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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...

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Autores principales: Chiang, Ting-Wei, Chen, Si-Yu, Pan, Yuan-Chien, Lin, Yu-Hsuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
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.
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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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