Comparison Between Manual Auditing and a Natural Language Process With Machine Learning Algorithm to Evaluate Faculty Use of Standardized Reports in Radiology.
Purpose: When implementing or monitoring department-sanctioned standardized radiology reports, feedback about individual faculty performance has been shown to be a useful driver of faculty compliance. Most commonly, these data are derived from manual audit, which can be both time-consuming and subje...
| Publicado en: | Journal of the American College of Radiology Vol. 15; no. 3; pp. 550 - 554 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2018 Part B
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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=128165463&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128165463 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15461440 EAO jtl: Journal of the American College of Radiology issn: 15461440 maglogo: N pubinfo: dt: Mar2018 Part B vid: 15 iid: 3 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 128165463 128165463 NLM29269244 128165463 10.1016/j.jacr.2017.10.042 NLM29269244 128165463 ppf: 550 ppct: 4 formats: tig: atl: Comparison Between Manual Auditing and a Natural Language Process With Machine Learning Algorithm to Evaluate Faculty Use of Standardized Reports in Radiology. aug: au: Guimaraes, Carolina V. Grzeszczuk, Robert IIIBisset, George S. Donnelly, Lane F. Bisset, George S 3rd affil: Department of Radiology, Texas Children’s Hospital, Houston, Texas sug: subj: Natural Language Processing Audit Documentation Standards Software Faculty, Medical Radiology Information Systems Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies ab: Purpose: When implementing or monitoring department-sanctioned standardized radiology reports, feedback about individual faculty performance has been shown to be a useful driver of faculty compliance. Most commonly, these data are derived from manual audit, which can be both time-consuming and subject to sampling error. The purpose of this study was to evaluate whether a software program using natural language processing and machine learning could accurately audit radiologist compliance with the use of standardized reports compared with performed manual audits.Methods: Radiology reports from a 1-month period were loaded into such a software program, and faculty compliance with use of standardized reports was calculated. For that same period, manual audits were performed (25 reports audited for each of 42 faculty members). The mean compliance rates calculated by automated auditing were then compared with the confidence interval of the mean rate by manual audit.Results: The mean compliance rate for use of standardized reports as determined by manual audit was 91.2% with a confidence interval between 89.3% and 92.8%. The mean compliance rate calculated by automated auditing was 92.0%, within that confidence interval.Conclusion: This study shows that by use of natural language processing and machine learning algorithms, an automated analysis can accurately define whether reports are compliant with use of standardized report templates and language, compared with manual audits. This may avoid significant labor costs related to conducting the manual auditing process. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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