Significant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical Documentation

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dc.contributor.authorRahimian, Maryam
dc.contributor.authorWarner, Jeremy L.
dc.contributor.authorJain, Sandeep K.
dc.contributor.authorDavis, Roger B.
dc.contributor.authorZerillo, Jessica A.
dc.contributor.authorJoyce, Robin M.
dc.date.accessioned2020-04-06T21:39:56Z
dc.date.available2020-04-06T21:39:56Z
dc.date.issued2019-06-11
dc.description.abstractPURPOSE OpenNotes is a national movement established in 2010 that gives patients access to their visit notes through online patient portals, and its goal is to improve transparency and communication. To determine whether granting patients access to their medical notes will have a measurable effect on provider behavior, we developed novel methods to quantify changes in the length and frequency of use of n-grams (sets of words used in exact sequence) in the notes. METHODS We analyzed 102,135 notes of 36 hematology/oncology clinicians before and after the OpenNotes debut at Beth Israel Deaconess Medical Center. We applied methods to quantify changes in the length and frequency of use of sequential co-occurrence of words (n-grams) in the unstructured content of the notes by unsupervised hierarchical clustering and proportional analysis of n-grams. RESULTS The number of significant n-grams averaged over all providers did not change, but for individual providers, there were significant changes. That is, all significant observed changes were provider specific. We identified eight providers who were late note signers. This group significantly reduced its late signing behavior after OpenNotes implementation. CONCLUSION Although the number of significant n-grams averaged over all providers did not change, our text-mining method detected major content changes in specific providers' documentation at the n-gram level. The method successfully identified a group of providers who decreased their late note signing behavior. (C) 2019 by American Society of Clinical Oncologyen_US
dc.description.departmentDepartment of Biomedical Informatics
dc.description.schoolSchool of Medicine
dc.identifier.citationRahimian M, Warner JL, Jain SK, Davis RB, Zerillo JA, Joyce RM. Significant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical Documentation. JCO Clin Cancer Inform. 2019;3:1–9. doi:10.1200/CCI.19.00012en_US
dc.identifier.doi10.1200/CCI.19.00012 JCO Clinical
dc.identifier.issn2473-4276
dc.identifier.urihttps://ir.vanderbilt.edu/xmlui/handle/1803/9894
dc.language.isoen_USen_US
dc.publisherJCO Clinical Cancer Informaticsen_US
dc.relation.ispartofseriesDBMI Technical Reports;#
dc.rightsLicensed under the Creative Commons Attribution 4.0 License
dc.source.urihttps://ascopubs.org/doi/10.1200/CCI.19.00012
dc.subjectMedical Recordsen_US
dc.subjectDoctorsen_US
dc.subjectAccessen_US
dc.titleSignificant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical Documentationen_US
dc.typeArticleen_US
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