Patterns of Screening Recall Behavior Among Subspecialty Breast Radiologists.

RATIONALE AND OBJECTIVES: Determine whether there are patterns of lesion recall among breast imaging subspecialists interpreting screening mammography, and if so, whether recall patterns correlate to morphologies of screen-detected cancers. MATERIALS AND METHODS: This Institutional Review Board-approved, retrospective review included all screening examinations January 3, 2012-October 1, 2018 interpreted by fifteen breast imaging subspecialists at a large academic medical center and two outpatient imaging centers. Natural language processing identified radiologist recalls by lesion type (mass, calcifications, asymmetry, architectural distortion); proportions of callbacks by lesion types were calculated per radiologist. Hierarchical cluster analysis grouped radiologists based on recall patterns. Groups were compared to overall practice and each other by proportions of lesion types recalled, and overall and lesion-specific positive predictive value-1 (PPV1). RESULTS: Among 161,859 screening mammograms with 13,086 (8.1%) recalls, Hierarchical cluster analysis grouped 15 radiologists into five groups. There was substantial variation in proportions of lesions recalled: calcifications 13%-18% (Chi-square 45.69, p < 0.00001); mass 16%-44% (Chi-square 498.42, p < 0.00001); asymmetry 13%-47% (Chi-square 660.93, p < 0.00001) architectural distortion 6%-20% (Chi-square 283.81, p < 0.00001). Radiologist groups differed significantly in overall PPV1 (range 5.6%-8.8%; Chi-square 17.065, p = 0.0019). PPV1 by lesion type varied among groups: calcifications 9.2%-15.4% (Chi-square 2.56, p = 0.6339); mass 5.6%-8.5% (Chi-square 1.31, p = 0.8597); asymmetry 3.4%-5.9% (Chi-square 2.225, p = 0.6945); architectural distortion 5.6%-10.8% (Chi-square 5.810, p = 0.2138). Proportions of recalled lesions did not consistently correlate to proportions of screen-detected cancer. CONCLUSION: Breast imaging subspecialists have patterns for screening mammography recalls, suggesting differential weighting of imaging findings for perceived malignant potential. Radiologist recall patterns are not always predictive of screen-detected cancers nor lesion-specific PPV1s.

Representing narrative evidence as clinical evidence logic statements.

Objective: Clinical evidence logic statements (CELS) are shareable knowledge artifacts in a semistructured "If-Then" format that can be used for clinical decision support systems. This project aimed to assess factors facilitating CELS representation. Materials and Methods: We described CELS representation of clinical evidence. We assessed factors that facilitate representation, including authoring instruction, evidence structure, and educational level of CELS authors. Five researchers were tasked with representing CELS from published evidence. Represented CELS were compared with the formal representation. After an authoring instruction intervention, the same researchers were asked to represent the same CELS and accuracy was compared with that preintervention using McNemar's test. Moreover, CELS representation accuracy was compared between evidence that is structured versus semistructured, and between CELS authored by specialty-trained versus nonspecialty-trained researchers, using χ2 analysis. Results: 261 CELS were represented from 10 different pieces of published evidence by the researchers pre- and postintervention. CELS representation accuracy significantly increased post-intervention, from 20/261 (8%) to 63/261 (24%, P value < .00001). More CELS were assigned for representation with 379 total CELS subsequently included in the analysis (278 structured and 101 semistructured) postintervention. Representing CELS from structured evidence was associated with significantly higher CELS representation accuracy (P = .002), as well as CELS representation by specialty-trained authors (P = .0004). Discussion: CELS represented from structured evidence had a higher representation accuracy compared with semistructured evidence. Similarly, specialty-trained authors had higher accuracy when representing structured evidence. Conclusion: Authoring instructions significantly improved CELS representation with a 3-fold increase in accuracy. However, CELS representation remains a challenging task.

Integration of a Community Radiology Division into a Subspecialty-Focused Academic Radiology Department.

