{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:33Z","timestamp":1755219813228,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>Half of nurses\u2019 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\u2019 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 2021,2022 and 2023 we installed the system in four general hospitals. We used \u201cReduction rate of overtime hours\u201d and \u201cturnover rate\u201d as indicators of the usefulness of this system. In the 900-bed university hospital, we compared the previous year\u2019s 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, these 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. In a university hospital, the turnover rate continued to decline in 2019:11.3%, 2020:10.0%, and 2021:7.5%. In a general hospital A, the turnover rate continued to decline in 2021:18.5%, 2022:11.5%, and 2023:4.8%. The other three hospitals, B, C, and D also reduced overtime hours and lowered their turnover rates. These results suggest that the implementation of this system can improve both the overtime and turnover rate.<\/jats:p>","DOI":"10.3233\/shti250861","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:20Z","timestamp":1754566400000},"source":"Crossref","is-referenced-by-count":0,"title":["Empirical Study on Reducing Overtime Work by Nurses Through the Application of Process Paths to All Inpatients"],"prefix":"10.3233","author":[{"given":"Satoko","family":"Tsuru","sequence":"first","affiliation":[{"name":"The University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tetsuro","family":"Tamamoto","sequence":"additional","affiliation":[{"name":"Nara Medical University Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Akihiro","family":"Nakao","sequence":"additional","affiliation":[{"name":"Innoxia Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Koichi","family":"Tanizaki","sequence":"additional","affiliation":[{"name":"The University of Tokyo"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chitose","family":"Watanabe","sequence":"additional","affiliation":[{"name":"Saitama Prefectural University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Etsuko","family":"Ishitobi","sequence":"additional","affiliation":[{"name":"Nara Medical University Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seiko","family":"Sasaki","sequence":"additional","affiliation":[{"name":"Tokyo Rinkai Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoko","family":"Maehara","sequence":"additional","affiliation":[{"name":"Bellland General Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chikako","family":"Yuno","sequence":"additional","affiliation":[{"name":"Komatsu Municipal Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yoriko","family":"Jutori","sequence":"additional","affiliation":[{"name":"Fujieda Municipal General Hospital"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250861","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:20Z","timestamp":1754566400000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250861"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250861","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}