Data · dataset · 2022
Development of novel optical character recognition system to reduce recording time for vital signs and prescriptions: A simulation-based study
Listed in Teesside University Research Data Repository
A within-subject experimental study was conducted at three community hospitals (Hitachi General Hospital, Southern Tohoku General hospital, and Takasaki Medical Centre) and two fire departments (Koriyama and Takasaki) between October and December 2021.
Description
Volunteer participants (15 paramedics, 10 nurses, and 13 physicians) were included. We prepared six sample pictures: three pictures of ambulance monitors for vital signs (normal, abnormal, and shock) and three pictures of a pharmacy notebook that provided the prescriptions (two, four, and six medications).
For each picture, the participants recorded the data using OCR or manual typing with a smartphone. The internet speed (Mbps) at the site was measured during the study when possible. The main outcome was recording time.
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All user actions on the system were automatically logged into the cloud data server for this study to calculate the recording time. A paired Wilcoxon signed-rank sum test were used to compare the OCR group and the typing group. The median recording times for vital signs were similar between the groups (e.g., normal state, 21 seconds [Interquartile range, IQR, 17–26 seconds] in the OCR group vs. 23 seconds [IQR, 18–31 seconds] in the typing group; p= 0.11).
In contrast, the median recording times for prescriptions were significantly shorter in the OCR group (e.g., six medication lists, 18 seconds [IQR, 14–21 seconds] in the OCR group vs. 144 seconds [IQR, 112–187 seconds] in the typing group; p< .001).
Links
Where it is published
- DOI doi.org/10.17632/rdcmmmdh48.1 ↗
DOI / persistent id · from researchdata tees ac uk
Catalogue records · 1
- OAI-PMH record data.mendeley.com/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Adata… ↗
metadata API · from researchdata tees ac uk
Topics
- From keywords
- Earth & Environmental Science
- Inferred from text
- Optical character recognition 75%
Provenance · 1 source records, 7 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Teesside University Research Data Repository | oai:data.mendeley.com/rdcmmmdh48.1 | 9 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| concepts[method].local:method:ocr | enrichment · researchdata tees ac uk | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/description |
| license | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/rights |
| publication_date | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | |
| title | source · researchdata tees ac uk | connector:researchdata_tees_ac_uk@1.0.0 | /metadata/dc/title |