Evidence map›Paper›PMID 24167326›Full record

ArticleTransactions of the American Ophthalmological Society2013

Evaluation of electronic health record implementation in ophthalmology at an academic medical center (an American Ophthalmological Society thesis).

Michael F Chiang, Sarah Read-Brown, Daniel C Tu, Dongseok Choi, David S Sanders, Thomas S Hwang, Steven Bailey, Daniel J Karr, Elizabeth Cottle, John C Morrison and 2 more

Abstract readCase ReportsEvaluation Study
In one paragraph

Article in Transactions of the American Ophthalmological Society, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
37citing 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

37 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Review
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  15. Comparing the Effects of Paper and Digital Checklists on Team Performance in Time-Critical Work.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2019
    Article
  16. Observational
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  18. Response to Letter: Secondary use of electronic health record data for clinical workflow analysis.Journal of the American Medical Informatics Association : JAMIA · 2018
    Article
  19. Decreasing triage time: effects of implementing a step-wise ESI algorithm in an EHR.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2018
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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

12 authors.

Michael F ChiangDepartment of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland (Dr Chiang, Ms Read-Brown, Dr Tu, Mr Sanders, Dr Choi, Dr Hwang, Dr Bailey, Dr Karr, Ms Cottle, Dr Morrison, Dr Wilson); Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland (Dr Chiang, Dr Yackel); Operative Care Division, Portland VA Medical Center, Portland (Dr Tu); and Department of Public Health & Preventive Medicine, Oregon Health & Science University, Portland (Dr Choi).
Sarah Read-Brown
Daniel C Tu
Dongseok Choi
David S Sanders
Thomas S Hwang
Steven Bailey
Daniel J Karr
Elizabeth Cottle
John C Morrison
David J Wilson
Thomas R Yackel

Funding

Proteomics CoreP30EY010572 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI TED S ACOTT · 1995 to 2026
$19.4M
Clinical and Genetic Analysis of Retinopathy of PrematurityR01EY019474 · NEI · OREGON HEALTH & SCIENCE UNIVERSITY · PI CAMPBELL, JOHN PETER · 2010 to 2020
$7.0M
NEI NIH HHS EY19474NEI NIH HHS P30 EY010572NEI NIH HHS P30EY10572BNEI NIH HHS R01 EY019474
6 · The paper itself

Abstract

purposeTo evaluate three measures related to electronic health record (EHR) implementation: clinical volume, time requirements, and nature of clinical documentation. Comparison is made to baseline paper documentation.

methodsAn academic ophthalmology department implemented an EHR in 2006. A study population was defined of faculty providers who worked the 5 months before and after implementation. Clinical volumes, as well as time length for each patient encounter, were collected from the EHR reporting system. To directly compare time requirements, two faculty providers who utilized both paper and EHR systems completed time-motion logs to record the number of patients, clinic time, and nonclinic time to complete documentation. Faculty providers and databases were queried to identify patient records containing both paper and EHR notes, from which three cases were identified to illustrate representative documentation differences.

resultsTwenty-three faculty providers completed 120,490 clinical encounters during a 3-year study period. Compared to baseline clinical volume from 3 months pre-implementation, the post-implementation volume was 88% in quarter 1, 93% in year 1, 97% in year 2, and 97% in year 3. Among all encounters, 75% were completed within 1.7 days after beginning documentation. The mean total time per patient was 6.8 minutes longer with EHR than paper (P<.01). EHR documentation involved greater reliance on textual interpretation of clinical findings, whereas paper notes used more graphical representations, and EHR notes were longer and included automatically generated text.

conclusionThis EHR implementation was associated with increased documentation time, little or no increase in clinical volume, and changes in the nature of ophthalmic documentation.

Indexed as

Academic Medical CentersOphthalmologyAgedDocumentationEfficiency, OrganizationalElectronic Health RecordsEye DiseasesFemaleHumansMaleMiddle AgedUnited States

Identifiers

PMID24167326
PMCPMC3797873

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.