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Abstract:
The integrity of health information in Electronic Health Record (EHR) is critical to delivering evidence-based care and improving population health through research. EHR errors are observed on fundamental data elements such as height/weight, causing both biologically implausible and patient-level conflicting data. We used a nonparametric regression with a Gaussian moving window to systematically identify outliers on population and individual level from IBM Explorys Therapeutic Dataset. This framework proves effective in improving data quality of EHR.

Learning Objective 1: Discuss the state-of-art informatics approaches to improving data quality in Electronic Health Record

Authors:

Wei Yao (Presenter)
IBM Watson Health

John Borsi, IBM Watson Health
Yifan Xu, IBM Watson Health
Nnenna Ibeanusi, IBM Watson Health

Presentation Materials:

Keywords