Standardized Reporting in IR: A Prospective Multi-Institutional Pilot Study

Challenges, Barriers, and Successes of Standardized Report Templates: Results of a Society of Interventional Radiology Survey

Registry data are being increasingly used to establish treatment guidelines, set benchmarks, allocate resources, and make payment decisions. Although many registries rely on manual data entry, the Society of Interventional Radiology (SIR) is using automated data extraction for its VIRTEX registry. This process relies on participants using consistent terminology with highly structured data in physician-developed standardized reports (SR). To better understand barriers to adoption, a survey was sent to 3,178 SIR members. Responses were obtained from 451 interventional radiology practitioners (14.2%) from 92 unique academic and 151 unique private practices. Of these, 75% used structured reports and 32% used the SIR SR. The most common barriers to the use of these reports include SR length (35% of respondents), lack of awareness about the SR (31%), and lack of agreement on adoption within practices (27%). The results demonstrated insights regarding barriers in the use and/or adoption of SR and potential solutions.

A History and Modern Framework for Quality Improvement in Interventional Radiology

Quality improvement (QI) initiatives have benefited patients as well as the broader practice of medicine. Large-scale QI has been facilitated by multi-institutional data registries, many of which were formed out of national or international medical society initiatives. With broad participation, QI registries have provided benefits that include but are not limited to establishing treatment guidelines, facilitating research related to uncommon procedures and conditions, and demonstrating the fiscal and clinical value of procedures for both medical providers and health systems. Because of the benefits offered by these databases, Society of Interventional Radiology (SIR) and SIR Foundation have committed to the development of an interventional radiology (IR) clinical data registry known as VIRTEX. A large IR database with participation from a multitude of practice environments has the potential to have a significant positive impact on the specialty through data-driven advances in patient safety and outcomes, clinical research, and reimbursement. This article reviews the current landscape of societal QI programs, presents a vision for a large-scale IR clinical data registry supported by SIR, and discusses the anticipated results that such a framework can produce.

Objective comparison of errors and report length between structured and freeform abdominopelvic computed tomography reports

PURPOSE

To objectively compare structured and freeform abdominopelvic CT reports based on the number and types of errors as well as report length.

METHODS

90 structured and 89 freeform reports from abdominopelvic CT scans with IV contrast obtained for the indication of abdominal pain were randomly selected for review. Each report was reviewed for errors, which were counted and categorized based on the type of error. The total number of words in each report was tallied.

RESULTS

105 total errors were found in the structured reports, compared to 157 total errors in freeform reports. There were 1.16 errors per structured report and 1.76 errors per freeform report (p < 0.001). 48% of structured reports contained at least one error, while 71% of freeform reports contained at least one error (p = 0.002). When a difference existed between the styles with regard to error categories, more errors were observed in freeform reports, with the exception of the duplicated period error where structured reports had more errors. No difference on the basis of average words per report existed, with 219.2 words per report for each reporting style.

CONCLUSION

The use of structured reporting for abdominopelvic CT results in less errors in the report when compared to freeform reporting, potentially reducing clinically significant adverse outcomes in patient care. The report length on the basis of number of words per report is not different between the two reporting styles.

Deep learning to convert unstructured CT pulmonary angiography reports into structured reports

Background

Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned about resultant decreased workflow efficiency. We tested a machine learning-based algorithm designed to convert unstructured computed tomography pulmonary angiography (CTPA) reports into structured reports.

Methods

A self-supervised convolutional neural network-based algorithm was trained on a dataset of 475 manually structured CTPA reports. Labels for individual statements included “pulmonary arteries,” “lungs and airways,” “pleura,” “mediastinum and lymph nodes,” “cardiovascular,” “soft tissues and bones,” “upper abdomen,” and “lines/tubes.” The algorithm was applied to a test set of 400 unstructured CTPA reports, generating a predicted label for each statement, which was evaluated by two independent observers. Per-statement accuracy was calculated based on strict criteria (algorithm label counted as correct if the statement unequivocally contained content only related to that particular label) and a modified criteria, accounting for problematic statements, including typographical errors, statements that did not fit well into the classification scheme, statements containing content for multiple labels, etc.

Results

Of the 4,157 statements, 3,806 (91.6%) and 3,986 (95.9%) were correctly labeled by the algorithm using strict and modified criteria, respectively, while 274 (6.6%) were problematic for the manual observers to label, the majority of which (n = 173) were due to more than one section being included in one statement.

Conclusion

This algorithm showed high accuracy in converting free-text findings into structured reports, which could improve communication between radiologists and clinicians without loss of productivity and provide more structured data for research/data mining applications.

Structured Reporting of IR Procedures: Effect on Report Compliance, Accuracy, and Satisfaction

PURPOSE

To compare effect of free-text versus structured reporting of IR procedures on report quality and report coding and value.

MATERIALS AND METHODS

In this retrospective study, 432 common consecutive free-text IR reports created during 4 months (from September 2013 to December 2013) before implementation of structured reporting (February 2014) and 415 structured IR reports created after implementation (from September 2014 to December 2014) were reviewed to assess ease of use and compliance with reporting requirements for regulatory requirements and coding. IR staff and trainees and referring physicians to IR were surveyed on procedure report attributes, such as detail, quality, and clarity.

RESULTS

Structured reporting increased compliance with reporting fluoroscopy time, radiation dose, and contrast administration compared with free-text reports (402/432 [93.1%] vs 251/415 [60.5%], P < .001; 402/432 [93.1%] vs 242/415 [58.3%], P < .001; and 395/432 [91.4%] vs 257/415 [61.9%], P < .001). Structured reporting decreased addendum requests for insufficient documentation from 43% (121/435 [28%] to 50/415 [12%], P = .01). Most IR physicians found structured reports to require less time to complete (21/26 [81%]), to be easier to complete (23/26 [89%]), and to have a similar or higher level of detail (19/26 [73%]) compared with free-text reports. Referring physicians were more satisfied with structured reports compared with free-text reports (6.9/10 vs 5.6/10, P = .03).

CONCLUSIONS

Structured IR reporting compared with free-text reporting improves compliance with radiation dose and contrast reporting, reporting and coding efficiency, and satisfaction among IR and referring physicians.