Evidence map›Paper›PMID 30477491›Full record

ArticleBMC medical informatics and decision making2018

Does Computerized Physician Order Entry Benefit from Dynamic Structured Data Entry? A Quasi-Experimental Study.

George Linn, Yung-Hsiang Ying, Koyin Chang

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. 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

3 authors.

George LinnDepartment of Obstetrics and Gynecology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
Yung-Hsiang YingCollege of Management, National Taiwan Normal University, 162 Hoping E Rd. Sec 1, Taipei, Taiwan.
Koyin ChangDepartment of Healthcare Information and Management, Ming Chuan University, No 5, De-Ming Rd, Taoyuan, Taiwan. kychang@mail.mcu.edu.tw.ORCID 0000-0001-8045-9521

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith advancements in information technology, computerized physician order entry (CPOE) and electronic Medical Records (eMR), have become widely utilized in medical settings. The predominant mode of CPOE in Taiwan is free text entry (FTE). Dynamic structured data entry (DSDE) was introduced more recently, and has increasingly drawn attention from hospitals across Taiwan. This study assesses how DSDE compares to FTE for CPOE.

methodsA quasi-experimental study was employed to investigate the time-savings, productivity, and efficiency effects of DSDE in an outpatient setting in the gynecological department of a major hospital in Taiwan. Trained female actor patients were employed in trials of both entry methods. Data were submitted to Shapiro-Wilk and Shapiro-Francia tests to assess normality, and then to paired t-tests to assess differences between DSDE and FTE.

resultsRelative to FTE, the use of DSDE resulted in an average of 97% time saved and 55% more abundant and detailed content in medical records. In addition, for each clause entry in a medical record, the time saved is 133% for DSDE compared to FTE.

conclusionThe results suggest that DSDE is a much more efficient and productive entry method for clinicians in hospital outpatient settings. Upgrading eMR systems to the DSDE format would benefit both patients and clinicians.

Indexed as

Electronic Health RecordsHospital DepartmentsMedical Order Entry SystemsOutpatient Clinics, HospitalAdultFemaleGynecologyHumansTaiwanComputerized physician order entryDynamic structured data entryElectronic medical recordQuasi-experimental study

Identifiers

PMID30477491
PMCPMC6258385

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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.