Implementation of an Institution-Wide Rules-Based Automated CT Protocoling System.

BACKGROUND. Examination protocoling is a noninterpretive task that increases radiologists' workload and can cause workflow inefficiencies. OBJECTIVE. The purpose of this study was to evaluate effects of an automated CT protocoling system on examination process times and protocol error rates. METHODS. This retrospective study included 317,597 CT examinations (mean age, 61.8 ± 18.1 [SD] years; male, 161,125; female, 156,447; unspecified sex, 25) from July 2020 to June 2022. A rules-based automated protocoling system was implemented institution-wide; the system evaluated all CT orders in the EHR and assigned a protocol or directed the order for manual radiologist protocoling. The study period comprised pilot (July 2020 to December 2020), implementation (January 2021 to December 2021), and postimplementation (January 2022 to June 2022) phases. Proportions of automatically protocoled examinations were summarized. Process times were recorded. Protocol error rates were assessed by counts of quality improvement (QI) reports and examination recalls and comparison with retrospectively assigned protocols in 450 randomly selected examinations. RESULTS. Frequency of automatic protocoling was 19,366/70,780 (27.4%), 68,875/163,068 (42.2%), and 54,045/83,749 (64.5%) in pilot, implementation, and postimplementation phases, respectively (p < .001). Mean (± SD) times from order entry to protocol assignment for automatically and manually protocoled examinations for emergency department examinations were 0.2 ± 18.2 and 2.1 ± 69.7 hours, respectively; mean inpatient examination times were 0.5 ± 50.0 and 3.5 ± 105.5 hours; and mean outpatient examination times were 361.7 ± 1165.5 and 1289.9 ± 2050.9 hours (all p < .001). Mean (± SD) times from order entry to examination completion for automatically and manually protocoled examinations for emergency department examinations were 2.6 ± 38.6 and 4.2 ± 73.0 hours, respectively (p < .001); for inpatient examinations were 6.3 ± 74.6 and 8.7 ± 109.3 hours (p = .001); and for outpatient examinations were 1367.2 ± 1795.8 and 1471.8 ± 2118.3 hours (p < .001). In the three phases, there were three, 19, and 25 QI reports and zero, one, and three recalls, respectively, for automatically protocoled examinations, versus nine, 19, and five QI reports and one, seven, and zero recalls for manually protocoled examinations. Retrospectively assigned protocols were concordant with 212/214 (99.1%) of automatically protocoled versus 233/236 (98.7%) of manually protocoled examinations. CONCLUSION. The automated protocoling system substantially reduced radiologists' protocoling workload and decreased times from order entry to protocol assignment and examination completion; protocol errors and recalls were infrequent. CLINICAL IMPACT. The system represents a solution for reducing radiologists' time spent performing noninterpretive tasks and improving care efficiency.

Automated tracking of emergency department abdominal CT findings during the COVID-19 pandemic using natural language processing.

PURPOSE: During the COVID-19 pandemic, emergency department (ED) volumes have fluctuated. We hypothesized that natural language processing (NLP) models could quantify changes in detection of acute abdominal pathology (acute appendicitis (AA), acute diverticulitis (AD), or bowel obstruction (BO)) on CT reports. METHODS: This retrospective study included 22,182 radiology reports from CT abdomen/pelvis studies performed at an urban ED between January 1, 2018 to August 14, 2020. Using a subset of 2448 manually annotated reports, we trained random forest NLP models to classify the presence of AA, AD, and BO in report impressions. Performance was assessed using 5-fold cross validation. The NLP classifiers were then applied to all reports. RESULTS: The NLP classifiers for AA, AD, and BO demonstrated cross-validation classification accuracies between 0.97 and 0.99 and F1-scores between 0.86 and 0.91. When applied to all CT reports, the estimated numbers of AA, AD, and BO cases decreased 43-57% in April 2020 (first regional peak of COVID-19 cases) compared to 2018-2019. However, the number of abdominal pathologies detected rebounded in May-July 2020, with increases above historical averages for AD. The proportions of CT studies with these pathologies did not significantly increase during the pandemic period. CONCLUSION: Dramatic decreases in numbers of acute abdominal pathologies detected by ED CT studies were observed early on during the COVID-19 pandemic, though these numbers rapidly rebounded. The proportions of CT cases with these pathologies did not increase, which suggests patients deferred care during the first pandemic peak. NLP can help automatically track findings in ED radiology reporting.

