Patient Data Sharing and Reduction of Overtime Work of Nurses by Innovation of Nursing Records Using Structured Clinical Knowledge

Satoko Tsuru, Tetsuro Tamamoto, Akihiro Nakao, Kouichi Tanizaki, Naohisa Yahagi

研究成果: Conference contribution

抄録

Half of nurses' overtime hours are due to records. Nursing records, which are mainly narrative records, cost a large amount of money. However, it has been pointed out that there are problems with their quality and post-use. In this study, we analyzed the value of nursing records for physicians. As a result, we found that the use of standard observation terms in nursing records can create an environment in which patients' conditions can be shared. To create this environment, the physicians of the clinical path committee classified hospitalized patients in terms of disease, treatment, and examination, and created a list of 778 process paths. Physicians, nurses, and researchers collaborated to develop digital contents with high-priority observation items and care actions adapted to patient conditions for each path. We developed a clinical support system equipped with these digital contents. In May 2019, we installed the system in a 900-bed university hospital. Then, in October 2020, we installed the system in a 400-bed general hospital. We used 'nurses' overtime hours for recording' and 'reduction rate' as indicators of the usefulness of this system. In the 900-bed university hospital, we compared the previous year's results for March, the end of the fiscal year. This overtime hours were 2,944 hours 00 minutes in March 2019 and 2,141 hours 55 minutes in March 2020. 27% reduction was indicated. The respective bed occupancy rates were 90.80 percent and 90.60 percent, with no difference. In the 400-bed general hospital, This overtime hours were compared to the previous year, covering November and December after one month of implementation. 386 hours in November 2019 and 204.5 hours in November 2020. 47% reduction indicated. 366 hours in December 2019 and 214.5 hours in December 2020. A reduction of 41% was shown. These results suggest that the implementation of this system can both improve the quality of team care and reduce overtime.

本文言語English
ホスト出版物のタイトルChallenges of Trustable AI and Added-Value on Health - Proceedings of MIE 2022
編集者Brigitte Seroussi, Patrick Weber, Ferdinand Dhombres, Cyril Grouin, Jan-David Liebe, Jan-David Liebe, Jan-David Liebe, Sylvia Pelayo, Andrea Pinna, Bastien Rance, Bastien Rance, Lucia Sacchi, Adrien Ugon, Adrien Ugon, Arriel Benis, Parisis Gallos
出版社IOS Press BV
ページ525-529
ページ数5
ISBN(電子版)9781643682846
DOI
出版ステータスPublished - 2022 5月 25
イベント32nd Medical Informatics Europe Conference, MIE 2022 - Nice, France
継続期間: 2022 5月 272022 5月 30

出版物シリーズ

名前Studies in Health Technology and Informatics
294
ISSN(印刷版)0926-9630
ISSN(電子版)1879-8365

Conference

Conference32nd Medical Informatics Europe Conference, MIE 2022
国/地域France
CityNice
Period22/5/2722/5/30

ASJC Scopus subject areas

  • 生体医工学
  • 健康情報学
  • 健康情報管理

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