Evidence map›Paper›PMID 37688578›Full record

ArticleJNCI cancer spectrum2023

Ctrl-C: a cross-sectional study of the electronic health record usage patterns of US oncology clinicians.

Sumi Sinha, A Jay Holmgren, Julian C Hong, Lisa S Rotenstein

Open access · goldAbstract read
In one paragraph

Article in JNCI cancer spectrum, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.5field-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

5 citing papers in PubMed, 4 citations in OpenAlex.

  1. EHR perception and momentary well-being in oncology professionals.ESMO real world data and digital oncology · 2026
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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 2 institutions in 1 country.

Sumi SinhaDepartment of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0002-9705-0340
A Jay HolmgrenDepartment of Medicine, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0002-7939-6831
Julian C HongDepartment of Radiation Oncology, University of California, San Francisco, San Francisco, CA, USA.ORCID 0000-0001-5172-6889
Lisa S RotensteinDepartment of Medicine, Brigham and Women's Hospital, Boston, MA, USA.ORCID 0000-0002-8270-7602
University of California, San Francisco · USBrigham and Women's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite some positive impact, the use of electronic health records (EHRs) has been associated with negative effects, such as emotional exhaustion. We sought to compare EHR use patterns for oncology vs nononcology medical specialists. In this cross-sectional study, we employed EHR usage data for 349 ambulatory health-care systems nationwide collected from the vendor Epic from January to August 2019. We compared note composition, message volume, and time in the EHR system for oncology vs nononcology clinicians. Compared with nononcology medical specialists, oncologists had a statistically significantly greater percentage of notes derived from Copy and Paste functions but less SmartPhrase use. They received more total EHR messages per day than other medical specialists, with a higher proportion of results and system-generated messages. Our results point to priorities for enhancing EHR systems to meet the needs of oncology clinicians, particularly as related to facilitating the complex documentation, results, and therapy involved in oncology care.

Indexed as

Electronic Health RecordsMedical OncologyOncologistsCross-Sectional StudiesHumansMaleUnited States

Identifiers

PMID37688578
PMCPMC10555739
OpenAlexW4386577883

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.