Work

Sick patients have more data: the non-random completeness of electronic health records.

Year: 2013

Type: article

Abstract: As interest in the reuse of electronic health record (EHR) data for research purposes grows, so too does awareness of the significant data quality problems in these non-traditional datasets. In the past, however, little attention has been paid to whether poor data quality merely introduces noise into EHR-derived datasets, or if there is potential for the creation of spurious signals and bias. In this study we use EHR data to demonstrate a statistically significant relationship between EHR completeness and patient health status, indicating that records with more data are likely to be more representative of sick patients than healthy ones, and therefore may not reflect the broader population found within the EHR.

Source: Europe PMC (PubMed Central)

Authors: Nicole G Weiskopf, Alex Rusanov, Chunhua Weng

Institution: Columbia University

Language: English

Topic: Electronic Health Records Systems

Subfield: Health Information Management

Field: Health Professions

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, U.S. National Library of Medicine

Awards: Anesthesiology Research Training, Developing Flexible EHR Plug-ins to Re-Engineer Clinical Care and Research Workflows, Bridging the Semantic Gap Between Research Eligibility Criteria and Clinical Data, Clinical and Translational Science Award, Training in Biomedical Informatics at Columbia University