The Fragile Chain: A Narrative Review of Communication Breakdown in Time-Sensitive Diagnoses Across the ED-Radiology-Inpatient Continuum
Abstract
Background: In time-sensitive medical conditions such as acute stroke, aortic dissection, and major trauma, diagnostic delays of minutes can drastically alter patient outcomes. The diagnostic pathway is a high-stakes relay involving multiple handoffs: from emergency department (ED) nursing and physicians, to radiographers, to radiologists, and finally to inpatient or interventional teams. Failures in communication at any point in this chain are a major source of preventable diagnostic error and patient harm. Aim: This narrative review aims to synthesize evidence on the critical communication pathways, vulnerabilities, and enabling strategies for handoffs from the ED through radiology to definitive inpatient care for time-sensitive diagnoses. Methods: A comprehensive literature search was conducted in PubMed, CINAHL, Scopus, and Web of Science (2010-2024). Results: The review identifies systemic vulnerabilities at each handoff: incomplete clinical information provided with imaging orders, ambiguous verbal communication, inefficient report dissemination, and failures in critical result notification. It highlights the pivotal but often overlooked roles of the radiographer as a situational communicator and the medical secretary as an information flow expediter. While health information systems like critical result alerts offer solutions, they often generate alert fatigue and can be circumvented. Conclusion: Safeguarding time-sensitive diagnoses requires a systems-engineering approach that hardwires communication protocols, formally recognizes the communicative roles of all team members (including radiographers and secretaries), and optimizes health information technology to support, not hinder, the cognitive and collaborative work of diagnosis.
Full text article
References
1. Adjei, N. B. (2022). Implementing TeamSTEPPS® training: Using evidence to impact teamwork on a medical-surgical unit. Medsurg Nursing, 31(1), 9-12.
2. Aluvalu, R., Mudrakola, S., Kaladevi, A. C., Sandhya, M. V. S., & Bhat, C. R. (2023). The novel emergency hospital services for patients using digital twins. Microprocessors and Microsystems, 98, 104794. https://doi.org/10.1016/j.micpro.2023.104794
3. Bruno, M. A., Walker, E. A., & Abujudeh, H. H. (2015). Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics, 35(6), 1668-1676. https://doi.org/10.1148/rg.2015150023
4. Bucholz, E. M., Normand, S. L. T., Wang, Y., Ma, S., Lin, H., & Krumholz, H. M. (2015). Life expectancy and years of potential life lost after acute myocardial infarction by sex and race: a cohort-based study of Medicare beneficiaries. Journal of the American College of Cardiology, 66(6), 645-655. https://doi.org/10.1016/j.jacc.2015.06.022
5. Callen, J., Georgiou, A., Li, J., & Westbrook, J. I. (2015). The impact for patient outcomes of failure to follow up on test results. How can we do better?. EJIFCC, 26(1), 38.
6. Calvo, F. A., Chera, B. S., Zubizarreta, E., Scalliet, P., Prasad, R. R., Quarneti, A., ... & Abdel-Wahab, M. (2020). The role of the radiation oncologist in quality and patient safety: A proposal of indicators and metrics. Critical reviews in oncology/hematology, 154, 103045. https://doi.org/10.1016/j.critrevonc.2020.103045
7. Coyner, A. S., Chen, J. S., Chang, K., Singh, P., Ostmo, S., Chan, R. P., ... & Imaging and Informatics in Retinopathy of Prematurity Consortium. (2022). Synthetic medical images for robust, privacy-preserving training of artificial intelligence: application to retinopathy of prematurity diagnosis. Ophthalmology Science, 2(2), 100126. https://doi.org/10.1016/j.xops.2022.100126
