Evidence map›Paper›PMID 30989402›Full record

ReviewCurrent treatment options in cardiovascular medicine2019

Virtual Care 2.0-a Vision for the Future of Data-Driven Technology-Enabled Healthcare.

Sanjeev P Bhavnani, Amy M Sitapati

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current treatment options in cardiovascular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
1.9field-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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. 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

2 authors at 2 institutions in 1 country.

Sanjeev P BhavnaniDivision of Cardiology, Principal Investigator Healthcare Innovation & Practice Transformation Laboratory, Scripps Clinic & Research Foundation, 9898 Genesee Ave. AMP 300, San Diego, CA, 92037, USA. Bhavnani.sanjeev@scrippshealth.org.
Amy M SitapatiDepartment of Medicine, Divisions of General Internal Medicine and Biomedical Informatics, Chief Medical Information Officer of Population Health, University of California San Diego, 9560 Towne Centre Drive, San Diego, CA, 92121, USA. asitapati@ucsd.edu.
Scripps Health · USUniversity of California, San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A busy community cardiologist finished reading eight echocardiograms over lunch and started clinic at 1 pm. As three patients waited, "Jane," a 45-year-old graphic designer was seen for "skipped heart beat." She works about 50 h a week, exercises at the local gym, and enjoys eating a healthy diet. About 4 months ago Jane began experiencing her heart "skipping beats." She initially attributed the symptoms to long hours in the office and caffeine. But, over the holiday, her brother purchased a smart watch and she began digitally recording her cardiac rhythm. About a month ago, the device detected possible atrial fibrillation, so she called and scheduled this visit for a cardiology consultation. Upon that visitation, she and her physician reviewed the device readings. While it appeared to be an irregular rhythm, before either considered a treatment plan, they began to ask questions ranging from the following: "Is this an accurate diagnosis?" "What other data are available to better understand the risk of a cardiac arrhythmia?" "How is this data analyzed so that the best treatment plan can be made?" "And, what type of clinical decision support system is required to 'virtually' monitor people like me using digital health devices to improve the efficiency and quality of care delivered in population health?"

Indexed as

Artificial intelligenceClinical decision supportDigital healthPatient engagementPopulation healthVirtual care

Identifiers

PMID30989402
OpenAlexW2937988809

What OpenQuestion holds

Textmetadata
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.