Effect of Patient Navigation on Completion of Lung Cancer Screening in Vulnerable Populations.

BACKGROUND: Although low-dose, CT-based lung cancer screening (LCS) can decrease lung cancer mortality in high-risk individuals, the process may be complex and pose challenges to patients, particularly those from minority underinsured and uninsured populations. We conducted a randomized controlled trial of telephone-based navigation for LCS within an integrated, urban, safety-net health care system. PATIENTS AND METHODS: Patients eligible for LCS were randomized (1:1) to usual care with or without navigation at Parkland Health in Dallas, Texas. The primary endpoint was completion of the first 3 consecutive steps in a patient's LCS process. We explored differences in completion of LCS steps between navigation and usual care groups, controlling for patient characteristics using the chi-square test. RESULTS: Patients (N=447) were randomized to either navigation (n=225) or usual care (n=222). Mean patient age was 62 years, 46% were female, and 69% were racial/ethnic minorities. There was no difference in completion of the first 3 steps of the LCS algorithm between arms (12% vs 9%, respectively; P=.30). For ordered LCS steps, completion rates were higher among patients who received navigation (86% vs 79%; P=.03). The primary reason for step noncompletion was lack of order placement. CONCLUSIONS: In this study, lack of order placement was a key reason for incomplete LCS steps. When orders were placed, patients who received navigation had higher rates of completion. Clinical team education and enhanced electronic health record processes to simplify order placement, coupled with patient navigation, may improve LCS in safety-net health care systems.

Geographic Accessibility and Completion of Initial Low-Dose CT-Based Lung Cancer Screening in an Urban Safety-Net Population.

BACKGROUND: Recent modifications to low-dose CT (LDCT)-based lung cancer screening guidelines increase the number of eligible individuals, particularly among racial and ethnic minorities. Because these populations disproportionately live in metropolitan areas, we analyzed the association between travel time and initial LDCT completion within an integrated, urban safety-net health care system. METHODS: Using Esri's StreetMap Premium, OpenStreetMap, and the r5r package in R, we determined projected private vehicle and public transportation travel times between patient residence and the screening facility for LDCT ordered in March 2017 through December 2022 at Parkland Memorial Hospital in Dallas, Texas. We characterized associations between travel time and LDCT completion in univariable and multivariable analyses. We tested these associations in a simulation of 10,000 permutations of private vehicle and public transportation distribution. RESULTS: A total of 2,287 patients were included in the analysis, of whom 1,553 (68%) completed the initial ordered LDCT. Mean age was 63 years, and 73% were underrepresented minorities. Median travel time from patient residence to the LDCT screening facility was 17 minutes by private vehicle and 67 minutes by public transportation. There was a small difference in travel time to the LDCT screening facility by public transportation for patients who completed LDCT versus those who did not (67 vs 66 min, respectively; P=.04) but no difference in travel time by private vehicle for these patients (17 min for both; P=.67). In multivariable analysis, LDCT completion was not associated with projected travel time to the LDCT facility by private vehicle (odds ratio, 1.01; 95% CI, 0.82-1.25) or public transportation (odds ratio, 1.14; 95% CI, 0.89-1.44). Similar results were noted across travel-type permutations. Black individuals were 29% less likely to complete LDCT screening compared with White individuals. CONCLUSIONS: In an urban population comprising predominantly underrepresented minorities, projected travel time is not associated with initial LDCT completion in an integrated health care system. Other reasons for differences in LDCT completion warrant investigation.

Associations of Ultrasound LI-RADS Visualization Score With Examination, Sonographer, and Radiologist Factors: Retrospective Assessment in Over 10,000 Examinations.