Fully Automated Deep Learning System for Bone Age Assessment.

Skeletal maturity progresses through discrete phases, a fact that is used routinely in pediatrics where bone age assessments (BAAs) are compared to chronological age in the evaluation of endocrine and metabolic disorders. While central to many disease evaluations, little has changed to improve the tedious process since its introduction in 1950. In this study, we propose a fully automated deep learning pipeline to segment a region of interest, standardize and preprocess input radiographs, and perform BAA. Our models use an ImageNet pretrained, fine-tuned convolutional neural network (CNN) to achieve 57.32 and 61.40% accuracies for the female and male cohorts on our held-out test images. Female test radiographs were assigned a BAA within 1 year 90.39% and within 2 years 98.11% of the time. Male test radiographs were assigned 94.18% within 1 year and 99.00% within 2 years. Using the input occlusion method, attention maps were created which reveal what features the trained model uses to perform BAA. These correspond to what human experts look at when manually performing BAA. Finally, the fully automated BAA system was deployed in the clinical environment as a decision supporting system for more accurate and efficient BAAs at much faster interpretation time (<2 s) than the conventional method.

Diagnostic yield of recommendations for chest CT examination prompted by outpatient chest radiographic findings.

PURPOSE: To evaluate the diagnostic yield of recommended chest computed tomography (CT) prompted by abnormalities detected on outpatient chest radiographic images. MATERIALS AND METHODS: This HIPAA-compliant study had institutional review board approval; informed consent was waived. Reports of all outpatient chest radiographic examinations performed at a large academic center during 2008 (n = 29 138) were queried to identify studies that included a recommendation for a chest CT imaging. The radiology information system was queried for these patients to determine if a chest CT examination was obtained within 1 year of the index radiographic examination that contained the recommendation. For chest CT examinations obtained within 1 year of the index chest radiographic examination and that met inclusion criteria, chest CT images were reviewed to determine if there was an abnormality that corresponded to the chest radiographic finding that prompted the recommendation. All corresponding abnormalities were categorized as clinically relevant or not clinically relevant, based on whether further work-up or treatment was warranted. Groups were compared by using t test and Fisher exact test with a Bonferroni correction applied for multiple comparisons. RESULTS: There were 4.5% (1316 of 29138 [95% confidence interval {CI}: 4.3%, 4.8%]) of outpatient chest radiographic examinations that contained a recommendation for chest CT examination, and increasing patient age (P < .001) and positive smoking history (P = .001) were associated with increased likelihood of a recommendation for chest CT examination. Of patients within this subset who met inclusion criteria, 65.4% (691 of 1057 [95% CI: 62.4%, 68.2%) underwent a chest CT examination within the year after the index chest radiographic examination. Clinically relevant corresponding abnormalities were present on chest CT images in 41.4% (286 of 691 [95% CI: 37.7%, 45.2%]) of cases, nonclinically relevant corresponding abnormalities in 20.6% (142 of 691 [95% CI: 17.6%, 23.8%]) of cases, and no corresponding abnormalities in 38.1% (263 of 691 [95% CI: 34.4%, 41.8%]) of cases. Newly diagnosed, biopsy-proven malignancies were detected in 8.1% (56 of 691 [95% CI: 6.2%, 10.4%]) of cases. CONCLUSION: A radiologist recommendation for chest CT to evaluate an abnormal finding on an outpatient chest radiographic examination has a high yield of clinically relevant findings.

Outcome of recommendations for radiographic follow-up of pneumonia on outpatient chest radiography.

