EHRs Processes Need Refined Phenotyping Application
By Katie Wike, contributing writer
Data collected in EHRs needs to be corrected and refined if it is to be any use to providers and researchers
A study published in the Journal of the American Informatics Association says while the data provided by electronic health records (EHRs) is invaluable to determining candidates for clinical trials and research, the methods for obtaining this information need refinement.
“One of the major challenges to reusing EHR data comes from the inaccuracy, incompleteness, complexity, and resulting bias inherent in the recording of the healthcare process. Therefore, EHR data cannot be treated simply as research data with noise and missing values; instead, the EHR carries systematic biases that must be addressed before the data can reach their potential,” say researchers.
Patient phenotype information is often hidden within EHR data, and uncovering it is made more difficult because of abbreviations, misspellings, and the use of local dialects in clinical notes. According to FierceEMR, the study’s authors “reviewed 97 different articles on patient cohort identification using EHRs, and examined the different approaches used for phenotyping, including natural language processing, statistical analyses, rules-based algorithms and hybrid approaches. They found that the current approaches to phenotyping were ‘often inadequate’ to the task.”
The authors of the study write, “We believe that the path forward involves systemizing the phenotyping process with the hope of future automation or partial automation. We hope to understand better how the healthcare process affects the recording of clinical information in the EHR so that we can improve and perhaps speed the generation of phenotypes.
“In summary, correlating EHR variables with healthcare process events produced sensible grouping of variables, but appeared to be highly sensitive to the manner in which the variables were collected. We believe that it may be possible to exploit this sensitivity to improve the phenotyping process, and that the approach may point the way in the longer run to a more automated and reliable phenotyping process.”
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