McKinley Glover1, Renata R Almeida2, Pamela W Schaefer2, Michael H Lev2, William A Mehan2. 1. Massachusetts General Physicians Organization, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. Electronic address: mckinley.glover@mgh.harvard.edu. 2. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.
Abstract
PURPOSE: Traditional radiology productivity metrics do not account for noninterpretive tasks (NITs). This study aimed to systematically quantify NITs and their impact on report turn-around time (RTAT) during solo academic neuroradiology overnight coverage in the emergency department. METHODS: Retrospective analysis of 1 week of data, including phone call quantity and duration, clinician identification badge access to the reading room ("badge swipes"), suspected acute strokes, imaging examination volume, and emergency department patient volume, was performed. Univariate analyses were employed to quantify NITs. Multivariate linear regression was used to determine if NITs within an hour are predictive of RTAT of studies completed within that hour. RESULTS: Sixty-three hours of overnight neuroradiology coverage were analyzed. The mean number of phone calls per hour was 8.7 (SD: 5.7), and mean duration of phone calls per hour was 12 min (SD: 9.6 min, range 1-46). The mean number of badge swipes per hour was 2.1 (SD 1.6). The mean number of examinations (CT and MRI) performed per hour was 2.2 (SD: 1.7). Regression analyses found total duration of phone calls in an hour as the strongest independent predictor of RTAT (unstandardized β = 4.25, P < .001). The overall multivariate model was also significant (P < .001, R2 = 0.596; adjusted R2 = 0.578). CONCLUSIONS: For every 1-min increase in total duration of calls in an hour, mean RTAT increased by 4.25 min. Standardizing capture of NITs may aid development of strategies that address productivity, communication, and value in radiology.
PURPOSE: Traditional radiology productivity metrics do not account for noninterpretive tasks (NITs). This study aimed to systematically quantify NITs and their impact on report turn-around time (RTAT) during solo academic neuroradiology overnight coverage in the emergency department. METHODS: Retrospective analysis of 1 week of data, including phone call quantity and duration, clinician identification badge access to the reading room ("badge swipes"), suspected acute strokes, imaging examination volume, and emergency department patient volume, was performed. Univariate analyses were employed to quantify NITs. Multivariate linear regression was used to determine if NITs within an hour are predictive of RTAT of studies completed within that hour. RESULTS: Sixty-three hours of overnight neuroradiology coverage were analyzed. The mean number of phone calls per hour was 8.7 (SD: 5.7), and mean duration of phone calls per hour was 12 min (SD: 9.6 min, range 1-46). The mean number of badge swipes per hour was 2.1 (SD 1.6). The mean number of examinations (CT and MRI) performed per hour was 2.2 (SD: 1.7). Regression analyses found total duration of phone calls in an hour as the strongest independent predictor of RTAT (unstandardized β = 4.25, P < .001). The overall multivariate model was also significant (P < .001, R2 = 0.596; adjusted R2 = 0.578). CONCLUSIONS: For every 1-min increase in total duration of calls in an hour, mean RTAT increased by 4.25 min. Standardizing capture of NITs may aid development of strategies that address productivity, communication, and value in radiology.
Authors: P D Chang; E Kuoy; J Grinband; B D Weinberg; M Thompson; R Homo; J Chen; H Abcede; M Shafie; L Sugrue; C G Filippi; M-Y Su; W Yu; C Hess; D Chow Journal: AJNR Am J Neuroradiol Date: 2018-07-26 Impact factor: 3.825
Authors: J J Heit; H Coelho; F O Lima; M Granja; A Aghaebrahim; R Hanel; K Kwok; H Haerian; C W Cereda; C Venkatasubramanian; S Dehkharghani; L A Carbonera; J Wiener; K Copeland; F Mont'Alverne Journal: AJNR Am J Neuroradiol Date: 2020-12-24 Impact factor: 3.825