INTRODUCTION: Assimilate a general radiology division into a subspecialty-focused radiology department at an academic medical center. METHODS: This Institutional Review Board-approved quality improvement initiative was performed at an academic medical centers' subspecialty-focused academic radiology department, aiming to assimilate a general radiology division providing interpretive services for a distributed set of community ambulatory practices. An Oversight Committee charged by the department chair created a charter with unambiguous goal, timelines, clear decision-making, and conflict resolution processes. The Committee assessed the resources and clinical capabilities of the general radiologists, and the anticipated shift in exam volume from the community into subspecialty divisions. Primary outcome, percentage of targeted organ systems-specific interpretations by general radiologists based on assigned subspecialty division, and secondary outcome of report turnaround time (TAT) for all ambulatory exams, were compared before and after sub-specialization. RESULTS: Among 10 general radiologists, 4.5 were assigned to subspecialty divisions; 5.5 continued to cover an independent general radiology practice in a for-profit delivery network. In the 5 months' post-transition, a total 86.6% (11,668/13,477) of reports by the integrated general radiologists were within designated subspecialty divisions vs 23.9% (2,586/10,829) pre-transition (P < 0.01). There was no change in ambulatory radiology report TAT for non-urgent care center (UCC) or UCC exams pre- vs post-integration. DISCUSSION: A quality improvement initiative with unambiguous decision-making and conflict resolution processes incorporated a general radiology practice (radiologists and exams) into a subspecialty-focused academic radiology practice without negatively impacting TAT metrics. Future studies would be needed to assess impact on quality of interpretations.

Predictors of malignancy in incidental adnexal lesions identified on CT in patients with prior non-ovarian cancer.

PURPOSE: To identify imaging features in incidental adnexal lesions which are associated with malignancy on portal venous phase contrast-enhanced CT in patients with known non-ovarian cancer. MATERIALS AND METHODS: This IRB-approved, HIPAA-compliant retrospective study was performed at a tertiary cancer center. Portal venous phase contrast-enhanced CT from January 2010 to December 2015 was reviewed to identify women with non-ovarian malignancy and incidental adnexal lesion, with mean 18 months (range 1-80 months) to definitive diagnosis or last imaging follow-up. Imaging features of adnexal lesions were recorded (size, laterality, shape, attenuation, and composition) and correlated with outcome (benign or malignant) using univariate and multivariate logistic regression analysis. A point-based system was used to predict likelihood of malignancy. RESULTS: Of 276 women (mean age 45 years), 216 (78.3%) had benign lesions, 58 (21.0%) ovarian metastasis, and 2 (0.7%) had primary ovarian malignancy. On logistic regression model, lesion size > 5 cm (p-value, OR, 95% CI 0.01, 9.11, 1.70-48.87), bilaterality (< 0.0001, 28.34, 7.46-107.67), irregular shape (0.01, 12.31, 1.61-94.05), higher-than-simple-fluid attenuation (< 0.0001, 28.27, 5.65-141.59), and heterogeneous composition (0.0017, 10.75, 2.45-47.23) were associated with malignant outcome (AUC 0.97). A point-based system incorporating these five features (possible 0-5 points) had AUC of 0.97. Rate of malignancy was 0% (0/147) if none of the features of malignancy were present, 12.7% (8/63) if one feature was present, 51.7% (15/29) if two features were present, and 100% (37/37) if three or more features present. CONCLUSION: Risk of malignancy of incidental adnexal lesions in women with prior non-ovarian cancer can be estimated based on lesion features seen on portal venous phase contrast-enhanced CT.

Electronic Worklist Improves Timeliness of Screening Mammogram Interpretation in an Urban Underserved Population.

OBJECTIVES: To evaluate the impact of an electronic workflow update on screening mammography turnaround time and time to diagnostic imaging for mammography performed on our urban mobile mammography van and at an urban community health center. METHOD: Prior to 10/15/2019, screening exams for the mammography van and urban community health center were made available for interpretation to a single designated radiologist via a manually generated paper list. On 10/15/2019, screening exams were routed electronically onto PACS for any breast radiologist across our Network to interpret. Screening mammogram turnaround time (defined as time form image acquisition to report finalization), time to diagnostic imaging, and time to tissue sampling were collected for pre- and post-implementation periods (6/1-9/30/2019 and 11/1/2019-2/29/2020, respectively) and compared via student t-test and statistical process control analyses. RESULTS: The number of screening exams in the pre- and post-implementation periods were 851 and 728 exams, respectively. Patients were predominately Black and/or African American (400/1579, 25%), non-English speaking (858/1579, 54%) and insured by Medicaid (751/1579, 48%). After implementation of the electronic workflow, turnaround time decreased from 101.0 to 36.4 hours (63.9%, P <0.001) and statistical process control analyses showed sustained decrease in mean turnaround time. However, mean time to diagnostic imaging and tissue sampling were unchanged after implementation (39 vs 45, days; P = 0.330 and 43 vs 59; P = 0.187, respectively). CONCLUSION: Electronic workflow management can reduce screening mammography turnaround time for underserved populations, but additional efforts are warranted to improve time to imaging follow-up for abnormal screening mammograms.