BACKGROUND. When performing ultrasound (US) for hepatocellular carcinoma (HCC) screening, numerous factors may impair hepatic visualization, potentially lowering sensitivity. US LI-RADS includes a visualization score as a technical adequacy measure. OBJECTIVE. The purpose of this article is to identify associations between examination, sonographer, and radiologist factors and the visualization score in liver US HCC screening. METHODS. This retrospective study included 6598 patients (3979 men, 2619 women; mean age, 58 years) at risk for HCC who underwent a total of 10,589 liver US examinations performed by 91 sonographers and interpreted by 50 radiologists. Visualization scores (A, no or minimal limitations; B, moderate limitations; C, severe limitations) were extracted from clinical reports. Patient location (emergency department [ED], in-patient, outpatient), sonographer and radiologist liver US volumes during the study period (< 50, 50-500, > 500 examinations), and radiologist practice pattern (US, abdominal, community, interventional) were recorded. Associations with visualization scores were explored. RESULTS. Frequencies of visualization scores were 71.5%, 24.2%, and 4.2% for A, B, and C, respectively. Scores varied significantly (p < .001) between examinations performed in ED patients (49.8%, 40.1%, and 10.2%), inpatients (58.8%, 33.9%, and 7.3%), and outpatients (76.7%, 20.3%, and 2.9%). Scores also varied significantly (p < .001) by sonographer volume (< 50 examinations: 58.4%, 33.7%, and 7.9%; > 500 examinations: 72.9%, 22.5%, and 4.6%); reader volume (< 50 examinations: 62.9%, 29.9%, and 7.1%; > 500 examinations: 67.3%, 28.0%, and 4.7%); and reader practice pattern (US: 74.5%, 21.3%, and 4.3%; abdominal: 67.0%, 28.1%, and 4.8%; community: 75.2%, 21.9%, and 2.9%; interventional: 68.5%, 24.1%, and 7.4%). In multivariable analysis, independent predictors of score C were patient location (ED/inpatient: odds ratio [OR], 2.62; p < .001) and sonographer volume (< 50: OR, 1.55; p = .01). Among sonographers performing 50 or more examinations, the percentage of outpatient examinations with score C ranged from 0.8% to 5.4%; 9/33 were above the upper 95% CI of 3.2%. CONCLUSION. The US LI-RADS visualization score may identify factors affecting quality of HCC screening examinations and identify outlier sonographers in terms of poor examination quality. The approach also highlights potential systematic biases among radiologists in their quality assessment process. CLINICAL IMPACT. These findings may be applied to guide targeted quality improvement efforts and establish best practices and performance standards for screening programs.

Preoperative Thoracic CT Findings Associated With Postoperative Mechanical Ventilation in Patients Undergoing Major Abdominal or Pelvic Surgery: A Matched Case-Control Study.

BACKGROUND. Postoperative prolonged mechanical ventilation is associated with increased morbidity and mortality. Reliable predictors of the need for postoperative mechanical ventilation after abdominal or pelvic surgeries are lacking. OBJECTIVE. The purpose of this study was to explore associations between preoperative thoracic CT findings and the need for postoperative mechanical ventilation after major abdominal or pelvic surgeries. METHODS. This retrospective case-control study included patients who underwent abdominal or pelvic surgeries during the period from January 1, 2014, through December 31, 2018, and had undergone preoperative thoracic CT. Case patients were patients who required postoperative mechanical ventilation. Control patients and case patients were matched at a 3:1 ratio on the basis of age, sex, body mass index, chronic obstructive pulmonary disease, smoking status, and surgery type. Two radiologists (readers 1 and 2) reviewed the CT images. Findings were compared between groups. RESULTS. The study included 165 patients (70 women, 95 men; mean age, 67.0 ± 9.7 [SD] years; 42 case patients and 123 matched control patients). Bronchial wall thickening and pericardial effusion were more frequent in case patients than control patients for reader 2 (10% vs 2%, p = .03; 17% vs 5%, p = .01) but not for reader 1. Pulmonary artery diameter (mean ± SD) was greater in case patients than control patients for reader 2 (2.9 ± 0.5 cm vs 2.8 ± 0.5 cm, p = .045) but not reader 1. Right lung height was lower in case patients than control patients for reader 1 (18.4 ± 2.9 cm vs 19.9 ± 2.7 cm, p = .01) and reader 2 (18.3 ± 2.9 cm vs 19.8 ± 2.7 cm, p = .01). Left lung height was lower in case patients than control patients for reader 1 (19.5 ± 3.1 cm vs 21.1 ± 2.6 cm, p = .01) and reader 2 (19.6 ± 2.4 cm vs 20.9 ± 2.6 cm, p = .01). Anteroposterior (AP) chest diameter was greater for case patients than control patients for reader 1 (14.0 ± 2.3 cm vs 12.9 ± 3.7 cm, p = .02) and reader 2 (14.2 ± 2.2 cm vs 13.2 ± 3.6 cm, p = .04). In a multivariable regression model using pooled reader data, bronchial wall thickening exhibited an odds ratio (OR) of 4.6 (95% CI, 1.3-16.5; p = .02); pericardial effusion, an OR of 5.1 (95% CI, 1.7-15.5; p = .004); pulmonary artery diameter, an OR of 1.4 per 1-cm increase (95% CI, 0.7-3.0; p = .32); mean lung height, an OR of 0.8 per 1-cm increase (95% CI, 0.7-1.001; p = .05); and AP chest diameter, an OR of 1.2 per 1-cm increase (95% CI, 1.013-1.4; p = .03). CONCLUSION. CT features are associated with the need for postoperative mechanical ventilation after abdominal or pelvic surgery. CLINICAL IMPACT. Many patients undergo thoracic CT before abdominal or pelvic surgery; the CT findings may complement preoperative clinical risk factors.

