Home The Clinical Impact of Resident-attending Discrepancies in On-call Radiology Reporting
Post
Cancel

The Clinical Impact of Resident-attending Discrepancies in On-call Radiology Reporting

Rationale and Objectives

The purpose of this study is to quantify the clinical impact of resident-attending discrepancies at a tertiary referral academic radiology residency program by assessing rates of intervention, discrepancy confirmation, recall rate, and management change rate; furthermore, a discrepancy categorization system will be assessed.

Materials and Methods

Retrospective review of the records was performed for n = 1482 discrepancies that occurred in the 17-month study period to assess the clinical impact of discrepancies. Discrepancies were grouped according to a previously published classification system. Management changes were recorded and grouped by severity. The recall rate was estimated for discharged patients. Any confirmatory testing was reviewed to evaluate the accuracy of the discrepant report. Categorical variables were compared to the chi-square test.

Results

The 1482 discrepancies led to management change in 661 cases (44.6%). The most common management change was follow-up imaging. Procedural interventions including surgery occurred in 50 cases (3.3%). The recall rate was 2.6%. Management changes were more severe with computed tomography examinations, inpatients, and when the discrepancy was in the chest and abdomen subspecialty. Also, management changes correlated with the discrepancy category assigned by the attending at the time of review.

Conclusions

Resident-attending discrepancies do cause management changes in 44.6% of discrepancies (0.62% overall); the most frequent change is follow-up imaging. The discrepancy categorization assigned by the attending correlated with the severity of management change.

Rationale and Objectives

A fundamental concept in radiology residency training is the development of independence in a paradigm of graded responsibility. In the past, residents obtained this experience working nights for the practice in which their residency was embedded. Although this model persists in many departments, there is an increasing drive to a 24-hour coverage model with an attending radiologist, even subspecialty radiologist, providing direct resident supervision and final signing throughout the night. This model poses a challenge to fostering independence in residency, particularly in the early years of training.

One of the strongest arguments for the 24-hour attending coverage is the need to avoid resident-attending discrepancies that lead to significant changes in management, particularly for discharged or critically ill patients. Although this justification is inherently reasonable, it is easy to overemphasize its importance when the true impact of discrepancies is not well defined. Previous studies have determined what the discrepancy rates are in several resident practice environments . Others have shown that discrepancy rates are correlated with duration of shift and vary with different imaging study types .

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Materials and Methods

Case Selection

Get Radiology Tree app to read full this article<

Discrepancy Analysis

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

TABLE 1

Description and Total Numbers of Clinical Management Changes Categorized and Grouped by Severity

Management Change Severity Class Number of Cases Therapeutic intervention 3 81 Medication change 102 Diagnostic intervention 31 Follow-up imaging 2 282 Discharge delay/admission/change in level of care 6 Consult or clinic visit 25 Stopped workup 1 9 Physical examination 21 Laboratory examination 17 Called patient or doctor 87 No change in management 821 Total 1482

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Statistical Methods

Get Radiology Tree app to read full this article<

Results

Case Selection

Get Radiology Tree app to read full this article<

Discrepancy Classification

Get Radiology Tree app to read full this article<

TABLE 2

Modality and Study Types of the Discrepant Studies

Modality Study Type_n_ Total CT CT abdomen pelvis 491 1013 (68.4%) CT chest 211 CT head 146 CT angiogram head/neck 29 CT spine 57 CT face/sinuses 33 CT MSK 8 Consult 38 CR CXR 264 469 (31.6%) CR MSK 191 CR abdomen 14 Total 1482

CR, computed radiography; CT, computed tomography; CXR, chest x-ray; MSK, musculoskeletal.