Variation in Radiologists' Follow-Up Imaging Recommendations for Small Cystic Pancreatic Lesions.

OBJECTIVE: This study aimed to determine the incidence, identify imaging and patient factors, and measure individual radiologist variation associated with follow-up recommendations for small focal cystic pancreatic lesions (FCPLs), a common incidental imaging finding. METHODS: This institutional review board-approved retrospective study analyzed 146,709 reports from abdominal CTs and MRIs performed in a large academic hospital from July 1, 2016, to June 30, 2018. A trained natural language processing tool identified 4,345 reports with FCPLs, which were manually reviewed to identify those containing one or more <1.5-cm pancreatic cysts. For these patients, patient, lesion, and radiologist features and follow-up recommendations for FCPL were extracted. A nonlinear random-effects model estimated degree of variation in follow-up recommendations across radiologists at department and division levels. RESULTS: Of 2,872 reports with FCPLs < 1.5 cm, 708 (24.7%) had FCPL-related follow-up recommendations. Average patient age was 67 years (SD ± 11). In all, 1,721 (60.0%) reports were for female patients; 59.3% of patients had only one cyst. In multivariable analysis, older patients had slightly lower follow-up recommendation rates (odds ratio [OR]: 0.98 [0.98-1.00] per additional year), and lesions associated with main duct dilatation and septation were more likely to have a follow-up recommendation (ORs: 1.93 [1.11-3.36] and 2.88 [1.89-4.38], respectively). Radiologist years in practice (P = .51), trainee presence (P = .21), and radiologist gender (P = .52) were not associated with increased follow-up recommendations. There was significant interradiologist variation in the Abdominal Imaging Division (P = .04), but not in Emergency Radiology (P = .31) or Cancer Imaging Divisions (P = .29). DISCUSSION: Interradiologist variation significantly contributes to variability in follow-up imaging recommendations for FCPLs.

Radiologist Variation in the Rates of Follow-up Imaging Recommendations Made for Pulmonary Nodules.

OBJECTIVE: Determine whether differences exist in rates of follow-up recommendations made for pulmonary nodules after accounting for multiple patient and radiologist factors. METHODS: This Institutional Review Board-approved, retrospective study was performed at an urban academic quaternary care hospital. We analyzed 142,001 chest and abdominal CT reports from January 1, 2016, to December 31, 2018, from abdominal, thoracic, and emergency radiology subspecialty divisions. A previously validated natural language processing (NLP) tool identified 24,512 reports documenting pulmonary nodule(s), excluding reports NLP-positive for lung cancer. A second validated NLP tool identified reports with follow-up recommendations specifically for pulmonary nodules. Multivariable logistic regression was used to determine the likelihood of pulmonary nodule follow-up recommendation. Interradiologist variability was quantified within subspecialty divisions. RESULTS: NLP classified 4,939 of 24,512 (20.1%) reports as having a follow-up recommendation for pulmonary nodule. Male patients comprised 45.3% (11,097) of the patient cohort; average patient age was 61.4 years (±14.1 years). The majority of reports were from outpatient studies (62.7%, 15,376 of 24,512), were chest CTs (75.9%, 18,615 of 24,512), and were interpreted by thoracic radiologists (63.7%, 15,614 of 24,512). In multivariable analysis, studies for male patients (odds ratio [OR]: 0.9 [0.8-0.9]) and abdominal CTs (OR: 0.6 [0.6-0.7] compared with chest CT) were less likely to have a pulmonary nodule follow-up recommendation. Older patients had higher rates of follow-up recommendation (OR: 1.01 for each additional year). Division-level analysis showed up to 4.3-fold difference between radiologists in the probability of making a follow-up recommendation for a pulmonary nodule. DISCUSSION: Significant differences exist in the probability of making a follow-up recommendation for pulmonary nodules among radiologists within the same subspecialty division.