OBJECTIVE: Follow-up chest radiographs are frequently recommended by radiologists to document the clearing of radiographically suspected pneumonia. However, the clinical utility of follow-up radiography is not well understood. The purpose of this study was to examine the incidence of important pulmonary pathology revealed during follow-up imaging of suspected pneumonia on outpatient chest radiography. MATERIALS AND METHODS: Reports of 29,138 outpatient chest radiography examinations performed at an academic medical center in 2008 were searched to identify cases in which the radiologist recommended follow-up chest radiography for presumed community-acquired pneumonia (n = 618). Descriptions of index radiographic abnormalities were recorded. Reports of follow-up imaging (radiography and CT) performed during the period from January 2008 to January 2010 were reviewed to assess the outcome of the index abnormality. Clinical history, demographics, microbiology, and pathology reports were reviewed and recorded. RESULTS: Compliance with follow-up imaging recommendations was 76.7%. In nine of 618 cases (1.5%), a newly diagnosed malignancy corresponded to the abnormality on chest radiography initially suspected to be pneumonia. In 23 of 618 cases (3.7%), an alternative nonmalignant disease corresponded with the abnormality on chest radiography initially suspected to be pneumonia. Therefore, in 32 of 618 patients (5.2%), significant new pulmonary diagnoses were established during follow-up imaging of suspected pneumonia. CONCLUSION: Follow-up imaging of radiographically suspected pneumonia leads to a small number of new diagnoses of malignancy and important nonmalignant diseases, which may alter patient management.

Using a web-based image quality assurance reporting system to improve image quality.

OBJECTIVE: The purpose of this study is to show the impact of a web-based image quality assurance reporting system on the rates of three common image quality errors at our institution. MATERIALS AND METHODS: A web-based image quality assurance reporting system was developed and used beginning in April 2009. Image quality endpoints were assessed immediately before deployment (period 1), approximately 18 months after deployment of a prototype reporting system (period 2), and approximately 12 months after deployment of a subsequent upgraded department-wide reporting system (period 3). A total of 3067 axillary shoulder radiographs were reviewed for correct orientation, 355 shoulder CT scans were reviewed for correct reformatting of coronal and sagittal images, and 346 sacral MRI scans were reviewed for correct acquisition plane of axial images. Error rates for each review period were calculated and compared using the Fisher exact test. RESULTS: Error rates of axillary shoulder radiograph orientation were 35.9%, 7.2%, and 10.0%, respectively, for the three review periods. The decrease in error rate between periods 1 and 2 was statistically significant (p < 0.0001). Error rates of shoulder CT reformats were 9.8%, 2.7%, and 5.8%, respectively, for the three review periods. The decrease in error rate between periods 1 and 2 was statistically significant (p = 0.03). Error rates for sacral MRI axial sequences were 96.5%, 32.5%, and 3.4%, respectively, for the three review periods. The decrease in error rates between periods 1 and 2 and between periods 2 and 3 was statistically significant (p < 0.0001). CONCLUSION: A web-based system for reporting image quality errors may be effective for improving image quality.

Implementation of a Semistructured Clinical Event Documentation Tool for Acute Adverse Contrast Reactions.

PURPOSE: To improve the efficiency and accuracy of clinicians documenting acute clinical events related to contrast agent administration using a web browser-based semistructured documentation support tool. METHODS: A new tool called Contrast Incident Support and Reporting (CISaR) was developed to enable radiologists responding to contrast reactions to document inciting contrast class, type of event, severity of contrast reaction, and recommendation for future contrast use. Retrospective analysis was conducted of all CT and MRI examinations performed between February 2018 and December 2019 across our hospital system with associated contrast reaction documentation. Time periods were defined as before tool deployment, early adoption, and steady-state deployment. The primary outcome measure was the presence of event documentation by a radiologist. The secondary outcome measure was completeness of the documentation parameters. RESULTS: A total of 431 CT and MRI studies with reactions were included in the study, and 50% of studies had radiologist documentation during the pre-CISaR period. This increased to 66% during the early adoption period and 89% in the post-CISaR period. It took approximately 9 months from the introduction of CISaR to reach full adoption and become the main method for adverse contrast reaction documentation. The percentage of radiologist documentation that detailed provoking contrast agent class, severity of reaction, reaction type, and future contrast agent recommendation all significantly increased (P < .0001), with greater than 95% inclusion of each element. CONCLUSION: The implementation of a semistructured electronic application for adverse contrast reaction reporting significantly increased radiologist documentation rate and completeness of the documentation.

