Natural Language Processing Using Online Analytic Processing for Assessing Recommendations in Radiology Reports

Dang, Pragya A.; Kalra, Mannudeep K.; Blake, Michael A.; Schultz, Thomas; Stout, Markus; Lemay, Paul R.; Freshman, David J.; Halpern, Elkan F.; Dreyer, Keith J.

doi:10.1016/j.jacr.2007.09.003

Cited by 27 publications

(20 citation statements)

References 9 publications

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“…The overall recommendation rate is comparable to rates previously reported for thoracic radiology and general radiography, which ranged from 7.7% to 10.9%, with notable differences in study design (1,3,7). We are unaware of another study that specifically reported chest CT recommendation rates from outpatient chest radiographic imaging.…”

Section: Discussionsupporting

confidence: 79%

“…Recommendation rates for CT imaging among the 11 thoracic radiologists varied from 2.5% to 8.7%, which was comparable to previously published data (3). Although previous studies showed a decreased likelihood of RAI with increased radiologist experience (1), the number of radiologists involved in our study was insufficient for a meaningful comparison of radiologist experience and RAIs.…”

Section: Discussionsupporting

confidence: 76%

“…Critics implicated RAIs as a cause for increase in use of imaging and the associated costs, although the data to support this are mixed (2)(3)(4). Recommendation rates and costs in isolation are insufficient indicators of clinical value of RAIs.…”

Section: Thoracic Imaging: Recommendations For Chest Ct Prompted By Omentioning

confidence: 99%

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Diagnostic Yield of Recommendations for Chest CT Examination Prompted by Outpatient Chest Radiographic Findings

Harvey

Gilman

et al. 2015

Radiology

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Purpose:To evaluate the diagnostic yield of recommended chest computed tomography (CT) prompted by abnormalities detected on outpatient chest radiographic images. Materials andMethods:This HIPAA-compliant study had institutional review board approval; informed consent was waived. Reports of all outpatient chest radiographic examinations performed at a large academic center during 2008 (n = 29 138) were queried to identify studies that included a recommendation for a chest CT imaging. The radiology information system was queried for these patients to determine if a chest CT examination was obtained within 1 year of the index radiographic examination that contained the recommendation. For chest CT examinations obtained within 1 year of the index chest radiographic examination and that met inclusion criteria, chest CT images were reviewed to determine if there was an abnormality that corresponded to the chest radiographic finding that prompted the recommendation. All corresponding abnormalities were categorized as clinically relevant or not clinically relevant, based on whether further work-up or treatment was warranted. Groups were compared by using t test and Fisher exact test with a Bonferroni correction applied for multiple comparisons. Results:There were 4.5% (1316 of 29138 [95% confidence interval {CI}: 4.3%, 4.8%]) of outpatient chest radiographic examinations that contained a recommendation for chest CT examination, and increasing patient age (P , .001) and positive smoking history (P = .001) were associated with increased likelihood of a recommendation for chest CT examination. Of patients within this subset who met inclusion criteria, 65.4% (691 of 1057 [95% CI: 62.4%, 68.2%) underwent a chest CT examination within the year after the index chest radiographic examination. Conclusion:A radiologist recommendation for chest CT to evaluate an abnormal finding on an outpatient chest radiographic examination has a high yield of clinically relevant findings.q RSNA, 2014

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Section: Discussionsupporting

confidence: 79%

Section: Discussionsupporting

confidence: 76%

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Diagnostic Yield of Recommendations for Chest CT Examination Prompted by Outpatient Chest Radiographic Findings

Harvey

Gilman

et al. 2015

Radiology

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“…Natural language processing methods can be used to find quality indicators of radiological practice (23), automatically analyze content (24), classify free-text reports (25) or assess the presence of sufficient clinical recommendations in radiological reports (26). Such methods successfully use more complex language models and retrieve contextual information, but they need to be tailored for specific conditions and require dedicated sets of examples for training and optimization (23).…”

Section: (Css) Distributions Of Conventional Free Text Reports (Cftr)mentioning

confidence: 99%

Objective Comparison Using Guideline-based Query of Conventional Radiological Reports and Structured Reports

