Quality assessment of functional status documentation in EHRs across different healthcare institutions

Sunyang Fu, Maria Vassilaki, Omar A. Ibrahim, Ronald C. Petersen, Sandeep Pagali, Jennifer St Sauver, Sungrim Moon, Liwei Wang, Jungwei W. Fan, Hongfang Liu, Sunghwan Sohn

Research output: Contribution to journalArticlepeer-review


The secondary use of electronic health records (EHRs) faces challenges in the form of varying data quality-related issues. To address that, we retrospectively assessed the quality of functional status documentation in EHRs of persons participating in Mayo Clinic Study of Aging (MCSA). We used a convergent parallel design to collect quantitative and qualitative data and independently analyzed the findings. We discovered a heterogeneous documentation process, where the care practice teams, institutions, and EHR systems all play an important role in how text data is documented and organized. Four prevalent instrument-assisted documentation (iDoc) expressions were identified based on three distinct instruments: Epic smart form, questionnaire, and occupational therapy and physical therapy templates. We found strong differences in the usage, information quality (intrinsic and contextual), and naturality of language among different type of iDoc expressions. These variations can be caused by different source instruments, information providers, practice settings, care events and institutions. In addition, iDoc expressions are context specific and thus shall not be viewed and processed uniformly. We recommend conducting data quality assessment of unstructured EHR text prior to using the information.

Original languageEnglish (US)
Article number958539
JournalFrontiers in Digital Health
StatePublished - Sep 27 2022


  • aging
  • electronic health records
  • functional status (activity levels)
  • information quality
  • natural language processing

ASJC Scopus subject areas

  • Health Informatics
  • Computer Science Applications
  • Biomedical Engineering
  • Medicine (miscellaneous)


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