Practical Approaches to Quality Improvement for Radiologists

Kelly, Aine Marie; Cronin, Paul

doi:10.1148/rg.2015150057

Cited by 28 publications

(14 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Ishikawa fishbone diagram summarizes potential causes for wrong side pertaining to the radiologists, environment, and imaging exam as well as PACS and voice recognition software (Fig. 2) (14). The Pareto chart summarizes causes of errors related to the wrong side of findings in the Thoracic Imaging Division (Fig.…”

Section: Resultsmentioning

confidence: 99%

Process improvement for reducing side discrepancies in radiology reports

Digumarthy

Vining

Tabari

et al. 2018

Acta Radiologica Open

View full text Add to dashboard Cite

BackgroundLaterality errors in radiology reports can lead to serious errors in management.PurposeTo reduce errors related to side discrepancies in radiology reports from thoracic imaging by 50% over a six-month period with education and voice recognition software tools.Material and MethodsAll radiology reports at the Thoracic Imaging Division from the fourth quarter of 2016 were reviewed manually for presence of side discrepancies (baseline data). Side discrepancies were defined as a lack of consistency in side labeling of any abnormality in the “Findings” to “Impression” sections of the reports. Process map and Ishikawa fishbone diagram (Microsoft Visio) were created. All thoracic radiologists were educated on side-related errors in radiology reports for plan–design–study–act cycle 1 (PDSA #1). Two weeks later, voice recognition software was configured to capitalize sides (RIGHT and LEFT) in the reports during dictated (PDSA# 2). Radiology reports were analyzed to determine side-discrepancy errors following each PDSA cycle (post-interventional data). Statistical run charts were created using QI Macros statistical software.ResultsBaseline data revealed 33 side-discrepancy errors in 47,876 reports with an average of 2.5 errors per week (range = 1–8 errors). Following PDSA #1, there were seven errors pertaining to side discrepancies over a two-week period. Errors declined following implementation of PDSA #2 to meet the target of 0.85 side-discrepancy error per week over seven weeks.ConclusionAutomated processes (such as capitalization of sides) help reduce left/right errors substantially without affecting reporting turnaround time.

show abstract

Section: Resultsmentioning

confidence: 99%

Process improvement for reducing side discrepancies in radiology reports

Digumarthy

Vining

Tabari

et al. 2018

Acta Radiologica Open

View full text Add to dashboard Cite

show abstract

“…However, it has to be considered that besides RADPEER, there are other well-established measures to improve the quality of radiological reports like daily clinical demonstrations and tumor boards that include as in the case of RADPEER a "second look" on radiological examinations, often by a different radiologist [32]. Morbidity & mortality conferences provide a systematic approach to analyze errors in radiology with the aim of reducing these in the future.…”

Section: Discussionmentioning

confidence: 99%

Time Requirement and Feasibility of a Systematic Quality Peer Review of Reporting in Radiology

Maurer

Brönnimann

Schroeder

et al. 2020

Rofo

View full text Add to dashboard Cite

Objective To estimate the human resources required for a retrospective quality review of different percentages of all routine diagnostic procedures in the Department of Radiology at Bern University Hospital, Switzerland. Materials and Methods Three board-certified radiologists retrospectively evaluated the quality of the radiological reports of a total of 150 examinations (5 different examination types: abdominal CT, chest CT, mammography, conventional X-ray images and abdominal MRI). Each report was assigned a RADPEER score of 1 to 3 (score 1: concur with previous interpretation; score 2: discrepancy in interpretation/not ordinarily expected to be made; score 3: discrepancy in interpretation/should be made most of the time). The time (in seconds, s) required for each review was documented and compared. A sensitivity analysis was conducted to calculate the total workload for reviewing different percentages of the total annual reporting volume of the clinic. Results Among the total of 450 reviews analyzed, 91.1 % (410/450) were assigned a score of 1 and 8.9 % (40/450) were assigned scores of 2 or 3. The average time (in seconds) required for a peer review was 60.4 s (min. 5 s, max. 245 s). The reviewer with the greatest clinical experience needed significantly less time for reviewing the reports than the two reviewers with less clinical expertise (p < 0.05). Average review times were longer for discrepant ratings with a score of 2 or 3 (p < 0.05). The total time requirement calculated for reviewing all 5 types of examination for one year would be more than 1200 working hours. Conclusion A retrospective peer review of reports of radiological examinations using the RADPEER system requires considerable human resources. However, to improve quality, it seems feasible to peer review at least a portion of the total yearly reporting volume. Key Points: Citation Format

show abstract

“…We did not consider noncommercial applications that also provide solutions, which are commonly used in research. A radiologist with 10+ years of experience in neuroradiology (AO) performed the coding of the collected data by using codebooks to examine which tasks of radiology [15,16] are targeted by an application and what kind of impacts it can have on these tasks (i.e. 'supporting', 'extending', 'replacing'; Table 1).…”

Section: Data Collection and Codingmentioning

confidence: 99%

Promises of artificial intelligence in neuroradiology: a systematic technographic review

2020

View full text Add to dashboard Cite

Purpose To conduct a systematic review of the possibilities of artificial intelligence (AI) in neuroradiology by performing an objective, systematic assessment of available applications. To analyse the potential impacts of AI applications on the work of neuroradiologists. Methods We identified AI applications offered on the market during the period 2017–2019. We systematically collected and structured information in a relational database and coded for the characteristics of the applications, their functionalities for the radiology workflow and their potential impacts in terms of ‘supporting’, ‘extending’ and ‘replacing’ radiology tasks. Results We identified 37 AI applications in the domain of neuroradiology from 27 vendors, together offering 111 functionalities. The majority of functionalities ‘support’ radiologists, especially for the detection and interpretation of image findings. The second-largest group of functionalities ‘extends’ the possibilities of radiologists by providing quantitative information about pathological findings. A small but noticeable portion of functionalities seek to ‘replace’ certain radiology tasks. Conclusion Artificial intelligence in neuroradiology is not only in the stage of development and testing but also available for clinical practice. The majority of functionalities support radiologists or extend their tasks. None of the applications can replace the entire radiology profession, but a few applications can do so for a limited set of tasks. Scientific validation of the AI products is more limited than the regulatory approval.

show abstract

Practical Approaches to Quality Improvement for Radiologists

Cited by 28 publications

References 23 publications

Process improvement for reducing side discrepancies in radiology reports

Process improvement for reducing side discrepancies in radiology reports

Time Requirement and Feasibility of a Systematic Quality Peer Review of Reporting in Radiology

Promises of artificial intelligence in neuroradiology: a systematic technographic review

Contact Info

Product

Resources

About