Screening Mammography Performance Metrics of 2D Digital Mammography Versus Digital Breast Tomosynthesis in Women With a Personal History of Breast Cancer.

BACKGROUND. Patients with a history of breast cancer are at higher risk of subsequent breast cancers and need close clinical and imaging follow-up. Limited data are available on screening of these patients with digital breast tomosynthesis (DBT) versus full-field digital mammography (FFDM). OBJECTIVE. The purpose of this study was to evaluate the screening mammography performance of DBT compared with FFDM among patients with a history of breast cancer undergoing imaging at a large academic oncology center. METHODS. This retrospective study included consecutively registered patients with a personal history of breast cancer treated with mastectomy or lumpectomy who underwent screening FFDM from October 2014 through September 2016 (5706 examinations of 4091 patients) or screening DBT from February 2017 through December 2018 (4440 examinations of 3647 patients). An institutional mammographic database was queried to obtain imaging type, breast density, history of mastectomy or lumpectomy, and BI-RADS category. An institutional breast cancer registry identified cancer diagnoses. Screening performance metrics were compared between FFDM and DBT groups. RESULTS. Recall rate was significantly lower with DBT than with FFDM (7.9% vs 10.1%; p < .001). DBT and FFDM did not differ in PPV1 (7.7% vs 6.1%; p = .36) or cancer detection rate (CDR) (6.1/1000 vs 6.0/1000; p = .97). Sensitivity was 96.4% for DBT and 71.4% for FFDM (p = .008). Specificity was 92.3% for DBT and 90.0% for FFDM (p < .001). With stratification by breast density, patients with nondense breast tissue had a lower recall rate with DBT than with FFDM (5.9% vs 8.8%; p < .001) and a nonsignificant increase in PPV1 (12.0% vs 6.4%; p = .05). The metrics were not otherwise different between DBT and FFDM among patients with nondense and those with dense breast tissue. Recall rates were lower with DBT than with FFDM among both patients who underwent mastectomy (7.8% vs 9.1%; p = .09) and those who underwent lumpectomy (7.9% vs 11.0%; p = .002). PPV1 and CDR were not different between DBT and FFDM among patients who underwent mastectomy and those who underwent lumpectomy. CONCLUSION. For patients with a personal history of breast cancer who have nondense breasts, the use of DBT as opposed to FFDM reduces recall rate and improves sensitivity and specificity. CDR and PPV1 remain unchanged. CLINICAL IMPACT. For women with a personal history of breast cancer and nondense breasts, DBT offers the potential to maintain the benefits of early cancer detection while reducing the potential harms of false-positive findings.

Clinical validation demonstrates concordance of qSOFA and POC lactate Bayesian model: Results from the ACDC Phase-2 program.

Sepsis is a common and lethal medical problem. The objective of this study was to validate a Bayesian Model that integrates qSOFA and prehospital Lactate, with a comparison analysis from a real clinical data of patients with sepsis. METHODS: We conducted a two tired validation study with one arm focusing on Bayesian modeling and a second retrospective observational arm addressing real data validation. For Bayesian modeling, sensitivity and specificity of prehospital lactate were attained from pooled meta-analysis data. Later, for clinical validation, we used data from 2016 to 2017 of ED patients diagnosed with sepsis. Pretest probabilities from qSOFA score where combined with prehospital lactate and inserted into a Bayesian model to calculate posttest probabilities. Absolute and relative diagnostic gains were calculated. Statistical significance was assessed via t-test, chi square and odds ratio. P value was set to be 0.05. RESULTS: For the Bayesian arm; meta-analysis data for prehospital lactate resulted in a positive likelihood ratio (LR+) of 1.69 and negative likelihood ratio (LR-) of 0.44. Integration of lactate and qSOFA demonstrated significant post-test improvements. On the Clinical Validation arm, 1470 patients were included with 176 patients meeting analysis criteria. When comparing qSOFA + Abnormal Lactate vs qSOFA and normal Lactate, the ICU vs Non-ICU cohorts were statistically different (p < 0.01) Odds Ratio: 2.35 (95% CI [1.22-4.6]). CONCLUSION: Bayesian mathematical model demonstrated that a qSOFA-based clinical decision can be complemented by the use of point of-care lactate. These results were confirmed by our clinical validation arm.

