Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining.
Objectives. We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities. Methods. We obtained fiscal year 2007 records for Veterans Health...
| Published in: | American Journal of Public Health Vol. 105; no. 6; pp. 1168 - 1174 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
American Public Health Association
Jun2015
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=109803706&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 109803706 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00900036 APH jtl: American Journal of Public Health issn: 00900036 maglogo: N pubinfo: dt: Jun2015 vid: 105 iid: 6 pid: 44 pub: American Public Health Association artinfo: ui: 109803706 10.2105/AJPH.2014.302440 ppf: 1168 ppct: 6 formats: tig: atl: Improving Identification of Fall-Related Injuries in Ambulatory Care Using Statistical Text Mining. aug: au: Luther, Stephen L. McCart, James A. Berndt, Donald J. Hahm, Bridget Finch, Dezon Jarman, Jay Foulis, Philip R. Lapcevic, William A. Campbell, Robert R. Shorr, Ronald I. Valencia, Keryl Motta Powell-Cope, Gail su: Clinics Confidence intervals Accidental falls Research funding Data mining Latent semantic analysis Data analysis software Electronic health records Statistical models Descriptive statistics Odds ratio sug: subj: All other out-patient care centres All Other Outpatient Care Centers Community health centres Offices of physicians Clinics Confidence intervals Accidental falls Research funding Data mining Latent semantic analysis Data analysis software Electronic health records Statistical models Descriptive statistics Odds ratio ab: Objectives. We determined whether statistical text mining (STM) can identify fall-related injuries in electronic health record (EHR) documents and the impact on STM models of training on documents from a single or multiple facilities. Methods. We obtained fiscal year 2007 records for Veterans Health Administration (VHA) ambulatory care clinics in the southeastern United States and Puerto Rico, resulting in a total of 26 010 documents for 1652 veterans treated for fall-related injury and 1341 matched controls. We used the results of an STM model to predict fall-related injuries at the visit and patient levels and compared them with a reference standard based on chart review. Results. STM models based on training data from a single facility resulted in accuracy of 87.5% and 87.1%, F-measure of 87.0% and 90.9%, sensitivity of 92.1% and 94.1%, and specificity of 83.6% and 77.8% at the visit and patient levels, respectively. Results from training data from multiple facilities were almost identical. Conclusions. STM has the potential to improve identification of fall-related injuries in the VHA, providing a model for wider application in the evolving national EHR system. pubtype: Academic Journal doctype: Journal Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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