Development and Validation of Queries Using Structured Query Language (SQL) to Determine the Utilization of Comparison Imaging in Radiology Reports Stored on PACS

Lakhani, Paras; Menschik, Elliot D.; Goldszal, Alberto; Murray, Joseph P.; Weiner, Mark G.; Langlotz, Curtis P.

doi:10.1007/s10278-005-7667-y

Cited by 9 publications

(10 citation statements)

References 20 publications

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“…This category covers applications that identify quality indicators of radiologic practice (59)(60)(61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73). These indicators can be used for internal quality assurance, comparison to established guidelines, or fulfilling legal requirements.…”

Section: Quality Assessment Of Radiologic Practicementioning

confidence: 99%

“…A subgroup of studies in this category exploited NLP to assess quality of content and format of the radiology report itself (64)(65)(66)(67)(68). Good radiologic practice is subject to conventions for standardized reporting.…”

Section: Clinical Support Servicesmentioning

confidence: 99%

“…As an example of report quality assessment, Lakhani et al developed a system to automatically identify whether review of comparison images was properly documented (64). Guidelines of the American College of Radiology advise to clearly document the use of comparison imaging, which is considered to improve diagnostic accuracy.…”

Section: Clinical Support Servicesmentioning

confidence: 99%

See 2 more Smart Citations

Natural Language Processing in Radiology: A Systematic Review

Pons¹,

Braun²,

Hunink³

et al. 2016

Radiology

450

309

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Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input. Used on radiology reports, NLP techniques enable automatic identification and extraction of information. By exploring the various purposes for their use, this review examines how radiology benefits from NLP. A systematic literature search identified 67 relevant publications describing NLP methods that support practical applications in radiology. This review takes a close look at the individual studies in terms of tasks (ie, the extracted information), the NLP methodology and tools used, and their application purpose and performance results. Additionally, limitations, future challenges, and requirements for advancing NLP in radiology will be discussed.

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Section: Quality Assessment Of Radiologic Practicementioning

confidence: 99%

Section: Clinical Support Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Natural Language Processing in Radiology: A Systematic Review

Pons¹,

Braun²,

Hunink³

et al. 2016

Radiology

450

309

View full text Add to dashboard Cite

show abstract

“…Our work complements prior work that has shown that NLP algorithms can be used to ascertain aspirin exposure from free-text progress notes,4,17 but may have the advantage of being more easily implemented because many health systems may have programmers familiar with SQL, whereas NLP requires more advanced training. Our work also expands prior work implementing SQL-based search of free-text notes for exposure and outcome ascertainment 26,27. We utilize not only inherent features to the SQL language, but supplement our methods with structured data to create a stronger algorithm.…”

Section: Discussionmentioning

confidence: 98%

Ascertainment of Aspirin Exposure Using Structured and Unstructured Large-scale Electronic Health Record Data

et al. 2019

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“…However, we were previously successful in mining data directly from free-text radiology reports using simple rule-based queries based on standard languages 12. Other authors have similarly used regular expressions for improving information retrieval 13.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Radiology Reports that Document Non-routine Communication of Critical or Significant Results

Lakhani

Langlotz

2009

J Digit Imaging

Self Cite

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The purpose of this investigation is to develop an automated method to accurately detect radiology reports that indicate non-routine communication of critical or significant results. Such a classification system would be valuable for performance monitoring and accreditation. Using a database of 2.3 million free-text radiology reports, a rule-based query algorithm was developed after analyzing hundreds of radiology reports that indicated communication of critical or significant results to a healthcare provider. This algorithm consisted of words and phrases used by radiologists to indicate such communications combined with specific handcrafted rules. This algorithm was iteratively refined and retested on hundreds of reports until the precision and recall did not significantly change between iterations. The algorithm was then validated on the entire database of 2.3 million reports, excluding those reports used during the testing and refinement process. Human review was used as the reference standard. The accuracy of this algorithm was determined using precision, recall, and F measure. Confidence intervals were calculated using the adjusted Wald method. The developed algorithm for detecting critical result communication has a precision of 97.0% (95% CI, 93.5–98.8%), recall 98.2% (95% CI, 93.4–100%), and F measure of 97.6% (ß = 1). Our query algorithm is accurate for identifying radiology reports that contain non-routine communication of critical or significant results. This algorithm can be applied to a radiology reports database for quality control purposes and help satisfy accreditation requirements.

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Development and Validation of Queries Using Structured Query Language (SQL) to Determine the Utilization of Comparison Imaging in Radiology Reports Stored on PACS

Cited by 9 publications

References 20 publications

Natural Language Processing in Radiology: A Systematic Review

Natural Language Processing in Radiology: A Systematic Review

Ascertainment of Aspirin Exposure Using Structured and Unstructured Large-scale Electronic Health Record Data

Automated Detection of Radiology Reports that Document Non-routine Communication of Critical or Significant Results

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