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Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development
BACKGROUND: There are numerous mobile apps for tracking work hours, but only a few of them record work hours automatically instead of relying on manual logging. No apps have been customized for medical staff, whose work schedules are highly complicated as they have both regular hours and on-call dut...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064958/ https://www.ncbi.nlm.nih.gov/pubmed/32130165 http://dx.doi.org/10.2196/16063 |
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author | Chiang, Ting-Wei Chen, Si-Yu Pan, Yuan-Chien Lin, Yu-Hsuan |
author_facet | Chiang, Ting-Wei Chen, Si-Yu Pan, Yuan-Chien Lin, Yu-Hsuan |
author_sort | Chiang, Ting-Wei |
collection | PubMed |
description | BACKGROUND: There are numerous mobile apps for tracking work hours, but only a few of them record work hours automatically instead of relying on manual logging. No apps have been customized for medical staff, whose work schedules are highly complicated as they have both regular hours and on-call duties. OBJECTIVE: The specific aims of this study were to (1) identify the Staff Hours app users’ GPS-defined work hours, (2) examine the overtime work hours from the app-recorded total work hours and the participants’ self-reported scheduled work hours, and (3) compare these app-recorded total work hours among different occupations. METHODS: We developed an app, Staff Hours, to automatically calculate a user’s work hours via GPS background data. Users can enter their scheduled hours, including regular hours and on-call duties. The app automatically generates overtime reports by comparing the app-recorded total work hours with the user-defined scheduled hours. A total of 183 volunteers (60 females and 123 males; mean age 32.98 years, SD 6.74) were included in this study. Most of the participants (162/183, 88.5%) were medical staff, and their positions were resident physicians (n=89), visiting staff (n=38), medical students (n=10), registered nurses (n=25), and non–health care professionals (non-HCPs; n=21). RESULTS: The total work hours (mean 55.69 hours, SD 21.34) of the 183 participants were significantly higher than their scheduled work hours (mean 50.67 hours, SD 21.44; P=.01). Medical staff had significantly longer total work hours (mean 57.01 hours, SD 21.20) than non-HCPs (mean 45.48 hours, SD 20.08; P=.02). Residents (mean 60.38 hours, SD 18.67) had significantly longer work hours than visiting staff (mean 51.42 hours, SD 20.33; P=.03) and non-HCPs (mean 45.48 hours, SD 20.08; P=.004). CONCLUSIONS: Staff Hours is the first automatic GPS location–based app designed for medical staff to track work hours and calculate overtime. For medical staff, this app could keep complete and accurate records of work hours in real time, reduce bias, and allow for better complying with labor regulations. |
format | Online Article Text |
id | pubmed-7064958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-70649582020-03-19 Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development Chiang, Ting-Wei Chen, Si-Yu Pan, Yuan-Chien Lin, Yu-Hsuan JMIR Mhealth Uhealth Original Paper BACKGROUND: There are numerous mobile apps for tracking work hours, but only a few of them record work hours automatically instead of relying on manual logging. No apps have been customized for medical staff, whose work schedules are highly complicated as they have both regular hours and on-call duties. OBJECTIVE: The specific aims of this study were to (1) identify the Staff Hours app users’ GPS-defined work hours, (2) examine the overtime work hours from the app-recorded total work hours and the participants’ self-reported scheduled work hours, and (3) compare these app-recorded total work hours among different occupations. METHODS: We developed an app, Staff Hours, to automatically calculate a user’s work hours via GPS background data. Users can enter their scheduled hours, including regular hours and on-call duties. The app automatically generates overtime reports by comparing the app-recorded total work hours with the user-defined scheduled hours. A total of 183 volunteers (60 females and 123 males; mean age 32.98 years, SD 6.74) were included in this study. Most of the participants (162/183, 88.5%) were medical staff, and their positions were resident physicians (n=89), visiting staff (n=38), medical students (n=10), registered nurses (n=25), and non–health care professionals (non-HCPs; n=21). RESULTS: The total work hours (mean 55.69 hours, SD 21.34) of the 183 participants were significantly higher than their scheduled work hours (mean 50.67 hours, SD 21.44; P=.01). Medical staff had significantly longer total work hours (mean 57.01 hours, SD 21.20) than non-HCPs (mean 45.48 hours, SD 20.08; P=.02). Residents (mean 60.38 hours, SD 18.67) had significantly longer work hours than visiting staff (mean 51.42 hours, SD 20.33; P=.03) and non-HCPs (mean 45.48 hours, SD 20.08; P=.004). CONCLUSIONS: Staff Hours is the first automatic GPS location–based app designed for medical staff to track work hours and calculate overtime. For medical staff, this app could keep complete and accurate records of work hours in real time, reduce bias, and allow for better complying with labor regulations. JMIR Publications 2020-02-25 /pmc/articles/PMC7064958/ /pubmed/32130165 http://dx.doi.org/10.2196/16063 Text en ©Ting-Wei Chiang, Si-Yu Chen, Yuan-Chien Pan, Yu-Hsuan Lin. Originally published in JMIR mHealth and uHealth (http://mhealth.jmir.org), 25.02.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mHealth and uHealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Chiang, Ting-Wei Chen, Si-Yu Pan, Yuan-Chien Lin, Yu-Hsuan Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development |
title | Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development |
title_full | Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development |
title_fullStr | Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development |
title_full_unstemmed | Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development |
title_short | Automatic Work-Hours Recorder for Medical Staff (Staff Hours): Mobile App Development |
title_sort | automatic work-hours recorder for medical staff (staff hours): mobile app development |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7064958/ https://www.ncbi.nlm.nih.gov/pubmed/32130165 http://dx.doi.org/10.2196/16063 |
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