Background Technological burden and medical complexity are significant drivers of clinician burnout. Electronic health record(EHR)-based population health management tools can be used to identify high-risk patient populations and implement prophylactic health practices. Their impact on clinician burnout, however, is not well understood. Our objective was to assess the relationship between ratings of EHR-based population health management tools and clinician burnout.Methods We conducted cross-sectional analyses of 2018 national Veterans Health Administration(VA) primary care personnel survey, administered as an online survey to all VA primary care personnel (n = 4257, response rate = 17.7%), using bivariate and multivariate logistic regressions. Our analytical sample included providers (medical doctors, nurse practitioners, physicians' assistants) and nurses (registered nurses, licensed practical nurses). The outcomes included two items measuring high burnout. Primary predictors included importance ratings of 10 population health management tools (eg. VA risk prediction algorithm, recent hospitalizations and emergency department visits, etc.).Results High ratings of 9 tools were associated with lower odds of high burnout, independent of covariates including VA tenure, team role, gender, ethnicity, staffing, and training. For example, clinicians who rated the risk prediction algorithm as important were less likely to report high burnout levels than those who did not use or did not know about the tool (OR 0.73; CI 0.61-0.87), and they were less likely to report frequent burnout (once per week or more) (OR 0.71; CI 0.60-0.84).Conclusions Burned-out clinicians may not consider the EHR-based tools important and may not be using them to perform care management. Tools that create additional technological burden may need adaptation to become more accessible, more intuitive, and less burdensome to use. Finding ways to improve the use of tools that streamline the work of population health management and/or result in less workload due to patients with poorly managed chronic conditions may alleviate burnout. More research is needed to understand the causal directional of the association between burnout and ratings of population health management tools.

Wang, Jane MD; Andreatos, Nikolaos MD; Margonis, Georgios Antonios MD, PhD Author Information

Background Although the value of Rat Sarcoma Oncogene (RAS) mutation status in predicting long-term outcomes in patients with colorectal liver metastases (CRLM) is widely accepted, the magnitude of its impact has recently been challenged by three large cohort studies. The aim of this meta-analysis is to reevaluate the impact of RAS mutations on overall survival (OS) and disease-free survival (DFS) in patients who underwent curative-intent resection of CRLM. Methods A comprehensive literature search was performed for studies reporting outcomes of patients undergoing curative-intent surgery stratified by RAS mutation status. Exclusion criteria were defined a priori. Subgroup analysis was performed to evaluate the effect of publication date, sample size, and KRAS vs any RAS mutation on overall outcomes. Results Ten studies incorporating 3115 patients with known RAS status were identified. Pooled results revealed significantly worse OS (Hazard Ratio 1.5, 95% CI 1.31-1.71) and DFS (Hazard Ratio 1.36, 95% CI 1.22-1.52) in RAS-mutated patients. Subgroup analyses revealed that studies including more than 300 patients or published after 2015 reported lower HR than their counterparts. Conclusion The results of this meta-analysis suggest that the prognostic value of RAS mutation status in patients with CRLM has been previously overestimated.

This article proposes a consensus nomenclature for fat‐containing renal and adrenal masses at MRI to reduce variability, improve understanding, and enhance communication when describing imaging findings. The MRI appearance of "macroscopic fat" occurs due to a sufficient number of aggregated adipocytes and results in one or more of: 1) intratumoral signal intensity (SI) loss using fat‐suppression techniques, or 2) chemical shift artifact of the second kind causing linear or curvilinear India‐ink (etching) artifact within or at the periphery of a mass at macroscopic fat–water interfaces. "Macroscopic fat" is most commonly observed in adrenal myelolipoma and renal angiomyolipoma (AML) and only rarely encountered in other adrenal cortical tumors and renal cell carcinomas (RCC). Nonlinear noncurvilinear signal intensity loss on opposed‐phase (OP) compared with in‐phase (IP) chemical shift MRI (CSI) may be referred to as "microscopic fat" and is due to: a) an insufficient amount of adipocytes, or b) the presence of fat within tumor cells. Determining whether the signal intensity loss observed on CSI is due to insufficient adipocytes or fat within tumor cells cannot be accomplished using CSI alone; however, it can be inferred when other imaging features strongly suggest a particular diagnosis. Fat‐poor AML are homogeneously hypointense on T2‐weighted (T2W) imaging and avidly enhancing; signal intensity loss at OP CSI is uncommon, but when present is usually focal and is caused by an insufficient number of adipocytes within adjacent voxels. Conversely, clear‐cell RCC are heterogeneously hyperintense on T2W imaging and avidly enhancing, with the signal intensity loss observed on OP CSI being typically diffuse and due to fat within tumor cells. Adrenal adenomas, adrenal cortical carcinoma, and adrenal metastases from fat‐containing primary malignancies also show signal intensity loss on OP CSI due to fat within tumor cells and not from intratumoral adipocytes.Level of Evidence: 5Technical Efficacy Stage: 3J. Magn. Reson. Imaging 2019;49:917–926.

OBJECTIVE:Our purpose was to assess whether histogram analysis of adrenal lesions from a single measurement of mean attenuation and SD, using a threshold of 10% of negative voxels, can replace voxel counting while maintaining diagnostic accuracy.MATERIALS AND METHODS:In a 4-year period, 325 adrenal lesions were detected on CT examinations of 308 consecutive patients. After exclusions, 91 patients with 108 lesions, including 20 malignant lesions and 88 adenomas (defined by histologic results or follow-up), were enrolled. Two observers retrospectively measured lesion size, mean attenuation value, and SD attenuation value and generated a pixel histogram. The 10th percentile (P10) was obtained from the conventional histogram analysis and was also calculated from the following formula: P10 = mean attenuation - (1.282 × SD). Diagnostic accuracies of the mean attenuation criterion, histogram analysis, and calculated 10th percentile were compared.RESULTS:The study group was composed of 74 patients with 88 adenomas and 17 patients with 20 malignant lesions, including seven adrenocortical carcinomas and 13 metastases; 93.1% of histograms showed normal distribution. The correlation between histogram analysis and calculated 10th percentile was 0.9827 and 0.9843 for reader 1 and 2 (p < 0.00001 for both). For both readers, sensitivity and specificity of the mean attenuation analysis were 65.9% (95% CI, 55.0-75.7%) and 100.0% (95% CI, 83.2-100%). The sensitivity and specificity of histogram analysis and calculated 10th percentile were the same, 87.5% (95% CI, 78.7-93.6%) and 95.0% (95% CI, 75.1-99.8%), for both readers. The increment increase in sensitivity was significant (p < 0.001), whereas the decrease in specificity was not (p = 0.15).CONCLUSION:For most adrenal lesions, the pixel attenuation has a gaussian distribution, allowing estimation of 10th percentile with a single measurement. The accuracy of histogram analysis and calculated 10th percentile outperformed the mean attenuation as a diagnostic criterion for nonfunctioning adenomas.