Department of Biomedical Informaticshttp://hdl.handle.net/1803/642024-03-28T11:23:09Z2024-03-28T11:23:09ZStructured override reasons for drug-drug interaction alerts in electronic health recordsMcCoy, Allison B.http://hdl.handle.net/1803/162372020-10-22T14:02:15Z2019-10-01T00:00:00ZStructured override reasons for drug-drug interaction alerts in electronic health records
McCoy, Allison B.
Objective: The study sought to determine availability and use of structured override reasons for drug-drug interaction (DDI) alerts in electronic health records.
Materials and Methods: We collected data on DDI alerts and override reasons from 10 clinical sites across the United States using a variety of electronic health records. We used a multistage iterative card sort method to categorize the override reasons from all sites and identified best practices.
Results: Our methodology established 177 unique override reasons across the 10 sites. The number of coded override reasons at each site ranged from 3 to 100. Many sites offered override reasons not relevant to DDIs. Twelve categories of override reasons were identified. Three categories accounted for 78% of all overrides: "will monitor or take precautions," "not clinically significant," and "benefit outweighs risk."
Discussion: We found wide variability in override reasons between sites and many opportunities to improve alerts. Some override reasons were irrelevant to DDIs. Many override reasons attested to a future action (eg, decreasing a dose or ordering monitoring tests), which requires an additional step after the alert is overridden, unless the alert is made actionable. Some override reasons deferred to another party, although override reasons often are not visible to other users. Many override reasons stated that the alert was inaccurate, suggesting that specificity of alerts could be improved.
Conclusions: Organizations should improve the options available to providers who choose to override DDI alerts. DDI alerting systems should be actionable and alerts should be tailored to the patient and drug pairs.
Only Vanderbilt University affiliated authors are listed on VUIR. For a full list of authors, access the version of record at https://academic.oup.com/jamia/article/26/10/934/5480565
2019-10-01T00:00:00ZMapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial EvaluationWu, PatrickGifford, AliyaMeng, XiangruiLi, XueCampbell, HarryVarley, TimZhao, JaunCarroll, RobertBastarache, LisaDenny, Joshua C.Theodoratou, EvropiWei, Wei-Qihttp://hdl.handle.net/1803/161462020-09-24T02:22:53Z2019-01-01T00:00:00ZMapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation
Wu, Patrick; Gifford, Aliya; Meng, Xiangrui; Li, Xue; Campbell, Harry; Varley, Tim; Zhao, Jaun; Carroll, Robert; Bastarache, Lisa; Denny, Joshua C.; Theodoratou, Evropi; Wei, Wei-Qi
Background: The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR).
Objective: The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes.
Methods: We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS.
Results: We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]).
Conclusions: This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.
2019-01-01T00:00:00ZConceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment WorkflowJain, Neha M.Culley, AlisonKnoop, TeresaMicheel, ChristineOsterman, TravisLevy, Miahttp://hdl.handle.net/1803/99992020-05-20T06:14:54Z2019-06-21T00:00:00ZConceptual Framework to Support Clinical Trial Optimization and End-to-End Enrollment Workflow
Jain, Neha M.; Culley, Alison; Knoop, Teresa; Micheel, Christine; Osterman, Travis; Levy, Mia
In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement. (C) 2019 by American Society of Clinical Oncology
2019-06-21T00:00:00ZSignificant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical DocumentationRahimian, MaryamWarner, Jeremy L.Jain, Sandeep K.Davis, Roger B.Zerillo, Jessica A.Joyce, Robin M.http://hdl.handle.net/1803/98942020-04-22T06:00:28Z2019-06-11T00:00:00ZSignificant and Distinctive n-Grams in Oncology Notes: A Text-Mining Method to Analyze the Effect of OpenNotes on Clinical Documentation
Rahimian, Maryam; Warner, Jeremy L.; Jain, Sandeep K.; Davis, Roger B.; Zerillo, Jessica A.; Joyce, Robin M.
PURPOSE 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 Oncology
2019-06-11T00:00:00Z