Abstract
Where should the data for operations analysis come from? It can be collected manually, by observations using notepads and stopwatches. However, the digital systems now widely used in radiology departments are a rich trove of useful data. There are many of these, but the two most important for this purpose are the DICOM standard for images (which contains a great deal of information in addition to the image) and the HL7 standard (created to exchange non-imaging information between radiology devices and applications). Data captured by each of these can be used to identify where and when events occurred, and in some cases who was involved. This chapter discusses the principal types of radiology data, their roles in radiology efficiency analyses, the ways of extracting them, and the principal data collection and implementation problems. We also discuss how we can verify data adequacy, and identify the processes behind the data collection.
“All models are wrong, but some are useful”
George Box, British statistician
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that the task can be even more complex. One would also need to be aware of the risks of overestimation and underestimation, as well as any physical constraints that might make it impossible for the room to exceed a certain maximum size, thus rendering the analysis useless beyond a certain point.
References
Ai J, Oglevee C, Pianykh OS. Determining waiting room occupancy at an outpatient clinic using simulated observations and probability-duration curves. J Am Coll Radiol. 2016;13(6):620–7.
Hawthorne effect. [Online]. https://en.wikipedia.org/wiki/Hawthorne_effect. Accessed 1 Jan 2018.
Observer-expectancy effect. Wikipedia. [Online]. https://en.wikipedia.org/wiki/Observer-expectancy_effect. Accessed 1 Jan 2018.
Pianykh OS. Digital Imaging and Communications in Medicine (DICOM): a practical introduction and survival guide. New York: Springer; 2012.
Bhagat R. HL7 for busy professionals: your no sweat guide to understanding HL7. Anchiove; 2015.
Trotter F, Uhlman D. Hacking healthcare: a guide to standards, workflows, and meaningful use. O’Reilly Media; 2011.
H. L. S. International. HL7 International. [Online]. www.hl7.org.
NEMA/MITA. DICOM Digital Imaging and Communications in Medicine. [Online]. dicom.nema.org.
Towbin AJ. Collecting data to facilitate change. J Am Coll Radiol. 2019;16(9 Pt B):1248–9.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Rosenthal, D., Pianykh, O. (2021). Mining Your Own Business. In: Efficient Radiology. Springer, Cham. https://doi.org/10.1007/978-3-030-53610-7_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-53610-7_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-53609-1
Online ISBN: 978-3-030-53610-7
eBook Packages: MedicineMedicine (R0)