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Cited by 16Electronic Health Records SystemsMachine Learning in HealthcareMedical Coding and Health Information

Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications

Open access
Sunyang Fu, Wen, Andrew, Schaeferle, Gavin M, Wilson, Patrick, Demuth, Gabriel O., Xiaoyang Ruan, Sijia Liu, Storlie, Curtis B. +1 more·2022·Unpublished
DOI 10.13140/rg.2.2.29516.54400PMC PMC9285181
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Abstract

Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems.

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  • Anemia is named differently across EHR systems; one might call it post-hemorrhagic anemia, while another refers to it as blood loss anemia.

    “For example, anemia is named differently across systems; one might call it “post-hemorrhagic anemia”, while another refers to it as “blood loss anemia”.”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [170]

    The Results (page unavailable) report that anemia expressions vary between Epic and Centricity. But the paper gives “Anemia Posthemorrhagic Acute (Blood Loss Anemia)” as one expression; it does not say one system uses “post-hemorrhagic anemia” while the other uses “blood loss anemia.” (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Such inconsistencies result in patient record discrepancies and complicate clinician interpretations.

    “Such inconsistencies result in patient record discrepancies and complicate clinician interpretations [170].”

    From Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors (2026) · cites this paper as [170]

    The Abstract and Discussion report differences in data completeness, documentation, and syntactic and semantic content across two EHR systems, supporting the point about inconsistent patient-record data. They do not report that these differences complicated clinicians’ interpretations; the stated consequences concern predictive models and data quality. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Fu, S., Wen, A., Schaeferle, G. M., Wilson, P., Demuth, G. O., Ruan, X., Liu, S., Storlie, C. B., & Liu, H.
Affiliations
Mayo Clinic (US)
ORCID
Venue
Unpublished
Publisher
Unpublished
Published
2022
Volume
2022
Pages
196-205
DOI
10.13140/rg.2.2.29516.54400
PMC
PMC9285181
OpenAlex
W4286377178
Cited by
16
References
4
Citations by year
2025: 5 · 2024: 3 · 2023: 5 · 2022: 2
FWCI
2.60
Type
journal article
Language
en
Open access
Locations
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (U01TR002062)
  • National Institute of Biomedical Imaging and Bioengineering (R01EB019403)
Topics
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
  • Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
Keywords
data quality, postoperative complications, predictive modeling, structured and unstructured data, data completeness, data timeliness, semantic variation, syntactic variation, model implementation

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