Abstract
Microservices are a software development approach where an application is structured as a collection of loosely coupled, independently deployable services, each focusing on executing a specific purpose. The development of microservices could have a significant impact on radiology workflows, allowing routine tasks to be automated and improving the efficiency and accuracy of radiologic tasks. This technical report describes the development of several microservices that have been successfully deployed in a tertiary cancer center, resulting in substantial time savings for radiologists and other staff involved in radiology workflows. These microservices include the automatic generation of shift emails, notifying administrative staff and faculty about fellows on rotation, notifying referring physicians about outside examinations, and populating report templates with information from PACS and RIS. The report outlines the common thought process behind developing these microservices, including identifying a problem, connecting various APIs, collecting data in a database, writing a prototype and deploying it, gathering feedback and refining the service, putting it in production, and identifying staff who are in charge of maintaining the service. The report concludes by discussing the benefits and challenges of microservices in radiology workflows, highlighting the importance of multidisciplinary collaboration, interoperability, security, and privacy.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
All relevant data are contained within the manuscript.
References
Lepanto L, Paré G, Aubry D, Robillard P, Lesage J. Impact of PACS on dictation turnaround time and productivity. J Digit Imaging. 2006 Mar;19(1):92–7.
Reiner BI, Siegel EL, Siddiqui K. Evolution of the digital revolution: a radiologist perspective. J Digit Imaging. 2003 Dec;16(4):324–30.
Chang CA, Strahan R, Jolley D. Non-clinical errors using voice recognition dictation software for radiology reports: a retrospective audit. J Digit Imaging. 2011 Aug;24(4):724–8.
Degnan AJ, Ghobadi EH, Hardy P, Krupinski E, Scali EP, Stratchko L, et al. Perceptual and Interpretive Error in Diagnostic Radiology-Causes and Potential Solutions. Acad Radiol. 2019 Jun;26(6):833–45.
McGrath AL, Dodelzon K, Awan OA, Said N, Bhargava P. Optimizing radiologist productivity and efficiency: Work smarter, not harder. Eur J Radiol. 2022 Oct;155:110131.
Doshi AM, Moore WH, Kim DC, Rosenkrantz AB, Fefferman NR, Ostrow DL, et al. Informatics solutions for driving an effective and efficient radiology practice. Radiographics. 2018 Oct;38(6):1810–22.
Dragoni N, Giallorenzo S, Lafuente AL, Mazzara M, Montesi F, Mustafin R, et al. Microservices: yesterday, today, and tomorrow. In: Mazzara M, Meyer B, editors. Present and ulterior software engineering. Cham: Springer International Publishing; 2017. p. 195–216.
Kan SH. Metrics and Models in Software Quality Engineering. 2nd ed. Boston: Addison-Wesley Professional; 2002.
Taghizadegan S: Essentials of Lean Six Sigma, Amsterdam, NL: Elsevier Science; 2010.
Harrison NB, Avgeriou P, Zdun U. On the impact of fault tolerance tactics on architecture patterns. Proceedings of the 2nd International Workshop on Software Engineering for Resilient Systems. New York, NY, USA: ACM 2010 Apr 15;12–21. https://doi.org/10.1145/2401736.2401738.
Bass L, Clements P, Kazman R. Software Architecture in Practice. Subsequent. Boston: Addison-Wesley Professional; 2003.
Minn MJ, Zandieh AR, Filice RW. Improving radiology report quality by rapidly notifying radiologist of report errors. J Digit Imaging. 2015 Aug;28(4):492–8.
Liao GJ, Nagy PG, Cook TS. The impact of imaging informatics fellowships. J Digit Imaging. 2016 Aug;29(4):438–42.
Lindsköld L, Wintell M, Lundberg N. Pitfalls in radiology informatics when deploying an enterprise solution. In: Liu BJ, Boonn WW, editors. Medical Imaging 2010: Advanced PACS-based Imaging Informatics and Therapeutic Applications. SPIE. 2010 Mar 11;76280Q. https://doi.org/10.1117/12.843670.
Kalra A, Chakraborty A, Fine B, Reicher J. Machine learning for automation of radiology protocols for quality and efficiency improvement. J Am Coll Radiol. 2020 Sep;17(9):1149–58.
Juluru K, Shih H-H, Keshava Murthy KN, Elnajjar P, El-Rowmeim A, Roth C, et al. Integrating Al Algorithms into the Clinical Workflow. Radiol Artif Intell. 2021 Nov;3(6):e210013.
Acknowledgements
The authors thank the Radiology IT staff and informatics team for their commitment and support.
Funding
The research of this department is in part funded by the NIH/NCI Cancer Center Support Grant P30 CA008748.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. All authors were involved in designing and refining the miniservices presented in this paper. The first draft of the manuscript was written by ASB, and all authors commented on previous versions of the manuscript.
Corresponding author
Ethics declarations
Ethics Approval
Not applicable.
Consent to Participate
Not applicable.
Consent for Publication
All authors read and approved the final manuscript. On behalf of all authors, the corresponding author states that there is no conflict of interest.
Competing Interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Becker, A.S., Chaim, J. & Vargas, H.A. Streamlining Radiology Workflows Through the Development and Deployment of Automated Microservices. J Digit Imaging. Inform. med. 37, 945–951 (2024). https://doi.org/10.1007/s10278-024-01034-9
Received:
Revised:
Accepted:
Published:
Version of record:
Issue date:
DOI: https://doi.org/10.1007/s10278-024-01034-9