8. de Souza Leite, K. F., Dos Santos, S. R., de Paula Andrade, R. L., de Faria, M. G. B. F., Saita, N. M., Arcêncio, R. A., ... & Monroe, A. A. (2022). Reducing care time after implementing protocols for acute ischemic stroke: a systematic review. Arquivos de Neuro-psiquiatria, 80(07), 725-740. DOI: 10.1055/s-0042-1755194
9. Dreizin, D., Staziaki, P. V., Khatri, G. D., Beckmann, N. M., Feng, Z., Liang, Y., ... & Fu, Y. (2023). Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel. Emergency radiology, 30(3), 251-265. https://doi.org/10.1007/s10140-023-02120-1
10. Fonarow, G. C., Smith, E. E., Saver, J. L., Reeves, M. J., Bhatt, D. L., Grau-Sepulveda, M. V., ... & Schwamm, L. H. (2011). Timeliness of tissue-type plasminogen activator therapy in acute ischemic stroke: patient characteristics, hospital factors, and outcomes associated with door-to-needle times within 60 minutes. Circulation, 123(7), 750-758. https://doi.org/10.1161/CIRCULATIONAHA.110.974675
11. Graber, M. L., Kissam, S., Payne, V. L., Meyer, A. N., Sorensen, A., Lenfestey, N., ... & Singh, H. (2012). Cognitive interventions to reduce diagnostic error: a narrative review. BMJ quality & safety, 21(7), 535-557. https://doi.org/10.1136/bmjqs-2011-000149
12. Han, X., Lowry, T. Y., Loo, G. T., Rabin, E. J., Grinspan, Z. M., Kern, L. M., ... & Shapiro, J. S. (2019). Expanding health information exchange improves identification of frequent emergency department users. Annals of Emergency Medicine, 73(2), 172-179. https://doi.org/10.1016/j.annemergmed.2018.07.024
13. Hussain, F., Cooper, A., Carson-Stevens, A., Donaldson, L., Hibbert, P., Hughes, T., & Edwards, A. (2019). Diagnostic error in the emergency department: learning from national patient safety incident report analysis. BMC emergency medicine, 19(1), 77. https://doi.org/10.1186/s12873-019-0289-3
14. Kamal, N., Aljendi, S., Carter, A., Cora, E. A., Chandler, T., Clift, F., ... & ACTEAST Collaborators. (2022). Improving access and efficiency of ischemic stroke treatment across four Canadian provinces using a stepped wedge trial: Methodology. Frontiers in Stroke, 1, 1014480. https://doi.org/10.3389/fstro.2022.1014480
15. Kohli, M. D., Summers, R. M., & Geis, J. R. (2017). Medical image data and datasets in the era of machine learning—whitepaper from the 2016 C-MIMI meeting dataset session. Journal of digital imaging, 30(4), 392-399. https://doi.org/10.1007/s10278-017-9976-3
16. Krumholz, H. M. (2020). Inflection point: ideas for accelerating breakthroughs and improving cardiovascular health. Circulation: Cardiovascular Quality and Outcomes, 13(12), e007615. https://doi.org/10.1161/CIRCOUTCOMES.120.007615
17. Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., ... & Kandarpa, K. (2019). A roadmap for foundational research on artificial intelligence in medical imaging: from the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology, 291(3), 781-791. https://doi.org/10.1148/radiol.2019190613
18. Larson, D. B., Froehle, C. M., Johnson, N. D., & Towbin, A. J. (2014). Communication in diagnostic radiology: meeting the challenges of complexity. American Journal of Roentgenology, 203(5), 957-964. https://doi.org/10.2214/AJR.14.12949
19. Moore, Q. T., Walker, D. A., Frush, D. P., Daniel, M., & Pavkov, T. W. (2022). Intrapersonal and Institutional Influences On Overall Perception of Radiation Safety Among Radiologic Technologists. Radiologic Technology, 93(3).
