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Patient understanding of radiation risk from medical computed tomography-A comparison of Hispanic vs. non-Hispanic emergency department populations.

ABSTRACT: Background. Cultural differences and language barriers may adversely impact patients with respect to understanding the risks/benefits of medical testing. Objective. We hypothesized that there would be no difference in Hispanic vs. non-Hispanic patients' knowledge of radiation risk that results from CT of the abdomen/pelvis (CTAP). Methods. We enrolled a convenience sample of adults at an inner-city emergency department (ED). Patients provided written answers to rate agreement on a 10-point scale for two correct statements comparing radiation exposure equality between: CTAP and 5 years of background radiation (question 1); CTAP and 200 chest x-rays (question 3). Patients also rated their agreement that multiple CT scans increase the lifetime cancer risk (question 2). Scores of >8 were considered good knowledge. Multivariate logistic regression analyses were performed to estimate the independent effect of the Hispanic variable. Results. 600 patients in the study group; 63% Hispanic, mean age 39.2 ± 13.9 years. Hispanics and non-Hispanics whites were similar with respect to good knowledge-level answers to question 1 (17.3 vs. 15.1%; OR = 1.2; 95% CI [0.74-2.0]), question 2 (31.2 vs. 39.3%; OR = 0.76; 95% CI [0.54-1.1]), and question 3 (15.2 vs. 16.5%; OR = 1.1; 95% CI [0.66-1.8]). Compared to patients who earned <20,000, patients with income >40,000 were more likely to answer question 2 with good knowledge (OR = 1.96; 95% CI [1.2-3.1]). Conclusion. The study group's overall knowledge of radiation risk was poor, but we did not find significant differences between Hispanic vs. non-Hispanic patients.

SUBMITTER: McNierney-Moore A

PROVIDER: S-EPMC4435503 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Patient understanding of radiation risk from medical computed tomography-A comparison of Hispanic vs. non-Hispanic emergency department populations.

McNierney-Moore Afton A Smith Cynthia C Guardiola Jose J Xu K Tom KT Richman Peter B PB

PeerJ 20150507

Background. Cultural differences and language barriers may adversely impact patients with respect to understanding the risks/benefits of medical testing. Objective. We hypothesized that there would be no difference in Hispanic vs. non-Hispanic patients' knowledge of radiation risk that results from CT of the abdomen/pelvis (CTAP). Methods. We enrolled a convenience sample of adults at an inner-city emergency department (ED). Patients provided written answers to rate agreement on a 10-point scale ...[more]

PMID: 26019999

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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:BACKGROUND: Severely injured trauma patients are exposed to clinically significant radiation doses from computed tomography (CT) imaging in the emergency department. Moreover, this radiation exposure is associated with an increased risk of cancer. The purpose of this study was to determine some effects of a radiation dose reduction strategy for CT in severely injured trauma patients in the emergency department. METHODS: We implemented the radiation dose reduction strategy in May 2009. A prospective observational study design was used to collect data from patients who met the inclusion criteria during this one year study (intervention group) from May 2009 to April 2010. The prospective data were compared with data collected retrospectively for one year prior to the implementation of the radiation dose reduction strategy (control group). By comparison of the cumulative effective dose and the number of CT examinations in the two groups, we evaluated effects of a radiation dose reduction strategy. All the patients met the institutional adult trauma team activation criteria. The radiation doses calculated by the CT scanner were converted to effective doses by multiplication by a conversion coefficient. RESULTS: A total of 118 patients were included in this study. Among them, 33 were admitted before May 2009 (control group), and 85 were admitted after May 2009 (intervention group). There were no significant differences between the two groups regarding baseline characteristics, such as injury severity and mortality. Additionally, there was no difference between the two groups in the mean number of total CT examinations per patient (4.8 vs. 4.5, respectively; p = 0.227). However, the mean effective dose of the total CT examinations per patient significantly decreased from 78.71 mSv to 29.50 mSv (p < 0.001). CONCLUSIONS: The radiation dose reduction strategy for CT in severely injured trauma patients effectively decreased the cumulative effective dose of the total CT examinations in the emergency department. But not effectively decreased the number of CT examinations.

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Dataset Information

Patient understanding of radiation risk from medical computed tomography-A comparison of Hispanic vs. non-Hispanic emergency department populations.

Publications

Patient understanding of radiation risk from medical computed tomography-A comparison of Hispanic vs. non-Hispanic emergency department populations.

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