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Minimizing Barriers in Learning for On-Call Radiology Residents—End-to-End Web-Based Resident Feedback System

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Abstract

Feedback is an essential part of medical training, where trainees are provided with information regarding their performance and further directions for improvement. In diagnostic radiology, feedback entails a detailed review of the differences between the residents’ preliminary interpretation and the attendings’ final interpretation of imaging studies. While the on-call experience of independently interpreting complex cases is important to resident education, the more traditional synchronous “read-out” or joint review is impossible due to multiple constraints. Without an efficient method to compare reports, grade discrepancies, convey salient teaching points, and view images, valuable lessons in image interpretation and report construction are lost. We developed a streamlined web-based system, including report comparison and image viewing, to minimize barriers in asynchronous communication between attending radiologists and on-call residents. Our system provides real-time, end-to-end delivery of case-specific and user-specific feedback in a streamlined, easy-to-view format. We assessed quality improvement subjectively through surveys and objectively through participation metrics. Our web-based feedback system improved user satisfaction for both attending and resident radiologists, and increased attending participation, particularly with regards to cases where substantive discrepancies were identified.

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References

  1. Baker A, Perreault D, Reid A, Blanchard CM: Feedback and organizations: feedback is good, feedback-friendly culture is better. Can Psychol 54(4):260–268, 2013

    Article  Google Scholar 

  2. Baker N: Employee feedback technologies in the human performance system. Hum Resour Dev Int 13(4):477–485, 2010

    Article  Google Scholar 

  3. Raemdonck I, Strijbos J-W: Feedback perceptions and attribution by secretarial employees. Eur J Train Dev 37(1):24–48, 2013

    Article  Google Scholar 

  4. Northcraft GB, Schmidt AM, Ashford SJ: Feedback and the rationing of time and effort among competing tasks. J Appl Psychol 96(5):1076–1086, 2011

    Article  PubMed  Google Scholar 

  5. Bindal T, Wall D, Goodyear HM: Trainee doctors’ views on workplace-based assessments: are they just a tick box exercise? Med Teach 33(11):919–927, 2011

    Article  PubMed  Google Scholar 

  6. Mar C, Chang S, Forster B: Remedial training for the radiology resident: a template for optimization of the learning plan. Acad Radiol 22(2):240–246, 2015

    Article  PubMed  Google Scholar 

  7. Lee S, Baek HJ, Jung HK, Il MJ, Cho SB, Choi BH, Bae K, Jeon KN, Choi DS, Shin HS, Kim DW: Interpretations of diffusion-weighted MR imaging by radiology residents in the emergency department: is diagnostic performance influenced by the level of residency training? Radiol Med 122(1):35–42, 2017

    Article  PubMed  Google Scholar 

  8. Gorniak RJT, Flanders AE, Sharpe RE: Trainee report dashboard: tool for enhancing feedback to radiology trainees about their reports. Radiographics 33(7):2105–2113, 2013

    Article  PubMed  Google Scholar 

  9. Harari AA, Conti MB, Jamal Bokhari SA, Staib LH, Taylor CR: The role of report comparison, analysis, and discrepancy categorization in resident education. Am J Roentgenol 207(6):1223–1231, 2016

    Article  Google Scholar 

  10. Sharpe RE, Surrey D, Gorniak RJT, Nazarian L, Rao VM, Flanders AE: Radiology report comparator: a novel method to augment resident education. J Digit Imaging 25(3):330–336, 2012

    Article  PubMed  Google Scholar 

  11. Kalaria AD, Filice RW: Comparison-Bot: an automated preliminary-final report comparison system. J Digit Imaging 29(3):325–330, 2016

    Article  PubMed  Google Scholar 

  12. Ruchman RB, Jaeger J, Wiggins EF, Seinfeld S, Thakral V, Bolla S, Wallach S: Preliminary radiology resident interpretations versus final attending radiologist interpretations and the impact on patient care in a community hospital. Am J Roentgenol 189(3):523–526, 2007

    Article  Google Scholar 

  13. Ruma J, Klein KA, Chong S, Wesolowski J, Kazerooni EA, Ellis JH, Myles JD: Cross-sectional examination interpretation discrepancies between on-call diagnostic radiology residents and subspecialty faculty radiologists: analysis by imaging modality and subspecialty. J Am Coll Radiol 8(6):409–414, 2011

    Article  PubMed  Google Scholar 

  14. Weinberg BD, Richter MD, Champine JG, Morriss MC, Browning T: Radiology resident preliminary reporting in an independent call environment: multiyear assessment of volume, timeliness, and accuracy. J Am Coll Radiol 12(1):95–100, 2015

    Article  PubMed  Google Scholar 

  15. Itri JN, Kim W, Scanlon MH: Orion: a web-based application designed to monitor resident and fellow performance on-call. J Digit Imaging 24(5):897–907, 2011

    Article  PubMed  PubMed Central  Google Scholar 

  16. Boud D, Molloy E: Rethinking models of feedback for learning: the challenge of design. Assess Eval High Educ 38(6):698–712, 2013

    Article  Google Scholar 

  17. Brown JM, Dickerson EC, Rabinowitz LC, Cohan RH, Ellis JH, Litell JM, Kaza RK, Lopez AN, Theyyunni NR, Weber JT, Kocher KE, Davenport MS: “Concordance” revisited: a multispecialty appraisal of “concordant” preliminary abdominopelvic CT reports. J Am Coll Radiol 13(9):1111–1117, 2016

    Article  PubMed  Google Scholar 

  18. Branstetter, IV BF, Morgan MB, Nesbit CE, Phillips JA, Lionetti DM, Chang PJ, Towers JD: Preliminary reports in the emergency department: is a subspecialist radiologist more accurate than a radiology resident? Acad Radiol 14(2):201–206, 2007

    Article  PubMed  Google Scholar 

  19. Harvey HB, Alkasab TK, Prabhakar AM, Halpern EF, Rosenthal DI, Pandharipande PV, Gazelle GS: Radiologist peer review by group consensus. J Am Coll Radiol 13(6):656–662, 2016

    Article  PubMed  Google Scholar 

  20. Cheng T, Dumire R, Golden S, Gregory J: Impact on patient care of discordance in radiology readings between external overnight radiology services and staff radiology readings at a level 1 trauma center. Am J Surg 205(3):280–283, 2013

    Article  PubMed  Google Scholar 

  21. Borgstede JP, Lewis RS, Bhargavan M, Sunshine JH: RADPEER quality assurance program: A multifacility study of interpretive disagreement rates. J Am Coll Radiol 1(1):59–65, 2004

    Article  PubMed  Google Scholar 

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Correspondence to Hailey H Choi.

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Choi, H.H., Clark, J., Jay, A.K. et al. Minimizing Barriers in Learning for On-Call Radiology Residents—End-to-End Web-Based Resident Feedback System. J Digit Imaging 31, 117–123 (2018). https://doi.org/10.1007/s10278-017-0015-1

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