Impact of Predictive Text Clinical Decision Support on Imaging Order Entry in the Emergency Department.

PURPOSE: Imaging clinical decision support (CDS) is designed to assist providers in selecting appropriate imaging studies and is now federally required. The aim of this study was to understand the effect of CDS on decisions and workflows in the emergency department (ED). METHODS: The authors' institution's order entry platform serves up structured indications for imaging orders. Imaging orders are scored by CDS on the basis of appropriate use criteria (AUC). CDS triggers alerts for imaging orders with low AUC scores. Because free text alone cannot be scored by CDS, an artificial intelligence predictive text (AIPT) module was implemented to guide the selection of structured indications when free-text indications are entered. A total of 17,355 imaging orders in the ED over 6 months were retrospectively analyzed. RESULTS: CDS alerts for low AUC scores were triggered for 3% of all imaging study orders (522 of 17,355). Providers spent an average of 24 seconds interacting with alerts. In 18 of 522 imaging orders with alerts, alternative studies were ordered. After AIPT implementation, the percentage of unscored studies significantly decreased from 81% to 45% (P < .001). CONCLUSIONS: In a quaternary academic ED, CDS alerts triggered by low AUC scores caused minimal increase in time spent on imaging order entry but had a relatively marginal impact on imaging study selection. AIPT implementation increased the number of scored studies and could potentially enhance CDS effects. CDS implementation enables the collection of novel data regarding which imaging studies receive low AUC scores. Future work could include exploring alternative models of CDS implementation to maximize its impact.

Economic impact of selective use of contrast for routine follow-up MRI of patients with multiple sclerosis.

BACKGROUND AND PURPOSE: Imaging and autopsy studies show intracranial gadolinium deposition in patients who have undergone serial contrast-enhanced MRIs. This observation has raised concerns when using contrast administration in patients who receive frequent MRIs. To address this, we implemented a contrast-conditional protocol wherein gadolinium is administered only for multiple sclerosis (MS) patients with imaging evidence of new disease activity on precontrast imaging. In this study, we explore the economic impact of our new MRI protocol. METHODS: We compared scanner time and Medicare reimbursement using our contrast-conditional methodology versus that of prior protocols where all patients received gadolinium. RESULTS: For 422 patients over 4 months, the contrast-conditional protocol amounted to 60% decrease in contrast injection and savings of approximately 20% of MRI scanner time. If the extra scanner time was used for performing MS follow-up MRIs in additional patients, the contrast-conditional protocol would amount to net revenue loss of $21,707 (∼3.7%). CONCLUSIONS: Implementation of a new protocol to limit contrast in MS follow-up MRIs led to a minimal decrease in revenue when controlled for scanner time utilized and is outweighed by other benefits, including substantial decreased gadolinium administration, increased patient comfort, and increased availability of scanner time, which depending on type of studies performed could result in additional financial benefit.

99mTc-Mebrofenin SPECT/CT in Hepatic Infarction.

A 68-year-old man with hereditary hypercoagulability was referred to nuclear medicine for elevated aminotransferases after a recent living-donor liver transplant. A hepatic infarction was suspected. A Tc-mebrofenin SPECT/CT was performed and showed decreased radiotracer uptake in a wedge-shaped distribution in the anterior liver suggestive of a hepatic infarction. Subsequently, an enhanced MRI corroborated the diagnosis. Oral anticoagulation therapy was then initiated, and aminotransferases soon normalized.

Early Impact of Pennsylvania Act 112 on Follow-up of Abnormal Imaging Findings.

OBJECTIVE: Pennsylvania Act 112 requires diagnostic imaging facilities to directly notify outpatients about significant imaging abnormalities that require follow-up care within 3 months. The effects of Act 112 on patient care are unclear. We sought to characterize follow-up discussions and care received by outpatients with significant imaging abnormalities as defined by Act 112. METHODS: We evaluated findings flagged for patient notification under Act 112 at our institution over a 1-month period. We analyzed findings for radiologic reporting, follow-up discussions between patients and ordering providers, and follow-up medical care provided. RESULTS: Follow-up discussions were documented for 87% of findings (n = 205 of 235) and occurred on average 6.0 days after imaging examinations were performed. Follow-up discussions directly attributable to the Act 112 letter occurred in 0.4% of findings. Follow-up care was provided for 74% of findings on average 31.3 days after imaging examinations were performed. Provider-initiated follow-up discussions occurred earlier and were associated with shorter time to follow-up care when compared with patient-initiated discussions. Direct contact of ordering provider by interpreting radiologist was a significant predictor of occurrence of follow-up discussions and length of time to follow-up care. DISCUSSION: Act 112 had a small impact at our institution on improving completed follow-up for abnormal imaging findings. Our results also imply that health systems should encourage timeliness of patient-provider discussions of abnormal imaging findings and facilitate direct radiologist communication with ordering providers. Future studies should evaluate the impact of Act 112 in different practice settings to understand its broader impact on follow-up care.

Denoising the speaking brain: toward a robust technique for correcting artifact-contaminated fMRI data under severe motion.

A comprehensive set of methods based on spatial independent component analysis (sICA) is presented as a robust technique for artifact removal, applicable to a broad range of functional magnetic resonance imaging (fMRI) experiments that have been plagued by motion-related artifacts. Although the applications of sICA for fMRI denoising have been studied previously, three fundamental elements of this approach have not been established as follows: 1) a mechanistically-based ground truth for component classification; 2) a general framework for evaluating the performance and generalizability of automated classifiers; and 3) a reliable method for validating the effectiveness of denoising. Here we perform a thorough investigation of these issues and demonstrate the power of our technique by resolving the problem of severe imaging artifacts associated with continuous overt speech production. As a key methodological feature, a dual-mask sICA method is proposed to isolate a variety of imaging artifacts by directly revealing their extracerebral spatial origins. It also plays an important role for understanding the mechanistic properties of noise components in conjunction with temporal measures of physical or physiological motion. The potentials of a spatially-based machine learning classifier and the general criteria for feature selection have both been examined, in order to maximize the performance and generalizability of automated component classification. The effectiveness of denoising is quantitatively validated by comparing the activation maps of fMRI with those of positron emission tomography acquired under the same task conditions. The general applicability of this technique is further demonstrated by the successful reduction of distance-dependent effect of head motion on resting-state functional connectivity.