Maros

Wenz

Förster

et al. 2018

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Abstract. Background: This feasibility study of text-miningbased scoring algorithm provides an objective comparison of structured reports (SR) and conventional free-text reports (cFTR) by means of guidelineStructured reporting (SR) represents a new direction in communicating radiological reports to clinicians in a way that is clear and uniform (1). However, follow-up studies on SR have shown mixed results regarding adaptation and adherence (2-4). In particular, neuroradiological residents most commonly complain that SRs are overly constraining and time-consuming (5). In spite of that, there is an increasing tendency to use online solutions to generate SR templates (6). For instance, "CT brain" and "MR brain" were the third and fifth most frequently viewed SR templates in the Radiological Society of North America (RSNA) online library (7). SR templates can serve as core frameworks, but because of heterogeneous representations of diseases and co-morbidities, there is a substantial need to allow for customization of each report -primarily in the form of additional free text. Consequently, the distinction between conventional free-text (narrative) reports (cFTR) and SR becomes even more blurred, which makes their objective comparison even harder. This raises two further questions: Are SRs indeed 'better' than cFTRs? How can cFTRs be compared with SRs in an objective, fast and scalable way if SRs are often a mixture of structured and free (narrative) text?Firstly, there is a need to define how to objectively quantify 'better'. Commonly the method of choice for such quality assessments is either an expert opinion-based rating (5, 8) or a survey-based evaluation by physicians (4). Although both methods provide valuable insight, they are very laborious and time-consuming for experts and do not scale well for large datasets (4,8). Therefore, we aimed to implement an approach using the adherence to imaging and clinical guidelines as a quality measure.Secondly, in order to objectively compare cFTR with SR, we suggest an evaluation based on widely used text-mining technique using the 'bag of words' representation and cosine similarity (9, 10). The cosine similarity provides the core of many information retrieval systems (11,12). Reports can be 843 This article is freely accessible online.

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“…In this manner, reports can be standardized by (for example) replacing each word with its stem. The aggregate of stems in a report can then be more readily searched for the stems related to the concept of interest (9)(10)(11).…”

Section: Figurementioning

confidence: 99%

Natural Language Processing Technologies in Radiology Research and Clinical Applications

et al. 2016

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The migration of imaging reports to electronic medical record systems holds great potential in terms of advancing radiology research and practice by leveraging the large volume of data continuously being updated, integrated, and shared. However, there are significant challenges as well, largely due to the heterogeneity of how these data are formatted. Indeed, although there is movement toward structured reporting in radiology (ie, hierarchically itemized reporting with use of standardized terminology), the majority of radiology reports remain unstructured and use free-form language. To effectively "mine" these large datasets for hypothesis testing, a robust strategy for extracting the necessary information is needed. Manual extraction of information is a time-consuming and often unmanageable task. "Intelligent" search engines that instead rely on natural language processing (NLP), a computer-based approach to analyzing free-form text or speech, can be used to automate this data mining task. The overall goal of NLP is to translate natural human language into a structured format (ie, a fixed collection of elements), each with a standardized set of choices for its value, that is easily manipulated by computer programs to (among other things) order into subcategories or query for the presence or absence of a finding. The authors review the fundamentals of NLP and describe various techniques that constitute NLP in radiology, along with some key applications. After completing this journal-based SA-CME activity, participants will be able to:■ Describe the set of technologies that compose present-day natural language processing in radiology.■ List examples of how these technologies have been combined to achieve specific objectives in radiology research and, potentially, clinical practice.■ Discuss current capabilities and possible future applications of use of natural language processing in radiology.

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Natural Language Processing Using Online Analytic Processing for Assessing Recommendations in Radiology Reports

Cited by 27 publications

References 9 publications

Diagnostic Yield of Recommendations for Chest CT Examination Prompted by Outpatient Chest Radiographic Findings

Diagnostic Yield of Recommendations for Chest CT Examination Prompted by Outpatient Chest Radiographic Findings

Objective Comparison Using Guideline-based Query of Conventional Radiological Reports and Structured Reports

Natural Language Processing Technologies in Radiology Research and Clinical Applications

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