Detection of Incidental Pulmonary Embolism on Conventional Contrast-Enhanced Chest CT: Comparison of an Artificial Intelligence Algorithm and Clinical Reports.

BACKGROUND. Artificial intelligence (AI) algorithms have shown strong performance for detection of pulmonary embolism (PE) on CT examinations performed using a dedicated protocol for PE detection. AI performance is less well studied for detecting PE on examinations ordered for reasons other than suspected PE (i.e., incidental PE [iPE]). OBJECTIVE. The purpose of this study was to assess the diagnostic performance of an AI algorithm for detection of iPE on conventional contrast-enhanced chest CT examinations. METHODS. This retrospective study included 2555 patients (mean age, 53.2 ± 14.5 [SD] years; 1340 women, 1215 men) who underwent 3003 conventional contrast-enhanced chest CT examinations (i.e., not using pulmonary CTA protocols) between September 2019 and February 2020. A commercial AI algorithm was applied to the images to detect acute iPE. A vendor-supplied natural language processing (NLP) algorithm was applied to the clinical reports to identify examinations interpreted as positive for iPE. For all examinations that were positive by the AI-based image review or by NLP-based report review, a multireader adjudication process was implemented to establish a reference standard for iPE. Images were also reviewed to identify explanations of AI misclassifications. RESULTS. On the basis of the adjudication process, the frequency of iPE was 1.3% (40/3003). AI detected four iPEs missed by clinical reports, and clinical reports detected seven iPEs missed by AI. AI, compared with clinical reports, exhibited significantly lower PPV (86.8% vs 97.3%, p = .03) and specificity (99.8% vs 100.0%, p = .045). Differences in sensitivity (82.5% vs 90.0%, p = .37) and NPV (99.8% vs 99.9%, p = .36) were not significant. For AI, neither sensitivity nor specificity varied significantly in association with age, sex, patient status, or cancer-related clinical scenario (all p > .05). Explanations of false-positives by AI included metastatic lymph nodes and pulmonary venous filling defect, and explanations of false-negatives by AI included surgically altered anatomy and small-caliber subsegmental vessels. CONCLUSION. AI had high NPV and moderate PPV for iPE detection, detecting some iPEs missed by radiologists. CLINICAL IMPACT. Potential applications of the AI tool include serving as a second reader to help detect additional iPEs or as a worklist triage tool to allow earlier iPE detection and intervention. Various explanations of AI misclassifications may provide targets for model improvement.

Use of Web-Based Calculator for the Implementation of ACR TI-RADS Risk-Stratification System.

In this article, we demonstrate the use of a software-based radiologist reporting tool for the implementation of American College of Radiology Thyroid Imaging, Reporting and Data System thyroid nodule risk-stratification. The technical details are described with emphasis on addressing the information security and patient privacy issues while allowing it to integrate with the electronic health record and radiology reporting dictation software. Its practical implementation is assessed in a quality improvement project in which guideline adherence and recommendation congruence were measured pre and post implementation. The descriptions of our solution and the release of the open-sourced codes may be helpful in future implementation of similar web-based calculators.

Response to COVID-19: Minimizing Risks, Addressing Challenges and Maintaining Operations in a Complex Academic Radiology Department.