Computer-assisted Reporting and Decision Support Increases Compliance with Follow-up Imaging and Hormonal Screening of Adrenal Incidentalomas.

OBJECTIVE: To assess the impact of using a computer-assisted reporting and decision support (CAR/DS) tool at the radiologist point-of-care on ordering provider compliance with recommendations for adrenal incidentaloma workup. METHOD: Abdominal CT reports describing adrenal incidentalomas (2014 - 2016) were retrospectively extracted from the radiology database. Exclusion criteria were history of cancer, suspected functioning adrenal tumor, dominant nodule size < 1 cm or ≥ 4 cm, myelolipomas, cysts, and hematomas. Multivariable logistic regression models were employed to predict follow-up imaging (FUI) and hormonal screening orders as a function of patient age and sex, nodule size, and CAR/DS use. CAR/DS reports were compared to conventional reports regarding ordering provider compliance with, frequency, and completeness of, guideline-warranted recommendations for FUI and hormonal screening of adrenal incidentalomas using Chi-square test. RESULT: Of 174 patients (mean age 62.4; 51.1% women) with adrenal incidentalomas, 62% (108/174) received CAR/DS-based recommendations versus 38% (66/174) unassisted recommendations. CAR/DS use was an independent predictor of provider compliance both with FUI (Odds Ratio [OR]=2.47, p = 0.02) and hormonal screening (OR=2.38, p = 0.04). CAR/DS reports recommended FUI (97.2%,105/108) and hormonal screening (87.0%,94/108) more often than conventional reports (respectively, 69.7% [46/66], 3.0% [2/66], both p <0.0001). CAR/DS recommendations more frequently included instructions for FUI time, protocol, and modality than conventional reports (all p <0.001). CONCLUSION: Ordering providers were at least twice as likely to comply with report recommendations for FUI and hormonal evaluation of adrenal incidentalomas generated using CAR/DS versus unassisted reporting. CAR/DS-directed recommendations were more adherent to guidelines than those generated without.

Incidence of nephrogenic systemic fibrosis after adoption of restrictive gadolinium-based contrast agent guidelines.

PURPOSE: To retrospectively determine the incidence of nephrogenic systemic fibrosis (NSF) in a large academic medical center after the adoption of restrictive gadolinium-based contrast agent (GBCA) administration guidelines. MATERIALS AND METHODS: For this retrospective HIPAA-compliant study, institutional review board approval was obtained and the requirement for informed consent was waived. Restrictive GBCA guidelines were adopted in May 2007. The guidelines (a) require a recent serum creatinine level measurement in any patient who is aged 60 years or older and/or at risk for renal disease, (b) limit the maximal weight-based GBCA dose administered to any patient with an estimated glomerular filtration rate (eGFR) lower than 60 mL/min/m(2) to 20 mL, and (c) prohibit the administration of any GBCA in patients who have an eGFR lower than 30 mL/min/m(2) and/or are undergoing chronic dialysis treatment (except in emergency situations). The electronic medical records were searched for all contrast material-enhanced magnetic resonance (MR) imaging examinations performed during the post-guidelines adoption period between January 2008 and March 2010 and the pre-guidelines adoption and transitional period between January 2002 and December 2007. Separate pathology records were searched for biopsy-confirmed cases of NSF during the same study periods. The incidences of NSF during the pre-guidelines adoption and transitional period and post-guidelines adoption period were compared by using the paired Z test. RESULTS: A total of 52,954 contrast-enhanced MR examinations were performed during the post-guidelines adoption period. Of these 52,954 examinations, 46,464 (88%) were performed in adult patients with an eGFR of 60 mL/min/m(2) or higher or presumed normal renal function and 6454 (12%) were performed in patients with an eGFR of 30-59 mL/min/m(2). Thirty-six patients with an eGFR lower than 30 mL/min/m(2) underwent contrast-enhanced MR imaging for emergent indications. Review of the pathology records for January 2008 to September 2010 revealed no new cases of NSF resulting from GBCA exposure. CONCLUSION: After restrictive guidelines regarding GBCA administration were instituted, no new cases of NSF were identified among 52,954 contrast-enhanced MR examinations, including those performed in patients with an eGFR lower than 60 mL/min/m(2).