Integrity of clinical information in radiology reports documenting pulmonary nodules.

OBJECTIVE: Quantify the integrity, measured as completeness and concordance with a thoracic radiologist, of documenting pulmonary nodule characteristics in CT reports and assess impact on making follow-up recommendations. MATERIALS AND METHODS: This Institutional Review Board-approved, retrospective cohort study was performed at an academic medical center. Natural language processing was performed on radiology reports of CT scans of chest, abdomen, or spine completed in 2016 to assess presence of pulmonary nodules, excluding patients with lung cancer, of which 300 reports were randomly sampled to form the study cohort. Documentation of nodule characteristics were manually extracted from reports by 2 authors with 20% overlap. CT images corresponding to 60 randomly selected reports were further reviewed by a thoracic radiologist to record nodule characteristics. Documentation completeness for all characteristics were reported in percentage and compared using χ2 analysis. Concordance with a thoracic radiologist was reported as percentage agreement; impact on making follow-up recommendations was assessed using kappa. RESULTS: Documentation completeness for pulmonary nodule characteristics differed across variables (range = 2%-90%, P < .001). Concordance with a thoracic radiologist was 75% for documenting nodule laterality and 29% for size. Follow-up recommendations were in agreement in 67% and 49% of reports when there was lack of completeness and concordance in documenting nodule size, respectively. DISCUSSION: Essential pulmonary nodule characteristics were under-reported, potentially impacting recommendations for pulmonary nodule follow-up. CONCLUSION: Lack of documentation of pulmonary nodule characteristics in radiology reports is common, with potential for compromising patient care and clinical decision support tools.

Predictors of Self-Reported Burnout Among Radiology Faculty at a Large Academic Medical Center.

OBJECTIVE: Determine predictors of self-reported burnout among academic radiologists. METHODS: In 2017, radiologists at an urban medical center completed the Stanford Wellness Survey, rating burnout via Likert scale (0: no burnout; 1: occasional stress, no burnout; 2: one or more burnout symptoms; 3: persistent burnout symptoms; 4: completely burned out). Univariate analyses assessed age, gender, family situation, clinical versus research focus, and academic rank for association with burnout (Likert scale ≥ 2). Responses in 11 domains querying definitions of burnout (professional fulfillment, emotional exhaustion, interpersonal disengagement), individual factors (sleep-related impairment, self-compassion, negative work impact on personal relationships), institutional factors (perceived appreciation, control over schedule, organizational or personal values alignment, electronic health record experience, supervisor's leadership quality) were evaluated for association with burnout, using χ2 and logistic regression to calculate odds ratios (ORs). RESULTS: In 159 of 204 (77.9%) completed radiologist surveys, 35.2% (56 of 159) reported burnout. Age < 40 years (P = .0068) and clinical focus (P = .0111) were significantly associated with burnout. In univariate analysis, all domains except electronic health record were statistically significant: emotional exhaustion (OR = 1.93, P < .0001); professional fulfillment (OR = 0.78, P < .0001); self-compassion (OR = 1.36, P < .0001); perceived appreciation (OR = 0.78, P < .0001); sleep-related impairment (OR = 1.20, P < .0001); supervisor's leadership quality (OR = 0.91, P < .0001); interpersonal disengagement (OR = 1.31, P < .0001); organizational or personal values alignment (OR = 0.87, P = .0004); negative work impact on personal relationships (OR = 1.10, P = .0070); control over schedule (OR = 0.80, P = .0054); electronic health record experience (OR=1.03, P = .5392). DISCUSSION: Nearly all questions significantly predicted self-reported burnout, observed in over one-third of academic radiologists. Younger age and clinical focus were associated with burnout. Initiatives targeting individual factors (eg, sleep impairment, self-compassion) and institutional factors (eg, physician appreciation, leadership-faculty interactions) may reduce burnout.

Comparing Diagnostic Performance of Digital Breast Tomosynthesis and Full-Field Digital Mammography.

