Evidence map›Paper›PMID 27358760›Full record

ArticleHealth and technology

Cybercare 2.0: meeting the challenge of the global burden of disease in 2030.

Joseph M Rosen, Luis Kun, Robyn E Mosher, Elliott Grigg, Ronald C Merrell, Christian Macedonia, Julien Klaudt-Moreau, Andrew Price-Smith, James Geiling

Open access · hybridAbstract read
In one paragraph

Article in Health and technology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.8field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 33 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Artificial Intelligence in Health Care: Bibliometric Analysis.Journal of medical Internet research · 2020
    Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 7 institutions in 1 country.

Joseph M RosenGeisel School of Medicine, Dartmouth College, Hanover, NH USA ; Plastic Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, NH USA ; Thayer School of Engineering, Dartmouth College, Hanover, NH USA.
Luis KunGlobal Citizen Safety and Security WG; IEEE Society on Social Implications of Technology (SSIT), Vienna, VA USA.
Robyn E MosherGeisel School of Medicine, Dartmouth College, Hanover, NH USA.
Elliott GriggAnesthesiology and Pain Medicine, University of Washington, Seattle Children's Hospital, Seattle, WA USA.
Ronald C MerrellVirginia Commonwealth University, Mentone, AL USA.
Christian MacedoniaLancaster General Health, Penn Medicine, Lancaster, PA USA.
Julien Klaudt-MoreauGeisel School of Medicine, Dartmouth College, Hanover, NH USA ; Thayer School of Engineering, Dartmouth College, Hanover, NH USA.
Andrew Price-SmithDepartment of Political Science, Colorado College, Colorado Springs, CO USA.
James GeilingGeisel School of Medicine, Dartmouth College, Hanover, NH USA ; Veterans Affairs Medical Center, White River Junction, VT USA.
Dartmouth College · USColorado College · USPublic Citizen · USUniversity of Pennsylvania Health System · USUniversity of Washington · USVirginia Commonwealth University · USWhite River Junction VA Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we propose to advance and transform today's healthcare system using a model of networked health care called Cybercare. Cybercare means "health care in cyberspace" - for example, doctors consulting with patients via videoconferencing across a distributed network; or patients receiving care locally - in neighborhoods, "minute clinics," and homes - using information technologies such as telemedicine, smartphones, and wearable sensors to link to tertiary medical specialists. This model contrasts with traditional health care, in which patients travel (often a great distance) to receive care from providers in a central hospital. The Cybercare model shifts health care provision from hospital to home; from specialist to generalist; and from treatment to prevention. Cybercare employs advanced technology to deliver services efficiently across the distributed network - for example, using telemedicine, wearable sensors and cell phones to link patients to specialists and upload their medical data in near-real time; using information technology (IT) to rapidly detect, track, and contain the spread of a global pandemic; or using cell phones to manage medical care in a disaster situation. Cybercare uses seven "pillars" of technology to provide medical care: genomics; telemedicine; robotics; simulation, including virtual and augmented reality; artificial intelligence (AI), including intelligent agents; the electronic medical record (EMR); and smartphones. All these technologies are evolving and blending. The technologies are integrated functionally because they underlie the Cybercare network, and/or form part of the care for patients using that distributed network. Moving health care provision to a networked, distributed model will save money, improve outcomes, facilitate access, improve security, increase patient and provider satisfaction, and may mitigate the international global burden of disease. In this paper we discuss how Cybercare is being implemented now, and envision its growth by 2030.

Indexed as

Burden of diseaseCell phonesCybercareGlobal healthHealth care costInformation technologyITTelemedicine

Identifiers

PMID27358760
PMCPMC4901101
OpenAlexW2396694279

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.