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Who explicitly requests the ordering of computed tomography for emergency department patients? A multicenter prospective study.

ABSTRACT: Emergency department (ED) computed tomography (CT) use has increased substantially in recent years, resulting in increased radiation exposure for patients. Few studies have assessed which parties contribute to CT ordering in the ED. The objective of this study was to determine the proportion of CT scans ordered due to explicit requests by various stakeholders in ED patient care. This is a prospective, observational study performed at three university hospital EDs. CT scans ordered during research assistant hours were eligible for inclusion. Attending emergency physicians (EPs) completed standardized data forms to indicate all parties who had explicitly requested that a specific CT be performed. Forms were completed before the CT results were known in order to minimize bias. Data were obtained from 77 EPs regarding 944 CTs. The parties most frequently requesting CTs were attending EPs (82.0 %, 95 % CI 79.4-84.3), resident physicians (28.6 %, 95 % CI 25.8-31.6), consulting physicians (24.4 %, 95 % CI 21.7-27.2), and admitting physicians (3.9 %, 95 % CI 2.9-5.4). In the 168 instances in which the attending EP did not explicitly request the CT, requests most commonly came from consulting physicians (51.2 %, 95 % CI 43.7-58.6), resident physicians in the ED (39.9 %, 95 % CI 32.8-47.4), and admitting physicians (8.9 %, 95 % CI 5.5-14.2). EPs were the sole party requesting CT in 46.2 % of cases while multiple parties were involved in 39.0 %. Patients, families, and radiologists were uncommon sources of such requests. Emergency physicians requested the majority of CTs, though nearly 20 % were actually not desired by them. Admitting, consulting, and resident physicians in the ED were important contributors to CT utilization.

SUBMITTER: Broder JS

PROVIDER: S-EPMC4876053 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Who explicitly requests the ordering of computed tomography for emergency department patients? A multicenter prospective study.

Broder Joshua Seth JS Bhat Rahul R Boyd Joshua P JP Ogloblin Ivan A IA Limkakeng Alexander A Hocker Michael Brian MB Drake Weiying Gao WG Miller Taylor T Harringa John Brian JB Repplinger Michael D MD

Emergency radiology 20160212 3

Emergency department (ED) computed tomography (CT) use has increased substantially in recent years, resulting in increased radiation exposure for patients. Few studies have assessed which parties contribute to CT ordering in the ED. The objective of this study was to determine the proportion of CT scans ordered due to explicit requests by various stakeholders in ED patient care. This is a prospective, observational study performed at three university hospital EDs. CT scans ordered during researc ...[more]

PMID: 26873604

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Project description:BACKGROUND:Reliably abstracting outcomes from free-text electronic health records remains a challenge. While automated classification of free text has been a popular medical informatics topic, performance validation using real-world clinical data has been limited. The two main approaches are linguistic (natural language processing [NLP]) and statistical (machine learning). The authors have developed a hybrid system for abstracting computed tomography (CT) reports for specified outcomes. OBJECTIVES:The objective was to measure performance of a hybrid NLP and machine learning system for automated outcome classification of emergency department (ED) CT imaging reports. The hypothesis was that such a system is comparable to medical personnel doing the data abstraction. METHODS:A secondary analysis was performed on a prior diagnostic imaging study on 3,710 blunt facial trauma victims. Staff radiologists dictated CT reports as free text, which were then deidentified. A trained data abstractor manually coded the reference standard outcome of acute orbital fracture, with a random subset double-coded for reliability. The data set was randomly split evenly into training and testing sets. Training patient reports were used as input to the Medical Language Extraction and Encoding (MedLEE) NLP tool to create structured output containing standardized medical terms and modifiers for certainty and temporal status. Findings were filtered for low certainty and past/future modifiers and then combined with the manual reference standard to generate decision tree classifiers using data mining tools Waikato Environment for Knowledge Analysis (WEKA) 3.7.5 and Salford Predictive Miner 6.6. Performance of decision tree classifiers was evaluated on the testing set with or without NLP processing. RESULTS:The performance of machine learning alone was comparable to prior NLP studies (sensitivity = 0.92, specificity = 0.93, precision = 0.95, recall = 0.93, f-score = 0.94), and the combined use of NLP and machine learning showed further improvement (sensitivity = 0.93, specificity = 0.97, precision = 0.97, recall = 0.96, f-score = 0.97). This performance is similar to, or better than, that of medical personnel in previous studies. CONCLUSIONS:A hybrid NLP and machine learning automated classification system shows promise in coding free-text electronic clinical data.

Project description:AimsCoronary computed tomography angiography is increasingly employed in the emergency department for suspected acute coronary syndrome patients. The HEART score has been proposed for initial risk stratification in these patients. The aim of this study was to investigate the diagnostic value and efficiency of the HEART score before coronary computed tomography angiography.Methods and resultsWe included patients suspected of acute coronary syndrome who underwent coronary computed tomography angiography in the emergency department. Based on the HEART score, patients were stratified as low-risk (HEART≤3), intermediate-risk (HEART4-6) and high-risk (HEART≥7). We assessed coronary computed tomography angiography for the presence of significant coronary artery disease (>50% stenosis). The primary outcome, the level of major adverse cardiac events, was a composite endpoint of all-cause mortality, acute coronary syndrome or coronary revascularisation within 30 days. The study population consisted of 340 patients (mean age: 55.6±10.1 years, 44.7% women), major adverse cardiac events occurred in 45 (13.2%) patients. The incidence of major adverse cardiac events in patients stratified as low-risk (35.0%), intermediate-risk (56.8%) and high-risk (8.2%) was 3.4%, 12.4% and 60.7%, respectively. All four low-risk patients with major adverse cardiac events had a HEART score of three. An algorithm where coronary computed tomography angiography is reserved for patients with HEART 3-6 resulted in a sensitivity of 97.8%, specificity of 84.1%, negative predictive value of 99.6% and positive predictive value of 48.4%, while reducing the need for coronary computed tomography angiography by 22% (n=75).ConclusionThe predictive value of coronary computed tomography angiography for 30-day major adverse cardiac events in suspected acute coronary syndrome patients is good, and reserving coronary computed tomography angiography for HEART score 3-6 patients reduces the number of needed coronary computed tomography angiograms without affecting diagnostic accuracy.

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Who explicitly requests the ordering of computed tomography for emergency department patients? A multicenter prospective study.

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Who explicitly requests the ordering of computed tomography for emergency department patients? A multicenter prospective study.

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