Advertisement

European Radiology

, Volume 27, Issue 5, pp 1934–1943 | Cite as

Usage of structured reporting in radiological practice: results from an Italian online survey

  • Lorenzo Faggioni
  • Francesca Coppola
  • Riccardo Ferrari
  • Emanuele Neri
  • Daniele Regge
Computer Applications

Abstract

Objectives

To assess the opinion on structured reporting (SR) and its usage by radiologist members of the Italian Society of Medical Radiology (SIRM) via an online survey.

Methods

All members received an email invitation to join the survey as an initiative by the SIRM Imaging Informatics Chapter. The survey included 10 questions about demographic information, definition of radiological SR, its usage in everyday practice, perceived advantages and disadvantages over conventional reporting and overall opinion about SR.

Results

1159 SIRM members participated in the survey. 40.3 % of respondents gave a correct definition of radiological SR, but as many as 56 % of them never used it at work. Compared with conventional reporting, the most appreciated advantages of SR were higher reproducibility (70.5 %), better interaction with referring clinicians (58.3 %) and the option to link metadata (36.7 %). Risk of excessive simplification (59.8 %), template rigidity (56.1 %) and poor user compliance (42.1 %) were the most significant disadvantages. Overall, most respondents (87.0 %) were in favour of the adoption of radiological SR.

Conclusions

Most radiologists were interested in radiological SR and in favour of its adoption. However, concerns about semantic, technical and professional issues limited its diffusion in real working life, encouraging efforts towards improved SR standardisation and engineering.

Key Points

Despite radiologists’ awareness, radiological SR is little used in working practice.

Perceived SR advantages are reproducibility, better clinico-radiological interaction and link to metadata.

Perceived SR disadvantages are excessive simplification, template rigidity and poor user compliance.

Improved standardisation and engineering may be helpful to boost SR diffusion.

Keywords

Structured reporting Conventional reporting DICOM Template RIS/PACS 

Notes

Acknowledgments

The scientific guarantor of this publication is Prof. Daniele Regge, MD. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. No complex statistical methods were necessary for this paper. Institutional review board approval was not required because this paper shows the results of a survey and did not involve human or animal subjects. Methodology: online survey (multicentre study).