Artificial Intelligence Enabling Radiology Reporting.

The radiology reporting process is beginning to incorporate structured, semantically labeled data. Tools based on artificial intelligence technologies using a structured reporting context can assist with internal report consistency and longitudinal tracking. To-do lists of relevant issues could be assembled by artificial intelligence tools, incorporating components of the patient's history. Radiologists will review and select artificial intelligence-generated and other data to be transmitted to the electronic health record and generate feedback for ongoing improvement of artificial intelligence tools. These technologies should make reports more valuable by making reports more accessible and better able to integrate into care pathways.

Computer-Assisted Reporting and Decision Support in Standardized Radiology Reporting for Cancer Imaging.

PURPOSE: Recent advances in structured reporting are providing an opportunity to enhance cancer imaging assessment to drive value-based care and improve patient safety. METHODS: The computer-assisted reporting and decision support (CAR/DS) framework has been developed to enable systematic ingestion of guidelines as clinical decision structured reporting tools embedded within the radiologist's workflow. RESULTS: CAR/DS tools can reduce the radiology reporting variability and increase compliance with clinical guidelines. The lung cancer use-case is used to describe various scenarios of a cancer imaging structured reporting pathway, including incidental findings, screening, staging, and restaging or continued care. Various aspects of these tools are also described using cancer-related examples for different imaging modalities and applications such as calculators. Such systems can leverage artificial intelligence (AI) algorithms to assist with the generation of structured reports and there are opportunities for new AI applications to be created using the structured data associated with CAR/DS tools. CONCLUSION: These AI-enabled systems are starting to allow information from multiple sources to be integrated and inserted into structured reports to drive improvements in clinical decision support and patient care.

Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.

RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intelligence (AI) system could help improve radiologist interrater agreement. MATERIALS AND METHODS: We performed a retrospective multi-radiologist user study to evaluate the impact of an AI system, the PXS score model, on the grading of categorical COVID-19 lung disease severity on 154 chest radiographs into four ordinal grades (normal/minimal, mild, moderate, and severe). Four radiologists (two thoracic and two emergency radiologists) independently interpreted 154 CXRs from 154 unique patients with COVID-19 hospitalized at a large academic center, before and after using the AI system (median washout time interval was 16 days). Three different thoracic radiologists assessed the same 154 CXRs using an updated version of the AI system trained on more imaging data. Radiologist interrater agreement was evaluated using Cohen and Fleiss kappa where appropriate. The lung disease severity categories were associated with clinical outcomes using a previously published outcomes dataset using Fisher's exact test and Chi-square test for trend. RESULTS: Use of the AI system improved radiologist interrater agreement (Fleiss κ = 0.40 to 0.66, before and after use of the system). The Fleiss κ for three radiologists using the updated AI system was 0.74. Severity categories were significantly associated with subsequent intubation or death within 3 days. CONCLUSION: An AI system used at the time of CXR study interpretation can improve the interrater agreement of radiologists.

A case tracking system with electronic medical record integration to automate outcome tracking for radiologists.

Radiologists make many diagnoses, but only sporadically get feedback on the subsequent clinical courses of their patients. We have created a web-based application that empowers radiologists to create and maintain personal databases of cases of interest. This tool integrates with existing information systems to minimize manual input such that radiologists can quickly flag cases for further follow-up without interrupting their clinical work. We have integrated this case-tracking system with an electronic medical record aggregation and search tool. As a result, radiologists can learn the outcomes of their patients with much less effort. We intend this tool to aid radiologists in their own personal quality improvement and to increase the efficiency of both teaching and research. We also hope to develop the system into a platform for systematic, continuous, quantitative monitoring of performance in radiology.

