Automated Detection of Radiology Reports that Document Non-routine Communication of Critical or Significant Results.
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...
| Publicado en: | Journal of Digital Imaging Vol. 23; no. 6; pp. 647 - 658 |
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| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2010
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105017032&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105017032 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Dec2010 vid: 23 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 105017032 55128925 10.1007/s10278-009-9237-1 NLM19826871 PMC2978900 105017032 ppf: 647 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Automated Detection of Radiology Reports that Document Non-routine Communication of Critical or Significant Results. aug: au: Lakhani P Langlotz C affil: Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, USA; e-mail: paras.lakhani2@uphs.upenn.edu sug: subj: Reports Information Retrieval Algorithms Automation Human Reference Values Validation Studies Confidence Intervals Data Mining Natural Language Processing Quality Control (Technology) Quality Assurance Joint Commission Sample Size ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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