BACKGROUND: There is great interest in understanding how residents spend their time in the hospital, but traditional time and motion studies are resource intensive and limited in scale. OBJECTIVE: We determined whether a real-time location system (RTLS) that uses infrared emitting badges can be used to track resident time and location. METHODS: Residents rotating on an internal medicine service in January 2018 were given the option to wear an RTLS badge. RTLS data were compared to the call schedule for each participating resident in a deidentified manner. Rules were created to identify work periods to be manually reviewed for data integrity. Reviewed work periods where there were extended periods of time without RTLS badge movement (eg, greater than 300 minutes) were excluded from analysis. RESULTS: Data were collected from 18 residents and included 236 work periods (2922 hours). Based on prespecified rules, 146 work periods were included, representing 83% of total eligible residents (n = 15) and 82% of total hours recorded (2397 hours). Residents spent the highest percentage of their time in physician workrooms (44%, SD 15%), followed by ward hallways (25%, SD 7%) and patient rooms (17%, SD 7%). Several work periods were excluded because residents left their RTLS badge in physician workrooms after the work period ended. CONCLUSIONS: This study demonstrates the potential utility of RTLS to measure resident time and location in the hospital.
BACKGROUND: There is great interest in understanding how residents spend their time in the hospital, but traditional time and motion studies are resource intensive and limited in scale. OBJECTIVE: We determined whether a real-time location system (RTLS) that uses infrared emitting badges can be used to track resident time and location. METHODS: Residents rotating on an internal medicine service in January 2018 were given the option to wear an RTLS badge. RTLS data were compared to the call schedule for each participating resident in a deidentified manner. Rules were created to identify work periods to be manually reviewed for data integrity. Reviewed work periods where there were extended periods of time without RTLS badge movement (eg, greater than 300 minutes) were excluded from analysis. RESULTS: Data were collected from 18 residents and included 236 work periods (2922 hours). Based on prespecified rules, 146 work periods were included, representing 83% of total eligible residents (n = 15) and 82% of total hours recorded (2397 hours). Residents spent the highest percentage of their time in physician workrooms (44%, SD 15%), followed by ward hallways (25%, SD 7%) and patient rooms (17%, SD 7%). Several work periods were excluded because residents left their RTLS badge in physician workrooms after the work period ended. CONCLUSIONS: This study demonstrates the potential utility of RTLS to measure resident time and location in the hospital.
Authors: Abraham Verghese; Blake Charlton; Jerome P Kassirer; Meghan Ramsey; John P A Ioannidis Journal: Am J Med Date: 2015-07-02 Impact factor: 4.965
Authors: Joseph L Dieleman; Ellen Squires; Anthony L Bui; Madeline Campbell; Abigail Chapin; Hannah Hamavid; Cody Horst; Zhiyin Li; Taylor Matyasz; Alex Reynolds; Nafis Sadat; Matthew T Schneider; Christopher J L Murray Journal: JAMA Date: 2017-11-07 Impact factor: 56.272
Authors: Sanjay V Desai; David A Asch; Lisa M Bellini; Krisda H Chaiyachati; Manqing Liu; Alice L Sternberg; James Tonascia; Alyssa M Yeager; Jeremy M Asch; Joel T Katz; Mathias Basner; David W Bates; Karl Y Bilimoria; David F Dinges; Orit Even-Shoshan; David M Shade; Jeffrey H Silber; Dylan S Small; Kevin G Volpp; Judy A Shea Journal: N Engl J Med Date: 2018-03-20 Impact factor: 91.245
Authors: Sanjay V Desai; Leonard Feldman; Lorrel Brown; Rebecca Dezube; Hsin-Chieh Yeh; Naresh Punjabi; Kia Afshar; Michael R Grunwald; Colleen Harrington; Rakhi Naik; Joseph Cofrancesco Journal: JAMA Intern Med Date: 2013-04-22 Impact factor: 21.873
Authors: Lauren Block; Robert Habicht; Albert W Wu; Sanjay V Desai; Kevin Wang; Kathryn Novello Silva; Timothy Niessen; Nora Oliver; Leonard Feldman Journal: J Gen Intern Med Date: 2013-08 Impact factor: 5.128
Authors: Kathlyn E Fletcher; Alexis M Visotcky; Jason M Slagle; Sergey Tarima; Matthew B Weinger; Marilyn M Schapira Journal: J Gen Intern Med Date: 2012-06-27 Impact factor: 5.128
Authors: Nathalie Wenger; Marie Méan; Julien Castioni; Pedro Marques-Vidal; Gérard Waeber; Antoine Garnier Journal: Ann Intern Med Date: 2017-01-31 Impact factor: 25.391
Authors: Michael A Rosen; Aaron S Dietz; Nam Lee; I-Jeng Wang; Jared Markowitz; Rhonda M Wyskiel; Ting Yang; Carey E Priebe; Adam Sapirstein; Ayse P Gurses; Peter J Pronovost Journal: PLoS One Date: 2018-10-12 Impact factor: 3.240
Authors: Brandon Tang; Ryan Sandarage; Jocelyn Chai; Kristin Anne Dawson; Katrina Rose Dutkiewicz; Stephan Saad; Vanessa Kitchin; Rose Hatala; Iain McCormick; Barry Kassen Journal: CMAJ Date: 2022-02-14 Impact factor: 8.262