A Novel Radiology Communication Tool to Reduce Workflow Interruptions: Clinical Evaluation of RadConnect.
Merlijn Sevenster, Kenneth F M Hergaarden, Omar Hertgers, Natalie H M Kruithof, Joost J H Roelofs, Jessica C Foster-Dingley, Stephan R Romeijn, Duy Duc Nguyen, Sandra Vosbergen, Hildo J Lamb
Author Information
Merlijn Sevenster: Royal Philips Electronics, High Tech Campus 34, 5656AE, Eindhoven, The Netherlands. merlijn.sevenster@philips.com. ORCID
Kenneth F M Hergaarden: Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Omar Hertgers: Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Natalie H M Kruithof: Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Joost J H Roelofs: Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Jessica C Foster-Dingley: Royal Philips Electronics, High Tech Campus 34, 5656AE, Eindhoven, The Netherlands.
Stephan R Romeijn: Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Duy Duc Nguyen: Royal Philips Electronics, High Tech Campus 34, 5656AE, Eindhoven, The Netherlands.
Sandra Vosbergen: Royal Philips Electronics, High Tech Campus 34, 5656AE, Eindhoven, The Netherlands.
Hildo J Lamb: Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.
Despite the importance of communication, radiology departments often depend on communication tools that were not created for the unique needs of imaging workflows, leading to frequent radiologist interruptions. The objective of this study was test the hypothesis that a novel asynchronous communication tool for the imaging workflow (RadConnect) reduces the daily average number of synchronous (in-person, telephone) communication requests for radiologists. We conducted a before-after study. Before adoption of RadConnect, technologists used three conventional communication methods to consult radiologists (in-person, telephone, general-purpose enterprise chat (GPEC)). After adoption, participants used RadConnect as a fourth method. Technologists manually recorded every radiologist consult request related to neuro and thorax CT scans in the 40 days before and 40 days after RadConnect adoption. Telephone traffic volume to section beepers was obtained from the hospital telephone system for the same period. The value and usability experiences were collected through an electronic survey and structured interviews. RadConnect adoption resulted in 53% reduction of synchronous (in-person, telephone) consult requests: from 6.1 ± 4.2 per day to 2.9 ± 2.9 (P < 0.001). There was 77% decrease (P < 0.001) in telephone volume to the neuro and thorax beepers, while no significant volume change was noted to the abdomen beeper (control group). Survey responses (46% response rate) and interviews confirmed the positive impact of RadConnect on interruptions. RadConnect significantly reduced radiologists' telephone interruptions. Study participants valued the role-based interaction and prioritized worklist overview in the survey and interviews. Findings from this study will contribute to a more focused work environment.
J.-P. J. Yu, A. P. Kansagra and J. Mongan, “The Radiologist’s Workflow Environment: Evaluation of Disruptors and Potential Implications,” J Am Coll Radiol, vol. 11, no. 6, pp. 589-593, 2014.
[DOI: 10.1016/j.jacr.2013.12.026]
A. Schemmel, M. Lee, T. Hanley, B. Pooler, T. Kennedy, A. Field, D. Wiegmann and J. Yu, “Radiology Workflow Disruptors: A Detailed Analysis,” J Am Coll Radiol., vol. 13, no. 10, pp. 1210-1214, 2016.
[DOI: 10.1016/j.jacr.2016.04.009]
M. Lee, A. Schemmel, B. Pooler, T. Hanley, T. Kennedy, A. Field, D. Wiegmann and J. Yu, “Radiology Workflow Dynamics: How Workflow Patterns Impact Radiologist Perceptions of Workplace Satisfaction,” Acad Radiol, vol. 24, no. 4, pp. 483-487, 2017.
[DOI: 10.1016/j.acra.2016.08.027]
R. M. Ratwani, E. Wang, A. Fong and C. Cooper, “A Human Factors Approach to Understanding the Types and Sources of Interruptions in Radiology Reading Rooms,” J Am Coll Radiol, pp. Sep;13(9):1102–5, 2016.
S. Sabat, P. Kalapos and E. Slonimsky, “Quantifying disruption of workflow by phone calls to the neuroradiology reading room,” BMJ Open Qual, vol. 8, no. 3, 2019.
B. J. Balint, S. D. Steenburg, H. Lin, C. Shen, J. L. Steele and R. B. Gunderman, “Do Telephone Call Interruptions Have an Impact on Radiology Resident Diagnostic Accuracy?,” Acad Radiol, vol. 21, no. 12, pp. 1623-1628, 2014.
[DOI: 10.1016/j.acra.2014.08.001]
R. Wynn, J. Howe, L. Kelahan, A. Fong, R. Filice and R. Ratwani, “The Impact of Interruptions on Chest Radiograph Interpretation: Effects on Reading Time and Accuracy,” Acad Radiol, vol. 25, no. 12, pp. 1515-1520, 2018.
[DOI: 10.1016/j.acra.2018.03.016]
J. Harolds, J. Parikh, E. Bluth, S. Dutton and M. Recht, “Burnout of Radiologists: Frequency, Risk Factors, and Remedies: A Report of the ACR Commission on Human Resources,” J Am Coll Radiol, vol. 13, no. 4, pp. 411-6, 2016.
[DOI: 10.1016/j.jacr.2015.11.003]
A. Kansagra, K. Liu and J.-P. Yu, “Disruption of Radiologist Workflow,” Curr Probl Diagn Radiol, vol. 45, no. 2, pp. 101-6, 2016.
[DOI: 10.1067/j.cpradiol.2015.05.006]
M. Sevenster, K. Hergaarden, O. Hertgers, D. Nguyen, V. Wijn, A. Vlachomitrou, S. Vosbergen and H. Lamb, “Design and perceived value of a novel solution for asynchronous communication in radiology,” Curr Prob Diagn Radiol, vol. 53, no. 1, pp. 96-101, 2024.
[DOI: 10.1067/j.cpradiol.2023.10.008]
C. Watura, D. Blunt and D. Amiras, “Ring Ring Ring! Characterising Telephone Interruptions During Radiology Reporting and How to Reduce These,” Curr Probl Diagn Radiol, vol. 48, no. 3, pp. 207-209, 2019.
[DOI: 10.1067/j.cpradiol.2018.01.004]
S. Quan, R. Wu, P. Rossos, T. Arany, S. Groe, D. Morra, B. Wong, R. Cavalcanti, W. Coke and F. Lau, “It’s not about pager replacement: an in-depth look at the interprofessional nature of communication in healthcare,” J Hosp Med, vol. 8, no. 3, pp. 137-43, 2013.
[DOI: 10.1002/jhm.2008]
R Core Team, R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing, 2022.