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers.

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve patient safety and drive value-based imaging. We discuss the advantages and potential applications that may be realized with the synergy between AI and CDS systems. From the perspective of both radiologist and ordering provider, CDS could be significantly empowered using AI. CDS enhanced by AI could reduce friction in radiology workflows and can aid AI developers to identify relevant imaging features their tools should be seeking to extract from images. Furthermore, these systems can generate structured data to be used as input to develop machine learning algorithms, which can drive downstream care pathways. For referring providers, an AI-enabled CDS solution could enable an evolution from existing imaging-centric CDS toward decision support that takes into account a holistic patient perspective. More intelligent CDS could suggest imaging examinations in highly complex clinical scenarios, assist on the identification of appropriate imaging opportunities at the health system level, suggest appropriate individualized screening, or aid health care providers to ensure continuity of care. AI has the potential to enable the next generation of CDS, improving patient care and enhancing providers' and radiologists' experience.

Curbing Inappropriate Usage of STAT Imaging at a Large Academic Medical Center.

PURPOSE: To evaluate a new system for processing and performing inpatient STAT diagnostic imaging with respect to utilization and time-based performance metrics. MATERIALS AND METHODS: This HIPAA-compliant study had institutional review board approval; informed consent was not required. The radiology information system of a large academic medical center was queried for all inpatient diagnostic imaging exams performed and interpreted from August 1, 2010, to October 31, 2012. Using customized software, data were evaluated based on order priority (non-STAT or STAT) and exam modality with respect to exam volume and time-based performance metrics (time-to-performance and preliminary interpretation time). Data were compared over 3 periods: August 1, 2010, to October 31, 2010 (preimplementation period); November 1, 2010, to October 31, 2011 (year 1 postimplementation); and November 1, 2011, to October 31, 2012 (year 2 postimplementation). RESULTS: In the first year after implementation of the new STAT policy, the percentage of inpatient exams ordered STAT significantly decreased from 22.1% to 5.4% (P < 0.001). This represented a proportional decrease of 26% (CT), 16% (MRI), 20% (US), and 24% (radiographs) relative to pre-STAT policy levels. The median time-to-performance and median preliminary interpretation time significantly decreased for all modalities after implementation of the policy (P < 0.05 for all modalities), decreasing by an average of 104 and 162 minutes, respectively. These changes persisted throughout year 2 postimplementation. CONCLUSION: A new institutional system for handling inpatient STAT diagnostic imaging results in a decreased number of STAT exams ordered and improved time-based performance metrics, thereby increasing workflow efficiency.

A Road Map for Translational Research on Artificial Intelligence in Medical Imaging: From the 2018 National Institutes of Health/RSNA/ACR/The Academy Workshop.

Advances in machine learning in medical imaging are occurring at a rapid pace in research laboratories both at academic institutions and in industry. Important artificial intelligence (AI) tools for diagnostic imaging include algorithms for disease detection and classification, image optimization, radiation reduction, and workflow enhancement. Although advances in foundational research are occurring rapidly, translation to routine clinical practice has been slower. In August 2018, the National Institutes of Health assembled multiple relevant stakeholders at a public meeting to discuss the current state of knowledge, infrastructure gaps, and challenges to wider implementation. The conclusions of that meeting are summarized in two publications that identify and prioritize initiatives to accelerate foundational and translational research in AI for medical imaging. This publication summarizes key priorities for translational research developed at the workshop including: (1) creating structured AI use cases, defining and highlighting clinical challenges potentially solvable by AI; (2) establishing methods to encourage data sharing for training and testing AI algorithms to promote generalizability to widespread clinical practice and mitigate unintended bias; (3) establishing tools for validation and performance monitoring of AI algorithms to facilitate regulatory approval; and (4) developing standards and common data elements for seamless integration of AI tools into existing clinical workflows. An important goal of the resulting road map is to grow an ecosystem, facilitated by professional societies, industry, and government agencies, that will allow robust collaborations between practicing clinicians and AI researchers to advance foundational and translational research relevant to medical imaging.