The coronavirus disease 2019 (COVID-19) pandemic has necessitated rapid response plans to minimize risks of infection in the workforce while ensuring maintenance of essential functions of radiology departments. Plan adoption is, however, challenged by the need to coordinate with institutional efforts, a rapidly expanding number of patients, and the diversity of clinical and administrative functions in the department. Here, we describe the implementation of a response plan in an academic radiology department, challenges encountered, and tactics used to address these challenges.

Awareness of relative CT utilization among peers is not associated with changes in imaging requests among emergency department providers in a large county hospital.

PURPOSE: The purpose of this quality improvement initiative was to study the effect of providing scorecards to emergency department providers to assess its effect on changes in utilization. METHODS: CT of the abdomen and pelvis, CT angiogram of the chest for pulmonary embolism, and CT of the head were targeted due to ordering variability, cost, and radiation exposure. The utilization rate for each provider was assessed for emergency department providers. Following this, providers were given scorecards regarding their utilization as well as their relative utilization compared with each other. Utilization was then monitored following the intervention to assess the effect of the scorecard on ordering practices. RESULTS: No significant effect on the utilization of these 3 exams was found after the scorecard intervention. CONCLUSION: Providing scorecards to make emergency department providers aware of their relative utilization does not significantly alter ordering behavior. Incentive-based systems may be required in order to lessen overutilization of these 3 commonly ordered radiology procedures in the emergency department.

A Technique to Identify Isoattenuating Gallstones with Dual-Layer Spectral CT: An ex Vivo Phantom Study.

Background Previously reported dual-energy CT methods for detecting noncalcified gallstones have reduced accuracy for gallstones smaller than 9 mm. Purpose To develop a dual-energy CT method for differentiating isoattenuating gallstones from bile and compare it with previously reported dual-energy CT methods by using a prospective ex vivo phantom reader study. Materials and Methods From May 2017 to May 2018, gallstones were collected from 105 patients (34 men; mean age, 51 years; age range, 18-84 years) undergoing cholecystectomy and placed inside 120-mL vials containing ox bile. The vials were placed inside a water-filled phantom and were scanned with dual-layer dual-energy CT. Thirty isoattenuating gallstones (4.3-24.7 mm in diameter) were evaluated. Conventional CT images, virtual noncontrast images, and monoenergetic images at 200 and 40 keV were created. Segmented images were created by using a two-dimensional histogram of Compton and photoelectric attenuation. Six readers evaluated the presence of isoattenuating gallstones in each image. Intra- and interreader agreement was measured by using percentage agreement, diagnostic performance was evaluated by using mean area under the receiver operating characteristic curve (AUC) estimates and pairwise comparisons, and the agreement of gallstone sizes measured at pathologic examination with those measured on segmented images was compared by using Bland-Altman analysis. Results For all gallstones, segmented images provided the highest mean intrareader (88.1%) and interreader (88.2% and 93.6%) agreements for all readers and reading sessions and the highest overall AUC (0.99; 95% confidence interval [CI]: 0.97, 1.00; adjusted P < .02 for all). For gallstones larger than 9 mm, no significant difference was found between the segmented and monoenergetic AUCs (all P > .94, adjusted P > .05 for all). For gallstones measuring 9 mm or smaller, the segmented images had the highest overall AUC (0.99; 95% CI: 0.97, 1.00; adjusted P < .01 for all). The mean difference in stone sizes was -0.6 mm, with limits of agreement from 2.6 to -3.8 mm. Conclusion Segmented images from Compton and photoelectric attenuation coefficients improve detection of isoattenuating gallstones compared with previously reported dual-energy CT methods. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Matos in this issue.

Utilization of Structured Reporting to Monitor Outcomes of Doppler Ultrasound Performed for Deep Vein Thrombosis.

