Evidence map›Paper›PMID 37123171›Full record

ArticleJournal of patient experience2023

Patient Experience With an In-Home COVID Virtual Observation Unit: An Analysis.

Emily M Hayden, Stephen C Dorner, Jonathan D Sonis, Benjamin A White

Open access · diamondAbstract read
In one paragraph

Article in Journal of patient experience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.2field-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

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
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

4 authors at 1 institution in 1 country.

Emily M HaydenDepartment of Emergency Medicine, Massachusetts General Hospital; Harvard Medical School, Boston, MA, USA.ORCID https://orcid.org/0000-0003-2660-8658
Stephen C DornerDepartment of Emergency Medicine, Massachusetts General Hospital; Harvard Medical School, Boston, MA, USA.
Jonathan D SonisDepartment of Emergency Medicine, Massachusetts General Hospital; Harvard Medical School, Boston, MA, USA.ORCID https://orcid.org/0000-0001-7009-4477
Benjamin A WhiteDepartment of Emergency Medicine, Massachusetts General Hospital; Harvard Medical School, Boston, MA, USA.
Harvard University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We performed a retrospective cohort study of patients admitted to a novel, home-based COVID Virtual Observation Unit (CVOU) from an urban, university-affiliated emergency department with ∼112,000 annual visits. Telephone-based survey questions were administered by nursing staff working with the program. Of 402 patients enrolled in the CVOU, 221 (55%) were able to be contacted during the study period; 180 (45%) agreed to participate in the telephone interview. Overall, 95% (169 out of 177) of the surveyed patients reported 8 to 10 on the likelihood to recommend CVOU and 82% (100 out of 122) rated the quality of care as 10 out of 10. Over 90% of respondents reported that all role groups (nurses, paramedics, and physicians) treated them with courtesy and respect, explained things in an understandable way, and listened to them carefully. Over 80% of respondents reported that the program kept them at home. In summary, patient experiences with this novel home-based care program were highly positive. These data help underscore the importance of patient-centeredness in home-based care, and further support the concept of these innovative care models.

Indexed as

COVID-19paramedicssurvey and questionnairestelemedicine

Identifiers

PMID37123171
PMCPMC10134109
OpenAlexW4367150636

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.