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1.
J Magn Reson Imaging ; 49(7): e265-e270, 2019 06.
Article in English | MEDLINE | ID: mdl-30637838

ABSTRACT

BACKGROUND: Clinical variability in MRI exam durations can impede efficient MRI utilization. There is a paucity of data regarding the degree of variability of identically protocoled MRI studies and when nontechnological factors contribute to time-length variations in MRI exams. PURPOSE: To measure the magnitude of variation in MRI exam duration for identically protocoled MRI exams and to identify potential contributors to variations in MRI exam times. STUDY TYPE: Retrospective. SUBJECTS: 2705 identically protocoled MRI examinations of the cervical spine without contrast, comprehensive stroke exams, and comprehensive brain examinations performed on adult patients from June 30, 2016 through June 30, 2017. ASSESSMENT: MRI exam duration was obtained directly from the image data. Potential predictors for exam length variability were evaluated including patient age, patient gender, performing technologist, patient status (inpatient/outpatient/emergency department), MRI field strength, use of sedation, day of week, and the time of day. STATISTICAL TESTS: Linear regression analysis was performed for each individual variable after correcting for the MRI exam type. A multivariate mixed model was generated to assess for independent associations between the predictors and exam duration. RESULTS: There was substantial variability in the duration of the selected clinical MRI exams, with standard deviations (SDs) ranging between 19% and 29% of the mean exam length for each individual type of exam. The performing technologist was the most significant identified factor contributing to this variation in exam length; SD = 2.645 (P < 0.001). Compared with outpatient exams, inpatient exams required 4.18 minutes longer to complete (P < 0.001), and emergency department studies 1.86 minutes longer (P = 0.005). Male gender was associated with an additional 1.36 minutes of exam time (P < 0.001). DATA CONCLUSION: Nontechnical factors are associated with substantial variation in MRI exam times. These variations can be predicted based on relatively simple clinical and demographic factors, with implications for MRI exam scheduling, protocol design, staff training, and workflow design. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 6 J. Magn. Reson. Imaging 2019.


Subject(s)
Brain/diagnostic imaging , Magnetic Resonance Imaging , Neuroimaging , Adult , Aged , Emergency Service, Hospital , Female , Humans , Inpatients , Linear Models , Male , Middle Aged , Multivariate Analysis , Outpatients , Reproducibility of Results , Retrospective Studies , Time Factors
2.
J Digit Imaging ; 31(2): 201-209, 2018 04.
Article in English | MEDLINE | ID: mdl-29404851

ABSTRACT

Many facets of an image acquisition workflow leave a digital footprint, making workflow analysis amenable to an informatics-based solution. This paper describes a detailed framework for analyzing workflow and uses acute stroke response timeliness in CT as a practical demonstration. We review methods for accessing the digital footprints resulting from common technologist/device interactions. This overview lays a foundation for obtaining data for workflow analysis. We demonstrate the method by analyzing CT imaging efficiency in the setting of acute stroke. We successfully used digital footprints of CT technologists to analyze their workflow. We presented an overview of other digital footprints including but not limited to contrast administration, patient positioning, billing, reformat creation, and scheduling. A framework for analyzing image acquisition workflow was presented. This framework is transferable to any modality, as the key steps of image acquisition, image reconstruction, image post processing, and image transfer to PACS are common to any imaging modality in diagnostic radiology.


Subject(s)
Efficiency, Organizational/standards , Radiology Information Systems/organization & administration , Stroke/diagnostic imaging , Tomography, X-Ray Computed/methods , Workflow , Brain/diagnostic imaging , Humans
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