Determining the clinical impact of imaging exams at the enterprise level is problematic, as radiology reports historically have been created with the content meant primarily for the referring provider. Structured reporting can establish the foundation for enterprise monitoring of imaging outcomes without manual review providing the framework for assessment of utilization and quality. Ultrasound (US) for deep vein thrombosis evaluation (DVT) is an ideal testbed for assessing this functionality. The system standard template for Doppler US for extremity venous evaluation for DVT was updated with a discrete fixed picklist of impression options and implemented system wide. Template utilization and interpretive outcomes were actively monitored and use reinforced as part of standard clinical practice. From January 1, 2017 to December 31, 2017, 9111 US exams for DVT were performed with 8997 utilizing structured reporting (98.75%). Of those in the structured reporting group, 1074 (11.79%) were positive for any type of DVT with 732 (8.03%) reported as Acute/New above the knee. Positive rates for any type of DVT were 10.29% emergency department, 14.17% inpatient, and 13.20% outpatient. While being the lowest positive rate, the emergency department had the highest overall volume of exams. Structured reporting for DVT US assessment outcomes can be implemented with a very high rate of radiologist adoption and adherence providing accurate determination of positive rates, month by month, in differing patient locations. Structured elements can be used to automatically trigger downstream processes; in our institution, this will alert providers in the EHR if the patient does not receive anticoagulation within 2 h of a positive test. This lays the foundation for effective enterprise assessment of imaging outcomes forming the basis of future quality and safety initiatives on optimizing health system resource utilization.

Implementing Shared, Standardized Imaging Protocols to Improve Cross-Enterprise Workflow and Quality.

Value-based imaging requires appropriate utilization and the delivery of consistently high-quality imaging at an acceptable cost. Challenges include developing standardized imaging protocols, ensuring consistent application by technologists, and monitoring quality. These challenges increase as enterprises grow in geographical extent and complexity through mergers or partnerships. Our imaging enterprise includes a university hospital and clinic system, a large county hospital and healthcare system, and a pediatric hospital and health system. Studies across the three systems are interpreted by one large academic radiology group with expertise in various subspecialties. Our goals were as follows: (1) Standardize imaging protocols; (2) adapt the imaging protocols to specific modalities and available equipment; and (3) disseminate this knowledge across all of the sites of care. Our approach involved three components: (1) facilitation of imaging protocol definition across subspecialty radiologist teams; (2) creation of a database which links the clinical imaging protocols to the scanner/machine specific acquisition protocols; and (3) delivery of a protocol library and updates to all users regardless of location. We successfully instituted a process for the development, implementation, and delivery of standardized imaging protocols in a complex, multi-institutional healthcare system. Key elements for success include (1) a Project Champion who is able to articulate the importance of protocol standardization in improving the quality of patient care, (2) strong, effective modality-specific operational committees, (3) a Project Lead to manage the process efficiently, and (4) an electronic publishing of the protocol database to facilitate ease of access and use.

Electronic Medical Record Integration for Streamlined DXA Reporting.

Dual-energy X-ray absorptiometry (DXA) is the most frequently performed examination to assess bone mineral density in clinical practice. Aside from images and graphical displays, many numerical values are part of DXA reports. These values are typically manually entered into the formal report through the electronic medical record or PACS workstation. The process takes time and is prone to errors. Exporting the DXA numerical data via HL7 engine to the electronic medical record was proposed to improve reporting efficiency and accuracy. The output from the DXA unit computer was reconfigured to export the report content via the HL7 interface engine into the electronic medical record. Radiology interpretive reporting was subsequently done directly in the electronic medical record. In the evaluation of errors, 100 preliminary DXA reports before the change and 100 after the change were examined. These reports were analyzed for errors that included decimal change, number transposition, negative number issue, other incorrect number error, and failure to include prior exam for comparison. In addition, report turnaround times were evaluated before and after the changes were made. Reporting time evaluations included 1-year volume prior to change (3915 reports) and 1 month post-change (206 reports). Of 100 DEXA exams before the change, 15 final reports contained 25 numerical errors. After the change, no numerical errors in the reports were identified. Exam end to final report time decreased from 2159 to 625 min on average. Automating data transmittal from the DXA modality for report generation improves accuracy and turnaround time. This approach did not require any third party software, and healthcare information security concerns were negated since we are using our standard workstations. Secondary to the affordability and applicability to the large percentage of the population using electronic medical record systems, this type of automated workflow is recommended.

Implementation of a Voice Messaging System is Associated With Improved Time-to-Treatment and Overall Survival in Patients With Hepatocellular Carcinoma.