TABLE 3

(a) Ten Most Frequent Organs with Discrepancies; (b) Ten Most Frequent Discrepant Diagnoses

(a) Organ Number of Discrepancies Lung 271 Brain 120 Colon 84 Small bowel 74 Kidney 51 Pulmonary artery 43 Rib 36 Cervical spine 34 Peritoneum 31 Thoracic spine 29

(b) Diagnosis Number of Discrepancies Fracture 130 Pneumonia 104 Pulmonary nodule 54 Compression fracture 37 Pulmonary embolism 33 Brain infarct 28 Colitis 25 Obstruction 23 Aneurysm 18 Subdural hematoma 18

Get Radiology Tree app to read full this article<

Management Changes

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

TABLE 4

Discrepancy Frequencies Grouped by Management Change Group and Subgrouped by Modality, Type of Error, Discrepancy Class Location and Severity, Attending Subspecialty, and Overread Subspecialty

(a) Management Change Group Modality Type of Error Discrepancy Class—Location Discrepancy Class—Severity CR CT Observation Interpretation Discharged Admitted In ED a (Critical) b (Time Dependent) c (Non-time Dependent) 1 (Mild) 49 87 95 41 94 32 10 2 51 83 2 (Moderate) 115 196 224 87 60 193 58 5 175 131 3 (Severe) 54 160 150 64 35 139 40 5 158 51 Total 218 443 469 192 189 364 108 12 384 265P -value .013 .848 <.001 <.001

(b) Management Change Group Attending Subspecialty Overread Subspecialty Abdo Chest MSK Neuro Abdo Chest MSK Neuro 1 (Mild) 58 45 12 21 55 25 30 26 2 (Moderate) 110 122 9 70 78 114 43 76 3 (Severe) 76 101 5 32 81 74 24 35 Total 244 268 26 123 214 213 97 137P -value .002 <.001

Abdo, abdomen; CR, computed radiography; CT, computed tomography; ED, emergency department; MSK, musculoskeletal.

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Review Latency

Get Radiology Tree app to read full this article<

Recall Rate

Get Radiology Tree app to read full this article<

Attending Performance

Get Radiology Tree app to read full this article<

Discussion

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Get Radiology Tree app to read full this article<

Conclusions

Get Radiology Tree app to read full this article<

References

  • 1. Blane C.E., Desmond J.S., Helvie M.A., et. al.: Academic radiology and the emergency department: does it need changing?. Acad Radiol 2007; 14: pp. 625-630.

  • 2. Cooper V.F., Goodhartz L.A., Nemcek A.A., et. al.: Radiology resident interpretations of on-call imaging studies: the incidence of major discrepancies. Acad Radiol 2008; 15: pp. 1198-1204.

  • 3. Tieng N., Grinberg D., Li S.F.: Discrepancies in interpretation of ED body computed tomographic scans by radiology residents. Am J Emerg Med 2007; 25: pp. 45-48.

  • 4. Ruutiainen A.T., Durand D.J., Scanlon M.H., et. al.: Increased error rates in preliminary reports issued by radiology residents working more than 10 consecutive hours overnight. Acad Radiol 2013; 20: pp. 305-311.

  • 5. Filippi C.G., Schneider B., Burbank H.N., et. al.: Discrepancy rates of radiology resident interpretations of on-call neuroradiology MR imaging studies. Radiology 2008; 249: pp. 972-979.

  • 6. Kung J.W., Melenevsky Y., Hochman M.G., et. al.: On-call musculoskeletal radiographs: discrepancy rates between radiology residents and musculoskeletal radiologists. Am J Roentgenol 2013; 200: pp. 856-859.

  • 7. Ruchman R.B., Jaeger J., Wiggins E.F., et. al.: Preliminary radiology resident interpretations versus final attending radiologist interpretations and the impact on patient care in a community hospital. Am J Roentgenol 2007; 189: pp. 523-526.

  • 8. Friedman S.M., Merman E., Chopra A.: Clinical impact of diagnostic imaging discrepancy by radiology trainees in an urban teaching hospital emergency department. Int J Emerg Med 2013; 6: pp. 24.