Compliance with ethical standards

Conflict of interest

None

References

  1. 1.
    Kalender WA, Quick HH (2011) Recent advances in medical physics. Eur Radiol 21:501–504CrossRefPubMedGoogle Scholar
  2. 2.
    Thrall JH (2014) Appropriateness and imaging utilization: "computerized provider order entry and decision support". Acad Radiol 21:1083–1087CrossRefPubMedGoogle Scholar
  3. 3.
    Hussein R, Engelmann U, Schroeter A, Meinzer HP (2004) DICOM structured reporting: Part 2. Problems and challenges in implementation for PACS workstations. Radiographics 24:897–909CrossRefPubMedGoogle Scholar
  4. 4.
    Schwartz LH, Panicek DM, Berk AR, Li Y, Hricak H (2011) Improving communication of diagnostic radiology findings through structured reporting. Radiology 260:174–181CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Larson DB, Towbin AJ, Pryor RM, Donnelly LF (2013) Improving consistency in radiology reporting through the use of department-wide standardized structured reporting. Radiology 267:240–250CrossRefPubMedGoogle Scholar
  6. 6.
    Brook OR, Brook A, Vollmer CM, Kent TS, Sanchez N, Pedrosa I (2015) Structured reporting of multiphasic CT for pancreatic cancer: potential effect on staging and surgical planning. Radiology 274:464–472CrossRefPubMedGoogle Scholar
  7. 7.
    Clunie DA (2000) DICOM structured reporting. PixelMed, BangorGoogle Scholar
  8. 8.
    Kahn CE Jr, Langlotz CP, Burnside ES et al (2009) Toward best practices in radiology reporting. Radiology 252:852–856CrossRefPubMedGoogle Scholar
  9. 9.
    Noumeir R (2006) Benefits of the DICOM structured report. J Digit Imaging 19:295–306CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Bosmans JM, Peremans L, Menni M, De Schepper AM, Duyck PO, Parizel PM (2012) Structured reporting: if, why, when, how-and at what expense? Results of a focus group meeting of radiology professionals from eight countries. Insights Imaging 3:295–302CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Bosmans JM, Neri E, Ratib O, Kahn CE Jr (2015) Structured reporting: a fusion reactor hungry for fuel. Insights Imaging 6:129–132CrossRefPubMedGoogle Scholar
  12. 12.
    Margolies LR, Pandey G, Horowitz ER, Mendelson DS (2016) Breast imaging in the era of big data: structured reporting and data mining. AJR Am J Roentgenol 206:259–264CrossRefPubMedGoogle Scholar
  13. 13.
    Karim S, Fegeler C, Boeckler D, H Schwartz L, Kauczor HU, von Tengg-Kobligk H (2013) Development, implementation, and evaluation of a structured reporting web tool for abdominal aortic aneurysms. JMIR Res Protoc 2, e30CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Barbosa F, Maciel LM, Vieira EM, Azevedo Marques PM, Elias J, Muglia VF (2010) Radiological reports: a comparison between the transmission efficiency of information in free text and in structured reports. Clinics 65:15–21CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Marcovici PA, Taylor GA (2014) Journal Club: structured radiology reports are more complete and more effective than unstructured reports. AJR Am J Roentgenol 203:1265–1271CrossRefPubMedGoogle Scholar
  16. 16.
    Durack JC (2014) The value proposition of structured reporting in interventional radiology. AJR Am J Roentgenol 203:734–738CrossRefPubMedGoogle Scholar
  17. 17.
    Weiss DL, Langlotz CP (2008) Structured reporting: patient care enhancement or productivity nightmare? Radiology 249:739–747CrossRefPubMedGoogle Scholar
  18. 18.
    RadLex (2016) https://www.radlex.org. Accessed 11 Jun 2016
  19. 19.
    European Society of Radiology (2013) ESR communication guidelines for radiologists. Insights Imaging 4:143–146CrossRefGoogle Scholar
  20. 20.
    Baron RL (2014) The radiologist as interpreter and translator. Radiology 272:4–8CrossRefPubMedGoogle Scholar
  21. 21.
    Vaché T, Bratan F, Mège-Lechevallier F, Roche S, Rabilloud M, Rouvière O (2014) Characterization of prostate lesions as benign or malignant at multiparametric MR imaging: comparison of three scoring systems in patients treated with radical prostatectomy. Radiology 272:446–455CrossRefPubMedGoogle Scholar
  22. 22.
    Winter TC (2015) The propaedeutics of structured reporting. Radiology 275:309–310CrossRefPubMedGoogle Scholar
  23. 23.
    Reiner BI (2012) Optimizing technology development and adoption in medical imaging using the principles of innovation diffusion, part II: practical applications. J Digit Imaging 25:7–10CrossRefPubMedGoogle Scholar
  24. 24.
    Coppola F, Bibbolino C, Grassi R et al (2016) Results of an Italian survey on teleradiology. Radiol Med. doi: 10.1007/s11547-016-0640-7
  25. 25.
    Ranschaert ER, Binkhuysen FH (2013) European teleradiology now and in the future: results of an online survey. Insights Imaging 4:93–102CrossRefPubMedGoogle Scholar
  26. 26.
    Travis AR, Sevenster M, Ganesh R, Peters JF, Chang PJ (2014) Preferences for structured reporting of measurement data: an institutional survey of medical oncologists, oncology registrars, and radiologists. Acad Radiol 21:785–796CrossRefPubMedGoogle Scholar
  27. 27.
    Johnson AJ, Chen MY, Swan JS, Applegate KE, Littenberg B (2009) Cohort study of structured reporting compared with conventional dictation. Radiology 253:74–80CrossRefPubMedGoogle Scholar
  28. 28.
    Langlotz CP (2009) Structured radiology reporting: are we there yet? Radiology 253:23–25CrossRefPubMedGoogle Scholar
  29. 29.
    Sistrom CL, Langlotz CP (2005) A framework for improving radiology reporting. J Am Coll Radiol 2:159–167CrossRefPubMedGoogle Scholar
  30. 30.
    Wang S, Pavlicek W, Roberts CC et al (2011) An automated DICOM database capable of arbitrary data mining (including radiation dose indicators) for quality monitoring. J Digit Imaging 24:223–233CrossRefPubMedGoogle Scholar
  31. 31.
    Lauretti DL, Neri E, Faggioni L, Paolicchi F, Caramella D, Bartolozzi C (2015) Automated contrast medium monitoring system for computed tomography—intra-institutional audit. Comput Med Imaging Graph 46(Pt 2):209–218CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Lorenzo Faggioni
    • 1
  • Francesca Coppola
    • 2
  • Riccardo Ferrari
    • 3
  • Emanuele Neri
    • 4
  • Daniele Regge
    • 5
  1. 1.UO Radiodiagnostica 1, Department of Diagnostic and Interventional RadiologyUniversity of PisaPisaItaly
  2. 2.Malpighi Radiology Unit, Department of Diagnostic and Preventive MedicineSant’Orsola-Malpighi University HospitalBolognaItaly
  3. 3.UOC Diagnostica per Immagini 1Urgenza EmergenzaRomeItaly
  4. 4.Sezione Dipartimentale Radiodiagnostica 3, Azienda Ospedaliera Universitaria PisanaDipartimento di Ricerca Traslazionale, Università di PisaPisaItaly
  5. 5.Candiolo Cancer Institute - FPO, IRCCSUniversità di Torino, Dipartimento di Scienze ChirurgicheCandiolo (To)Italy

Personalised recommendations