BACKGROUND: The diagnosis and treatment of cancer is a continuum, with multiple steps and interfaces between patients and providers allowing for potential delays in cancer recognition and subsequent treatment. The diagnosis and treatment of hepatocellular carcinoma (HCC) is especially prone to missteps along the continuum, leading to treatment delays due to non-tissue-based diagnosis and multiprovider treatments. The aim of this study was to evaluate outcome measures after implementation of a voice messaging system (VMS) designed to streamline patient referrals to downstream treatment physicians and ultimately reduce delays in HCC treatment, thereby improving outcome measures. METHODS: A retrospective study of outpatients with HCC was conducted in a safety net hospital between February 2008 and January 2012. In February 2010, VMS notification of HCC to the ordering physician and downstream treating physicians was implemented. Patients were divided into 2 groups: (1) preintervention: diagnosis 2 years before implementation or failure of notification following implementation, and (2) postintervention: diagnosis 2 years after implementation. Demographics, tumor characteristics, treatment, and survival were compared. RESULTS: This study included 96 patients diagnosed with HCC: 51 in the preintervention group and 45 in the postintervention group. The main cause of chronic liver disease was HCV infection, and no differences in symptoms, liver dysfunction, tumor characteristics, or treatment were observed between groups. The time from diagnosis to clinic contact (0.5 vs 2.9 months; P=.003) and time from detection to treatment (2.2 vs 5.5 months; P=.005) was significantly shorter after implementation of the VMS. Barcelona Clinic Liver Cancer stage A status (hazard ratio [HR], 3.1; 95% CI, 2, 6), treatment (HR, 1.9; 95% CI, 1, 4), and VMS (HR, 1.8; 95% CI, 1, 3) were independently associated with overall survival. Patients diagnosed after implementation of the VMS had a median survival of 28.5 versus 15.7 months (P=.02). CONCLUSIONS: Implementation of VMS reduces time to treatment and time to clinic visit. Reduction in time to treatment is associated with improved outcome independent of tumor stage, underlying liver function, and treatment.

Performance of Multiple Pretrained BERT Models to Automate and Accelerate Data Annotation for Large Datasets.

Purpose: To develop and evaluate domain-specific and pretrained bidirectional encoder representations from transformers (BERT) models in a transfer learning task on varying training dataset sizes to annotate a larger overall dataset. Materials and Methods: The authors retrospectively reviewed 69 095 anonymized adult chest radiograph reports (reports dated April 2020-March 2021). From the overall cohort, 1004 reports were randomly selected and labeled for the presence or absence of each of the following devices: endotracheal tube (ETT), enterogastric tube (NGT, or Dobhoff tube), central venous catheter (CVC), and Swan-Ganz catheter (SGC). Pretrained transformer models (BERT, PubMedBERT, DistilBERT, RoBERTa, and DeBERTa) were trained, validated, and tested on 60%, 20%, and 20%, respectively, of these reports through fivefold cross-validation. Additional training involved varying dataset sizes with 5%, 10%, 15%, 20%, and 40% of the 1004 reports. The best-performing epochs were used to assess area under the receiver operating characteristic curve (AUC) and determine run time on the overall dataset. Results: The highest average AUCs from fivefold cross-validation were 0.996 for ETT (RoBERTa), 0.994 for NGT (RoBERTa), 0.991 for CVC (PubMedBERT), and 0.98 for SGC (PubMedBERT). DeBERTa demonstrated the highest AUC for each support device trained on 5% of the training set. PubMedBERT showed a higher AUC with a decreasing training set size compared with BERT. Training and validation time was shortest for DistilBERT at 3 minutes 39 seconds on the annotated cohort. Conclusion: Pretrained and domain-specific transformer models required small training datasets and short training times to create a highly accurate final model that expedites autonomous annotation of large datasets.Keywords: Informatics, Named Entity Recognition, Transfer Learning Supplemental material is available for this article. ©RSNA, 2022See also the commentary by Zech in this issue.

Prevalence And Impact of Medical Comorbidities in A Real-World Lung Cancer Screening Population.