Toward Augmented Radiologists: Changes in Radiology Education in the Era of Machine Learning and Artificial Intelligence.

Radiology practice will be altered by the coming of artificial intelligence, and the process of learning in radiology will be similarly affected. In the short term, radiologists will need to understand the first wave of artificially intelligent tools, how they can help them improve their practice, and be able to effectively supervise their use. Radiology training programs will need to develop curricula to help trainees acquire the knowledge to carry out this new supervisory duty of radiologists. In the longer term, artificially intelligent software assistants could have a transformative effect on the training of residents and fellows, and offer new opportunities to bring learning into the ongoing practice of attending radiologists.

Magnetic resonance imaging of the lumbar spine: determining clinical impact and potential harm from overuse.

BACKGROUND: Lumbar spine magnetic resonance imaging is frequently said to be "overused" in the evaluation of low back pain, yet data concerning the extent of overuse and the potential harmful effects are lacking. PURPOSE: The objective of this study was to determine the proportion of examinations with a detectable impact on patient care (actionable outcomes). STUDY DESIGN: This is a retrospective cohort study. PATIENT SAMPLE: A total of 5,365 outpatient lumbar spine magnetic resonance (MR) examinations were conducted. OUTCOME MEASURES: Actionable outcomes included (1) findings leading to an intervention making use of anatomical information such as surgery; (2) new diagnoses of cancer, infection, or fracture; or (3) following known lumbar spine pathology. Potential harm was assessed by identifying examinations where suspicion of cancer or infection was raised but no positive diagnosis made. METHODS: A medical record aggregation/search system was used to identify lumbar spine MR examinations with positive outcome measures. Patient notes were examined to verify outcomes. A random sample was manually inspected to identify missed positive outcomes. RESULTS: The proportion of actionable lumbar spine magnetic resonance imaging was 13%, although 93% were appropriate according to the American College of Radiology guidelines. Of 36 suspected cases of cancer or infection, 81% were false positives. Further investigations were ordered on 59% of suspicious examinations, 86% of which were false positives. CONCLUSIONS: The proportion of lumbar spine MR examinations that inform management is small. The false-positive rate and the proportion of false positives involving further investigation are high. Further study to improve the efficiency of imaging is warranted.

The Effect of Computer-Assisted Reporting on Interreader Variability of Lumbar Spine MRI Degenerative Findings: Five Readers With 30 Disc Levels.

PURPOSE: The aim of this study was to assess differences in interreader variability among radiologists after the implementation of a computer-assisted reporting (CAR) tool for the interpretation of degenerative disc disease on lumbar spine MRI. METHODS: Thirty lumbar spine MRI examinations were selected from the radiology database. Five fellowship-trained musculoskeletal radiologists evaluated each L4-L5 disc in a blinded fashion and reported the findings using a traditional free dictation approach. One month later, they reinterpreted the same discs using a web browser-based CAR tool in the same blinded fashion. The degrees of central canal stenosis, neural foraminal stenosis, and facet joint osteoarthritis; presence or absence of lateral recess stenosis; types of disc bulge or herniation; and herniation location using both methods were recorded. Percentage disagreement among the radiologists for each variable was calculated and compared using the Wilcoxon signed rank test. RESULTS: There was a statistically significant decrease among the five radiologists in percentage disagreement for neural foraminal stenosis (46% versus 35%, P = .0146) and facet joint osteoarthritis (45% and 22%, P < .0001) for reports created by free dictation compared with those created using the CAR tool. There was no statistically significant difference in interreader variability for the assessment of central canal stenosis, lateral recess effacement, disc herniation, disc bulge, or herniation location. CONCLUSIONS: Implementation of a CAR tool for the interpretation of degenerative changes on lumbar spine MRI decreases interreader variability in the assessment of neural foraminal stenosis and facet joint osteoarthritis.