20. Malik, M. A., Motta-Calderon, D., Piniella, N., Garber, A., Konieczny, K., Lam, A., ... & Dalal, A. K. (2022). A structured approach to EHR surveillance of diagnostic error in acute care: an exploratory analysis of two institutionally-defined case cohorts. Diagnosis, 9(4), 446-457. https://doi.org/10.1515/dx-2022-0032
21. Man, S., Solomon, N., Mac Grory, B., Alhanti, B., Uchino, K., Saver, J. L., ... & Fonarow, G. C. (2023). Shorter door-to-needle times are associated with better outcomes after intravenous thrombolytic therapy and endovascular thrombectomy for acute ischemic stroke. Circulation, 148(1), 20-34. https://doi.org/10.1161/CIRCULATIONAHA.123.064053
22. Müller, M., Jürgens, J., Redaèlli, M., Klingberg, K., Hautz, W. E., & Stock, S. (2018). Impact of the communication and patient hand-off tool SBAR on patient safety: a systematic review. BMJ open, 8(8), e022202. https://doi.org/10.1136/bmjopen-2018-022202
23. Nobel, J. M., van Geel, K., & Robben, S. G. (2022). Structured reporting in radiology: a systematic review to explore its potential. European radiology, 32(4), 2837-2854. https://doi.org/10.1007/s00330-021-08327-5
24. Pierre, K., Haneberg, A. G., Kwak, S., Peters, K. R., Hochhegger, B., Sananmuang, T., ... & Forghani, R. (2023, April). Applications of artificial intelligence in the radiology roundtrip: process streamlining, workflow optimization, and beyond. In Seminars in Roentgenology (Vol. 58, No. 2, pp. 158-169). WB Saunders. https://doi.org/10.1053/j.ro.2023.02.003
25. Pons, E., Braun, L. M., Hunink, M. M., & Kors, J. A. (2016). Natural language processing in radiology: a systematic review. Radiology, 279(2), 329-343. https://doi.org/10.1148/radiol.16142770
26. Powell, D. K., & Silberzweig, J. E. (2015). State of structured reporting in radiology, a survey. Academic radiology, 22(2), 226-233. https://doi.org/10.1016/j.acra.2014.08.014
27. Powers, W. J., Rabinstein, A. A., Ackerson, T., Adeoye, O. M., Bambakidis, N. C., Becker, K., ... & American Heart Association Stroke Council. (2019). Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke, 50(12), e344-e418. https://doi.org/10.1161/STR.0000000000000211
28. Rockall, A. G., Justich, C., Helbich, T., & Vilgrain, V. (2022). Patient communication in radiology: moving up the agenda. European Journal of Radiology, 155, 110464. https://doi.org/10.1016/j.ejrad.2022.110464
29. Royuela, A., Abad, C., Vicente, A., Muriel, A., Romera, R., Fernandez-Felix, B. M., ... & Zamora, J. (2019). Implementation of a computerized decision support system for computed tomography scan requests for nontraumatic headache in the emergency department. The Journal of Emergency Medicine, 57(6), 780-790. https://doi.org/10.1016/j.jemermed.2019.08.026
30. Singh, H., Meyer, A. N., & Thomas, E. J. (2014). The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations. BMJ quality & safety, 23(9), 727-731. https://doi.org/10.1136/bmjqs-2013-002627
31. Weber, T. F., Spurny, M., Hasse, F. C., Sedlaczek, O., Haag, G. M., Springfeld, C., ... & Berger, A. K. (2020). Improving radiologic communication in oncology: a single-centre experience with structured reporting for cancer patients. Insights into Imaging, 11(1), 106. https://doi.org/10.1186/s13244-020-00907-1
32. Zaboli, R., Malmoon, Z., Soltani-Zarandi, M. R., & Hassani, M. (2018). Factors affecting sentinel events in hospital emergency department: a qualitative study. International Journal of Health Care Quality Assurance, 31(6), 575-586. https://doi.org/10.1108/IJHCQA-07-2017-0137
33. Zygmont, M. E., Itri, J. N., Rosenkrantz, A. B., Duong, P. A. T., Gettle, L. M., Mendiratta-Lala, M., ... & Kadom, N. (2017). Radiology research in quality and safety: current trends and future needs. Academic radiology, 24(3), 263-272. https://doi.org/10.1016/j.acra.2016.07.021
Authors
Copyright (c) 2024 Sarah Farhan Alenizi, Fawzia Zayed Eid Al-Mutairi, Abidah Abed Obaidallah Alabsi, Yousef Saif W Alotaibi, Aljoharah Ghazi Almarzoghi, Asmaa Anwar Shehatah, Jamaan Bakheet Abdullah Aldossari, Nawal Yahya Ali Zaylaee, Raed Ateeq Talmas Alotaibi, Tirad Rakid Mushih Alruweaili, Nasser Hamid Aljohani, Amnah Mohammed Ali Talbi, WASIF SALEEM SALAMAH ALOLASI, Nasser Fandi Sumayhan Alanazi
This work is licensed under a Creative Commons Attribution 4.0 International License.