BACKGROUND: Lung cancer screening trials generally enroll motivated, relatively healthy, and adherent populations. We therefore evaluated the prevalence and effects of comorbidities in a real-world population undergoing low-dose computed tomography (LDCT) scans. PATIENTS AND METHODS: We calculated the Charlson Comorbidity Index (CCI) of patients for whom an initial low-dose computed tomography (LDCT) for lung cancer screening was ordered between February 2017 and February 2019 in an integrated safety-net healthcare system. We examined the association between CCI and initial LDCT completion using multivariable logistic regression, assessed the association between specific medical comorbidity and LDCT completion using Chi-square test or Fisher's exact test as appropriate, and examined the association between CCI and LDCT Lung-RADS results using Fisher's exact test. RESULTS: A total of 1358 patients were included in the analysis. Mean age was 63 years, 57% were women, and 50% were Black. Patients had moderate comorbidity burden (median CCI 3) with chronic pulmonary disease the most common comorbidity. Overall, 943 LDCT (70%) were completed. There was no difference in 30-day, 90-day, or 1-year completion rates of initial LDCT according to CCI. However, 30-day LDCT completion rates did increase over time (P < .001). Lung-RADS scores were not associated with CCI. CONCLUSION: In a real-world setting, patients undergoing lung cancer screening have moderate comorbidity burden. The degree and type of medical comorbidity are not associated with initial screening completion or results. Timeliness of LDCT completion may improve as program experience increases.

Active Reprioritization of the Reading Worklist Using Artificial Intelligence Has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage.

PURPOSE: To determine how to optimize the delivery of machine learning techniques in a clinical setting to detect intracranial hemorrhage (ICH) on non-contrast-enhanced CT images to radiologists to improve workflow. MATERIALS AND METHODS: In this study, a commercially available machine learning algorithm that flags abnormal noncontrast CT examinations for ICH was implemented in a busy academic neuroradiology practice between September 2017 and March 2019. The algorithm was introduced in three phases: (a) as a "pop-up" widget on ancillary monitors, (b) as a marked examination in reading worklists, and (c) as a marked examination for reprioritization based on the presence of the flag. A statistical approach, which was based on a queuing theory, was implemented to assess the impact of each intervention on queue-adjusted wait and turnaround time compared with historical controls. RESULTS: Notification with a widget or flagging the examination had no effect on queue-adjusted image wait (P > .99) or turnaround time (P = .6). However, a reduction in queue-adjusted wait time was observed between negative (15.45 minutes; 95% CI: 15.07, 15.38) and positive (12.02 minutes; 95% CI: 11.06, 12.97; P < .0001) artificial intelligence-detected ICH examinations with reprioritization. Reduced wait time was present for all order classes but was greatest for examinations ordered as routine for both inpatients and outpatients because of their low priority. CONCLUSION: The approach used to present flags from artificial intelligence and machine learning algorithms to the radiologist can reduce image wait time and turnaround times.© RSNA, 2021See also the commentary by O'Connor and Bhalla in this issue.

Capacity Prioritization Initiative Reduced the Wait Time for Port Placement and Facilitated Increased Volume of Port Placements at a Large County Health System.

DESCRIPTION OF THE PROBLEM: Wait time from request to placement of ports in interventional radiology had increased from 14 to 27 days over a 4-month period. The goal of this project was to reduce the wait time by 15% within 4 months while accommodating additional volume. INSTITUIONAL APPROACH TO ADDRESS PROBLEM: Capacity analysis revealed 2 bottlenecks: (1) inadequate provider capacity for preprocedural visits in interventional radiology clinic and (2) inadequate number of spots for port placement in the angiography schedule. The intervention consisted of: (1) 2 reserved slots in the attending physician's morning clinic schedule and (2) 3 daily guaranteed spots for port placement in the angiography suite. Both changes were integrated into the electronic medical record scheduling system. DESCRIPTION OF OUTCOMES: After the intervention, per biweekly period, the number of port requests increased by 17% (Preintervention: 16.6 ± 3.1, Postintervention: 20.1 ± 4.1, P = 0.03), the number of completed clinic visits increased by 19% (Preintervention: 16.7 ± 5.1, Postintervention: 20.5 ± 3.6, P = 0.05), and the number of port placements increased by 19% (Preintervention: 16.9 ± 3.9, Postintervention: 21.0 ± 3.5, P = 0.02). The average wait time from request to placement decreased by 22% (Preintervention: 22.2 ± 4.4 days, Postintervention: 18.3 ± 3.4 days, P = 0.03), driven by a 49% decrease in wait time between request and clinic visit (Preintervention: 11.0 ± 2.3 days, Postintervention: 7.4 ± 1.0 days, P = 0.03). CONCLUSIONS: Prioritization of clinic and angiography suite capacity, integrated into the electronic scheduling system, significantly reduced the wait time for port placement, even with significant increases in the volume of port requests.