OBJECTIVE: Compare diagnostic performance of screening full-field digital mammography (FFDM), a hybrid FFDM and digital breast tomosynthesis (DBT) environment, and DBT only. MATERIALS AND METHODS: This institutional review board-approved, retrospective study consisted of all patients undergoing screening mammography at an urban academic medical center and outpatient imaging facility between January 1, 2011, and December 31, 2017. We used the electronic health record data warehouse to extract report data and patient demographics. A validated natural language processing algorithm extracted BI-RADS score from each report. An institutional cancer registry identified cancer diagnoses. Primary outcomes of recall rate, cancer detection rate (CDR), and positive predictive value 1 (PPV1) were calculated for three periods: FFDM-only environment, hybrid environment, and DBT-only environment. A χ2 test was used to compare recall rate, CDR, and PPV1. RESULTS: A total of 179,028 screening mammograms comprised the study cohort: 41,818 (23.3%) during the FFDM-only period, 83,125 (46.4%) during the hybrid period, and 54,084 (30.2%) during the DBT-only period. Recall rates were 10.4% (4,279 of 41,280) for the FFDM-only period, 10.6% (8,761 of 82,917) for the hybrid period, and 10.8% (5,850 of 54,020) for the DBT-only period (P = .96). CDR (cancers per 1,000 examinations) was 2.6 per 1,000, 4.9 per 1,000, and 6.0 per 1,000 for FFDM only, hybrid, and DBT only, respectively (P < .01). PPV1s (number of cancers per number of recalls) were 2.5% for the FFDM-only period, 4.6% for the hybrid period, and 5.6% for the DBT-only period (P < .01). CONCLUSION: Recall rates were not significantly different within the three periods in the breast imaging practice. However, PPV1 and CDR were significantly higher with DBT only.

Radiologists' Self-Assessment Versus Peer Assessment of Perceived Probability of Recommending Additional Imaging.

OBJECTIVE: Determine radiologist ability to accurately select the probability of recommendation of additional imaging (RAI) for themselves and colleagues when arrayed in a feedback report. METHODS: In this institutional review board-approved study, we analyzed 318,366 diagnostic imaging reports from examinations performed in the radiology department of a large quaternary teaching hospital during calendar year 2016. A validated machine learning algorithm identified reports containing RAI. A multivariable logistic regression model was then used to determine the probability of RAI. In 2018, an e-mailed survey asked radiologists to identify their own RAI probability and that of their colleagues from a report arrayed lowest to highest. Radiologists were grouped into quartiles based on their RAI probability. χ2 Analysis compared self-assessment and assessment of colleagues between quartiles. RESULTS: Forty-eight of 57 radiologists completed the survey (84.2%). Fourteen (29.2%) accurately self-identified their RAI probability (chose the correct quartile); 34 (70.8%) did not. There was no statistically significant difference between quartiles of radiologists and their ability to self-identify their RAI probability (ie, radiologists in the bottom or top quartile of RAI probabilities did not correctly predict their RAI probability). However, radiologists were better able to identify the RAI probability of their colleagues who were in the top and bottom quartiles. DISCUSSION: Radiologists were unable to estimate their own RAI probability but were better at predicting the RAI probability of colleagues. Given that radiologists, and physicians in general, may be poor evaluators of their own performance, objective assessment tools are likely needed to help reduce unwarranted variation.

Unscheduled Radiologic Examination Orders in the Electronic Health Record: A Novel Resource for Targeting Ambulatory Diagnostic Errors in Radiology.

PURPOSE: The aim of this study was to assess the prevalence of unscheduled radiologic examination orders in an electronic health record, and the proportion of unscheduled orders that are clinically necessary, by modality. METHODS: This retrospective study was conducted from January to October 2016 at an academic institution. All unscheduled radiologic examination orders were retrieved for seven modalities (CT, MR, ultrasound, obstetric ultrasound, bone densitometry, mammography, and fluoroscopy). After excluding duplicates, 100 randomly selected orders from each modality were assigned to two physician reviewers who classified their clinical necessity, with 10% overlap. Interannotator agreement was assessed using κ statistics, the percentage of clinically necessary unscheduled orders was compared, and χ2 analysis was used to assess differences by modality. RESULTS: A total 494,503 radiologic examination orders were placed during the study period. After exclusions, 33,546 unscheduled orders were identified, 7% of all radiologic examination orders. Among 700 reviewed unscheduled orders, agreement was substantial (κ = 0.63). Eighty-seven percent of bone densitometric examinations and sixty-five percent of mammographic studies were considered clinically necessary, primarily for follow-up management. The majority of orders in each modality were clinically necessary, except for CT, obstetric ultrasound, and fluoroscopy (P < .0001). CONCLUSIONS: Large numbers of radiologic examination orders remain unscheduled in the electronic health record. A substantial portion are clinically necessary, representing potential delays in executing documented provider care plans. Clinically unnecessary unscheduled orders may inadvertently be scheduled and performed. Identifying and performing clinically necessary unscheduled radiologic examination orders may help reduce diagnostic errors related to diagnosis and treatment delays and enhance patient safety, while eliminating clinically unnecessary unscheduled orders will help avoid unneeded testing.

