Evidence map›Paper›PMID 37634203›Full record

ArticleJMIR nursing2023

Impact of an Electronic Medical Record-Connected Questionnaire on Efficient Nursing Documentation: Usability and Efficacy Study.

Kana Kodama, Shozo Konishi, Shirou Manabe, Katsuki Okada, Junji Yamaguchi, Shoya Wada, Kento Sugimoto, Sakiko Itoh, Daiyo Takahashi, Ryo Kawasaki and 2 more

Open access · goldAbstract read
In one paragraph

Article in JMIR nursing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.3field-weighted citation impact, top 9% 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

7 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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 at 5 institutions in 1 country.

Kana KodamaDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0002-7001-6839
Shozo KonishiDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0001-6458-2685
Shirou ManabeDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0002-3083-5012
Katsuki OkadaDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0002-1725-781X
Junji YamaguchiMKS Inc, Osaka, Japan.ORCID https://orcid.org/0009-0006-1872-5351
Shoya WadaDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0001-7055-1009
Kento SugimotoDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0002-6874-2399
Sakiko ItohDepartment of Home Health and Palliative Care Nursing, Graduate School of Health Care Sciences, Tokyo Medical and Dental University, Tokyo, Japan.ORCID https://orcid.org/0000-0002-1711-208X
Daiyo TakahashiMKS Inc, Osaka, Japan.ORCID https://orcid.org/0009-0008-3057-2436
Ryo KawasakiDivision of Public Health, Department of Social Medicine, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0002-7492-6303
Yasushi MatsumuraDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0001-9379-3924
Toshihiro TakedaDepartment of Medical Informatics, Osaka University Graduate School of Medicine, Suita, Japan.ORCID https://orcid.org/0000-0003-3073-7823
The University of Osaka · JPMKS Instruments (Japan) · JPOsaka Gakuin University · JPOsaka National Hospital · JPTokyo Medical and Dental University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDocumentation tasks comprise a large percentage of nurses' workloads. Nursing records were partially based on a report from the patient. However, it is not a verbatim transcription of the patient's complaints but a type of medical record. Therefore, to reduce the time spent on nursing documentation, it is necessary to assist in the appropriate conversion or citation of patient reports to professional records. However, few studies have been conducted on systems for capturing patient reports in electronic medical records. In addition, there have been no reports on whether such a system reduces the time spent on nursing documentation.

objectiveThis study aims to develop a patient self-reporting system that appropriately converts data to nursing records and evaluate its effect on reducing the documenting burden for nurses.

methodsAn electronic medical record-connected questionnaire and a preadmission nursing questionnaire were administered. The questionnaire responses entered by the patients were quoted in the patient profile for inpatient assessment in the nursing system. To clarify its efficacy, this study examined whether the use of the electronic questionnaire system saved the nurses' time entering the patient profile admitted between August and December 2022. It also surveyed the usability of the electronic questionnaire between April and December 2022.

resultsA total of 3111 (78%) patients reported that they answered the electronic medical questionnaire by themselves. Of them, 2715 (88%) felt it was easy to use and 2604 (85%) were willing to use it again. The electronic questionnaire was used in 1326 of 2425 admission cases (use group). The input time for the patient profile was significantly shorter in the use group than in the no-use group (P<.001). Stratified analyses showed that in the internal medicine wards and in patients with dependent activities of daily living, nurses took 13%-18% (1.3 to 2 minutes) less time to enter patient profiles within the use group (both P<.001), even though there was no difference in the amount of information. By contrast, in the surgical wards and in the patients with independent activities of daily living, there was no difference in the time to entry (P=.50 and P=.20, respectively), but there was a greater amount of information in the use group.

conclusionsThe study developed and implemented a system in which self-reported patient data were captured in the hospital information network and quoted in the nursing system. This system contributes to improving the efficiency of nurses' task recordings.

Indexed as

data capturedata conversiondocumentationdocumentingEHRelectronic medical recordelectronic questionnaireEMRhealth recordhealth recordsinformation systeminformation systemsmedical informaticsmedical recordsnursingnursing recordnursing recordsnursing systempatient dataquestionnairesself-reportedusability

Identifiers

PMID37634203
PMCPMC10562973
OpenAlexW4386204518

What OpenQuestion holds

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LicenceCC BY
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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.