other novel technology, there are qualitative differences of critical importance to concerns regarding explainability, health equity, and data security. Drawing on experiences at the University of Toronto Faculty of Medicine and MIT Critical Data’s “datathons”, the authors advocate for a dual-focused approach: combining robust data science-focused additions to baseline health research curricula and extracurricular programs to cultivate leadership in this space.

    • Liam G. McCoy
    • , Sujay Nagaraj
    • , Felipe Morgado
    • , Vinyas Harish
    • , Sunit Das
    •  & Leo Anthony Celi
  • Review Article
    | Open Access

    • Kristine Arges
    • , Themistocles Assimes
    • , Vikram Bajaj
    • , Suresh Balu
    • , Mustafa R. Bashir
    • , Laura Beskow
    • , Rosalia Blanco
    • , Robert Califf
    • , Paul Campbell
    • , Larry Carin
    • , Victoria Christian
    • , Scott Cousins
    • , Millie Das
    • , Marie Dockery
    • , Pamela S. Douglas
    • , Ashley Dunham
    • , Julie Eckstrand
    • , Dominik Fleischmann
    • , Emily Ford
    • , Elizabeth Fraulo
    • , John French
    • , Sanjiv S. Gambhir
    • , Geoffrey S. Ginsburg
    • , Robert C. Green
    • , Francois Haddad
    • , Adrian Hernandez
    • , John Hernandez
    • , Erich S. Huang
    • , Glenn Jaffe
    • , Daniel King
    • , Lynne H. Koweek
    • , Curtis Langlotz
    • , Yaping J. Liao
    • , Kenneth W. Mahaffey
    • , Kelly Marcom
    • , William J. Marks Jr.
    • , David Maron
    • , Reid McCabe
    • , Shannon McCall
    • , Rebecca McCue
    • , Jessica Mega
    • , David Miller
    • , Lawrence H. Muhlbaier
    • , Rajan Munshi
    • , L. Kristin Newby
    • , Ezra Pak-Harvey
    • , Bray Patrick-Lake
    • , Michael Pencina
    • , Eric D. Peterson
    • , Fatima Rodriguez
    • , Scarlet Shore
    • , Svati Shah
    • , Steven Shipes
    • , George Sledge
    • , Susie Spielman
    • , Ryan Spitler
    • , Terry Schaack
    • , Geeta Swamy
    • , Martin J. Willemink
    •  & Charlene A. Wong