Factors Associated With Optimal Follow-up in Women With BI-RADS 3 Breast Findings.

OBJECTIVE: Assess rate of and factors associated with optimal follow-up in patients with BI-RADS 3 breast findings. METHODS: This Institutional Review Board-approved, retrospective cohort study, performed at an academic medical center, included all women undergoing breast imaging (ultrasound and mammography) in 2016. Index reports for unique patients with an assessment of BI-RADS 3 (retrieved via natural language processing) comprised the study population. Patient-specific and provider-related features were extracted from the Research Data Warehouse. The Institutional Cancer Registry identified patients diagnosed with breast cancer. Optimal follow-up rate was calculated as patients with follow-up imaging on the same breast 3 to 9 months from the index examination among patients with BI-RADS 3 assessments. Univariate analysis and multivariable logistic regression determined features associated with optimal follow-up. Malignancy rate and time to malignancy detection were recorded. RESULTS: Among 93,685 breast imaging examinations, 64,771 were from unique patients of which 2,967 had BI-RADS 3 findings (4.6%). Excluding patients with off-site index examinations and those with another breast examination <3 months from the index, 1,125 of 1,511 patients (74%) had optimal follow-up. In univariate and multivariable analysis, prior breast cancer was associated with optimal follow-up; younger age, Hispanic ethnicity, divorced status, and lack of insurance were associated with not having optimal follow-up. Malignancy rate was 0.86%, and mean time to detection was 330 days. DISCUSSION: Follow-up of BI-RADS 3 breast imaging findings is optimal in only 74% of women. Further interventions to promote follow-up should target younger, unmarried women, those with Hispanic ethnicity, and women without history of breast cancer and without insurance coverage.

Variation in Follow-up Imaging Recommendations in Radiology Reports: Patient, Modality, and Radiologist Predictors.

Background Variation between radiologists when making recommendations for additional imaging and associated factors are, to the knowledge of the authors, unknown. Clear identification of factors that account for variation in follow-up recommendations might prevent unnecessary tests for incidental or ambiguous image findings. Purpose To determine incidence and identify factors associated with follow-up recommendations in radiology reports from multiple modalities, patient care settings, and imaging divisions. Materials and Methods This retrospective study analyzed 318 366 reports obtained from diagnostic imaging examinations performed at a large urban quaternary care hospital from January 1 to December 31, 2016, excluding breast and US reports. A subset of 1000 reports were randomly selected and manually annotated to train and validate a machine learning algorithm to predict whether a report included a follow-up imaging recommendation (training-and-validation set consisted of 850 reports and test set of 150 reports). The trained algorithm was used to classify 318 366 reports. Multivariable logistic regression was used to determine the likelihood of follow-up recommendation. Additional analysis by imaging subspecialty division was performed, and intradivision and interradiologist variability was quantified. Results The machine learning algorithm classified 38 745 of 318 366 (12.2%) reports as containing follow-up recommendations. Average patient age was 59 years ± 17 (standard deviation); 45.2% (143 767 of 318 366) of reports were from male patients. Among 65 radiologists, 57% (37 of 65) were men. At multivariable analysis, older patients had higher rates of follow-up recommendations (odds ratio [OR], 1.01 [95% confidence interval {CI}: 1.01, 1.01] for each additional year), male patients had lower rates of follow-up recommendations (OR, 0.9; 95% CI: 0.9, 1.0), and follow-up recommendations were most common among CT studies (OR, 4.2 [95% CI: 4.0, 4.4] compared with radiography). Radiologist sex (P = .54), presence of a trainee (P = .45), and years in practice (P = .49) were not significant predictors overall. A division-level analysis showed 2.8-fold to 6.7-fold interradiologist variation. Conclusion Substantial interradiologist variation exists in the probability of recommending a follow-up examination in a radiology report, after adjusting for patient, examination, and radiologist factors. © RSNA, 2019 See also the editorial by Russell in this issue.

