The Introduction and Development of the H-index for Imaging Utilizers: A Novel Metric for Quantifying Utilization of Emergency Department Imaging

Disproportionate Use in Minor Trauma Is Driving Emergency Department Cervical Spine Imaging: An Injury Severity Score-Based Analysis

PURPOSE

Clinical practice guidelines intended to reduce unnecessary cervical spine imaging have yielded mixed results. We aimed to assess evolving emergency department (ED) cervical spine imaging utilization in patients with trauma by injury severity.

METHODS

Using 2009 to 2018 IBM MarketScan Commercial Databases, we identified ED trauma encounters, associated cervical spine imaging, and related diagnosis codes. We classified encounters by injury severity (minor, intermediate, major) using an International Classification of Disease code-derived Injury Severity Score algorithm and studied evolving imaging utilization using multivariable Poisson regression models.

RESULTS

Of all 11,346,684 ED visits for trauma, 7,753,914 (68.3%), 3,524,250 (31.1%), and 68,520 (0.6%) involved minor, intermediate, and major injuries, respectively. Overall cervical spine imaging increased 5.7% annually (incidence rate ratio [IRR] 1.057, P < .001) with radiography decreasing 2.7% annually (IRR 0.973, P < .001) and CT increasing 10.5% annually (IRR 1.105, P < .001). Radiography utilization remained unchanged for minor injuries (IRR 0.994, P = .14) but decreased for intermediate (IRR 0.928 versus minor, P < .001) and major (IRR 0.931 versus minor, P < .001) injuries. Increases in CT utilization were greatest for minor injuries (IRR 1.109, P < .001) with smaller increases in intermediate (IRR 0.960 versus minor, P < .001) and major (IRR 0.987 versus minor, P = .022) injuries.

CONCLUSIONS

Recent increases in cervical spine imaging in commercially insured patients with trauma seen in the ED have been largely related to increases in CT for patients with only minor injuries, in whom imaging utilization has been historically low. Further study is necessary to assess appropriateness, implications on costs and population radiation dose, and factors influencing ordering decision making.

A novel corpus-based computing method for handling critical word-ranking issues: An example of COVID-19 research articles

A corpus is a massive body of structured textual data that are stored and operated electronically. It usually combines with statistics, machine learning algorithms, or artificial intelligence (AI) technologies to explore the semantic relationship between lexical units, and beneficial when applied to language learning, information processing, translation, and so forth. In the face of a novel disease, like, COVID-19, establishing medical-specific corpus will enhance frontline medical personnel's information acquisition efficiency, guiding them on the right approaches to respond to and prevent the novel disease. To effectively retrieve critical messages from the corpus, appropriately handling word-ranking issues is quite crucial. However, traditional frequency-based approaches may cause bias in handling word-ranking issues because they neither optimize the corpus nor integrally take words' frequency dispersion and concentration criteria into consideration. Thus, this paper develops a novel corpus-based approach that combines a corpus software and Hirsch index (H-index) algorithm to handle the aforementioned issues simultaneously, making word-ranking processes more accurate. This paper compiled 100 COVID-19-related research articles as an empirical example of the target corpus. To verify the proposed approach, this study compared the results of two traditional frequency-based approaches and the proposed approach. The results indicate that the proposed approach can refine corpus and simultaneously compute words' frequency dispersion and concentration criteria in handling word-ranking issues.