Evidence map›Paper›PMID 33936516›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2020

EHR Conversion on the PreOp Care: A Pre-Post Workflow Comparison.

Lu Zheng, Benjamin J Duncan, David R Kaufman, Stephanie K Furniss, Adela Grando, Karl A Poterack, Richard A Helmers, Timothy A Miksch, Brad N Doebbeling

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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, 1 synthesis or guideline pooled it.

  1. Pooled it
  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

9 authors.

Lu ZhengCollege of Health Solutions, Arizona State University, AZ, US.
Benjamin J DuncanCollege of Health Solutions, Arizona State University, AZ, US.
David R KaufmanSUNY Downstate Health Sciences University, Brooklyn, NY, US.
Stephanie K FurnissCollege of Health Solutions, Arizona State University, AZ, US.
Adela GrandoCollege of Health Solutions, Arizona State University, AZ, US.
Karl A PoterackInformatics and Knowledge Management, Mayo Clinic, Rochester, MN, US.
Richard A HelmersDepartment of Pulmonary Medicine, Mayo Clinic Health System, Northwest, Wisconsin, Eau Claire, WI, US.
Timothy A MikschInformatics and Knowledge Management, Mayo Clinic, Rochester, MN, US.
Brad N DoebbelingCollege of Health Solutions, Arizona State University, AZ, US.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The impact of EHRs conversion on clinicians' daily work is crucial to evaluate the success of the intervention for Hospitals and to yield valuable insights into quality improvement. To assess the impact of different EHR systems on the preoperative nursing workflow, we used a structured framework combining quantitative time and motion study and qualitative cognitive analysis to characterize, visualize and explain the differences before and after an EHR conversion. The results showed that the EHR conversion brought a significant decrease in the patient case time and a reduced percentage of time using EHR. PreOp nurses spent a higher proportion of time caring for the patient, while the important tasks were completed in a more continuous pattern after the EHR conversion. The workflow variance was due to different nurse's cognitive process and the task time change was reduced because of some new interface features in the new EHR systems.

Indexed as

WorkflowElectronic Health RecordsHumansTime and Motion Studies

Identifiers

PMID33936516
PMCPMC8075530

What OpenQuestion holds

Textmetadata
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Registered trials

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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.