Can Automated Retrieval of Data from Emergency Department Physician Notes Enhance the Imaging Order Entry Process?

BACKGROUND: When a paucity of clinical information is communicated from ordering physicians to radiologists at the time of radiology order entry, suboptimal imaging interpretations and patient care may result. OBJECTIVES: Compare documentation of relevant clinical information in electronic health record (EHR) provider note to computed tomography (CT) order requisition, prior to ordering of head CT for emergency department (ED) patients presenting with headache. METHODS: In this institutional review board-approved retrospective observational study performed between April 1, 2013 and September 30, 2014 at an adult quaternary academic hospital, we reviewed data from 666 consecutive ED encounters for patients with headaches who received head CT. The primary outcome was the number of concept unique identifiers (CUIs) relating to headache extracted via ontology-based natural language processing from the history of present illness (HPI) section in ED notes compared with the number of concepts obtained from the imaging order requisition. RESULTS: Our analysis was conducted on cases where the HPI note section was completed prior to image order entry, which occurred in 23.1% (154/666) of encounters. For these 154 encounters, the number of CUIs specific to headache per note extracted from the HPI (median = 3, interquartile range [IQR]: 2-4) was significantly greater than the number of CUIs per encounter obtained from the imaging order requisition (median = 1, IQR: 1-2; Wilcoxon signed rank p < 0.0001). Extracted concepts from notes were distinct from order requisition indications in 92.9% (143/154) of cases. CONCLUSION: EHR provider notes are a valuable source of relevant clinical information at the time of imaging test ordering. Automated extraction of clinical information from notes to prepopulate imaging order requisitions may improve communication between ordering physicians and radiologists, enhance efficiency of ordering process by reducing redundant data entry, and may help improve clinical relevance of clinical decision support at the time of order entry, potentially reducing provider burnout from extraneous alerts.

A Bayesian decision support sequential model for severity of illness predictors and intensive care admissions in pneumonia.

BACKGROUND: Community-acquired pneumonia (CAP) is one of the leading causes of morbidity and mortality in the USA. Our objective was to assess the predictive value on critical illness and disposition of a sequential Bayesian Model that integrates Lactate and procalcitonin (PCT) for pneumonia. METHODS: Sensitivity and specificity of lactate and PCT attained from pooled meta-analysis data. Likelihood ratios calculated and inserted in Bayesian/ Fagan nomogram to calculate posttest probabilities. Bayesian Diagnostic Gains (BDG) were analyzed comparing pre and post-test probability. To assess the value of integrating both PCT and Lactate in Severity of Illness Prediction we built a model that combined CURB65 with PCT as the Pre-Test markers and later integrated the Lactate Likelihood Ratio Values to generate a combined CURB 65 + Procalcitonin + Lactate Sequential value. RESULTS: The BDG model integrated a CUBR65 Scores combined with Procalcitonin (LR+ and LR-) for Pre-Test Probability Intermediate and High with Lactate Positive Likelihood Ratios. This generated for the PCT LR+ Post-test Probability (POSITIVE TEST) Posterior probability: 93% (95% CI [91,96%]) and Post Test Probability (NEGATIVE TEST) of: 17% (95% CI [15-20%]) for the Intermediate subgroup and 97% for the high risk sub-group POSITIVE TEST: Post-Test probability:97% (95% CI [95,98%]) NEGATIVE TEST: Post-test probability: 33% (95% CI [31,36%]) . ANOVA analysis for CURB 65 (alone) vs CURB 65 and PCT (LR+) vs CURB 65 and PCT (LR+) and Lactate showed a statistically significant difference (P value = 0.013). CONCLUSIONS: The sequential combination of CURB 65 plus PCT with Lactate yielded statistically significant results, demonstrating a greater predictive value for severity of illness thus ICU level care.