  • 9. Bruni S.G., Bartlett E., Yu E.: Factors involved in discrepant preliminary radiology resident interpretations of neuroradiological imaging studies: a retrospective analysis. Am J Roentgenol 2012; 198: pp. 1367-1374.

  • 10. Carney E., Kempf J., DeCarvalho V., et. al.: Preliminary interpretations of after-hours CT and sonography by radiology residents versus final interpretations by body imaging radiologists at a level 1 trauma center. Am J Roentgenol 2003; 181: pp. 367-373.

  • 11. Meyer R.E., Nickerson J.P., Burbank H.N., et. al.: Discrepancy rates of on-call radiology residents’ interpretations of CT angiography studies of the neck and circle of Willis. Am J Roentgenol 2009; 193: pp. 527-532.

  • 12. Mellnick V., Raptis C., McWilliams S., et. al.: On-call radiology resident discrepancies: categorization by patient location and severity. J Am Coll Radiol 2016; 13: pp. 1233-1238.

  • 13. Jackson V.P., Cushing T., Abujudeh H.H., et. al.: RADPEER scoring white paper. J Am Coll Radiol 2009; 6: pp. 21-25.

  • 14. Ruutiainen A.T., Scanlon M.H., Itri J.N.: Identifying benchmarks for discrepancy rates in preliminary interpretations provided by radiology trainees at an academic institution. J Am Coll Radiol 2011; 8: pp. 644-648.

  • 15. Branstetter B.F., Morgan M.B., Nesbit C.E., et. al.: Preliminary reports in the emergency department: is a subspecialist radiologist more accurate than a radiology resident?. Acad Radiol 2007; 14: pp. 201-206.

  • 16. Erly W.K., Berger W.G., Krupinski E., et. al.: Radiology resident evaluation of head CT scan orders in the emergency department. AJNR Am J Neuroradiol 2002; 23: pp. 103-107.

  • 17. Eakins C., Ellis W.D., Pruthi S., et. al.: Second opinion interpretations by specialty radiologists at a pediatric hospital: rate of disagreement and clinical implications. Am J Roentgenol 2012; 199: pp. 916-920.

  • 18. Zan E., Yousem D.M., Carone M., et. al.: Second-opinion consultations in neuroradiology. Radiology 2010; 255: pp. 135-141.

  • 19. Lauritzen P.M., Andersen J.G., Stokke M.V., et. al.: Radiologist-initiated double reading of abdominal CT: retrospective analysis of the clinical importance of changes to radiology reports. BMJ Qual Saf 2016; 25: pp. 595-603.

  • 20. Maloney E., Lomasney L.M., Schomer L.: Application of the RADPEER scoring language to interpretation discrepancies between diagnostic radiology residents and faculty radiologists. J Am Coll Radiol 2012; 9: pp. 264-269.

  • 21. Lal N.R., Murray U.M., Eldevik O.P., et. al.: Clinical consequences of misinterpretations of neuroradiologic CT scans by on-call radiology residents. AJNR Am J Neuroradiol 2000; 21: pp. 124-129.

  • 22. Stevens K.J., Griffiths K.L., Rosenberg J., et. al.: Discordance rates between preliminary and final radiology reports on cross-sectional imaging studies at a level 1 trauma center. Acad Radiol 2008; 15: pp. 1217-1226.

  • 23. Sistrom C., Deitte L.: Factors affecting attending agreement with resident early readings of computed tomography and magnetic resonance imaging of the head, neck, and spine. Acad Radiol 2008; 15: pp. 934-941.

  • 24. Chung J.H., Strigel R.M., Chew A.R., et. al.: Overnight resident interpretation of torso CT at a level 1 trauma center an analysis and review of the literature. Acad Radiol 2009; 16: pp. 1155-1160.

  • 25. Ruma J., Klein K.A., Chong S., et. al.: 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 2011; 8: pp. 409-414.

This post is licensed under CC BY 4.0 by the author.