Facilitating Surveillance of Incidental Findings Using a Novel Reporting Template: Proof of Concept in Patients With Pancreatic Abnormalities.

OBJECTIVE: To determine the surveillance impact of utilizing a discrete field in structured radiology reports in patients with incidental pancreatic findings. METHODS: We implemented a dictation template containing a discrete structured field element to auto-trigger listing of patients with incidental pancreatic findings on a pancreas clinic registry in the electronic health record. We isolated CT and MRI reports with incidental pancreatic findings over a 24-month period. We stratified patients by presence or absence of the discrete field element in reports (flagged versus unflagged) and evaluated the impact of report flagging on likelihood of clinic follow-up, follow-up imaging, endoscopic ultrasound, surgical intervention, genetics referral, obtaining pathologic diagnosis, and time interval between index imaging to various outcomes. RESULTS: Patients with flagged reports were more likely to be seen or discussed in a pancreas clinic compared with those with unflagged reports (189 of 376, 50.3% versus 79 of 474, 16.7%; P <. 001). Patients with flagged reports were more likely to get follow-up imaging than patients with unflagged reports (188 of 376, 50.0% versus 121 of 474, 25.5%; P < .001) and were more likely to undergo appropriate management of actionable findings compared with patients in the unflagged group (23 of 62, 37.1% versus 28 of 129, 21.7%; P = .036). DISCUSSION: Implementation of a structured discrete field element for reporting of patients with incidental pancreatic findings had positive impact on surveillance measures and can be applied in other organ systems with established surveillance guidelines to standardize patient care.

Clinician Variation in Ordering and Completion of Low-Dose Computed Tomography for Lung Cancer Screening in a Safety-Net Medical System.

BACKGROUND: Less than 5% of eligible individuals in the United States undergo lung cancer screening. Variation in clinicians' participation in lung cancer screening has not been determined. PATIENTS AND METHODS: We studied medical providers who ordered ≥ 1 low-dose computed tomography (LDCT) for lung cancer screening from February 2017 through February 2019 in an integrated safety-net healthcare system. We analyzed associations between provider characteristics and LDCT orders and completion using chi-square, Fisher exact, and Student t tests, as well as ANOVA and multinomial logistic regression. RESULTS: Among an estimated 194 adult primary care physicians, 144 (74%) ordered at least 1 LDCT, as did 39 specialists. These 183 medical providers ordered 1594 LDCT (median, 4; interquartile range, 2-9). In univariate and multivariate models, family practice providers (P < .001) and providers aged ≥ 50 years (P = .03) ordered more LDCT than did other clinicians. Across providers, the median proportion of ordered LDCT that were completed was 67%. The total or preceding number of LDCT ordered by a clinician was not associated with the likelihood of LDCT completion. CONCLUSION: In an integrated safety-net healthcare system, most adult primary care providers order LDCT. The number of LDCT ordered varies widely among clinicians, and a substantial proportion of ordered LDCT are not completed.

A Comprehensive CT Radiation Dose Reduction and Protocol Standardization Program in a Complex, Tertiary Hospital System.

PURPOSE: To present our experience in reducing CT radiation doses in a complex tertiary health system through CT protocol standardization and optimization. METHODS: A CT radiation task force was created to reduce CT protocol heterogeneity and radiation doses. Redundant protocols were eliminated. By an iterative process, protocols with least radiation dose were identified. Radiation dose tracking software was used to store and analyze radiation doses. CT protocols were published in an intranet site after training of technologists. SOPs were established for maintaining and changing protocols. The radiation doses for each CT protocol before and after optimization were compared using geometric means. RESULTS: A total of 222 CT protocols were reviewed, with elimination of 86 protocols. One-year follow-up showed homogeneous protocols with lower radiation doses. The improvement in radiation doses ranged from 23% to 58% (P< 0.001). CONCLUSION: CT radiation dose reduction of up to 58% can be achieved by homogenizing and optimizing CT protocols through a comprehensive CT operations program.