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Quality assessment of functional status documentation in EHRs across different healthcare institutions.
Fu, Sunyang; Vassilaki, Maria; Ibrahim, Omar A; Petersen, Ronald C; Pagali, Sandeep; St Sauver, Jennifer; Moon, Sungrim; Wang, Liwei; Fan, Jungwei W; Liu, Hongfang; Sohn, Sunghwan.
Afiliación
  • Fu S; Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Vassilaki M; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
  • Ibrahim OA; Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Petersen RC; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
  • Pagali S; Department of Neurology, Mayo Clinic, Rochester, MN, United States.
  • St Sauver J; Department of Medicine, Mayo Clinic, Rochester, MN, United States.
  • Moon S; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
  • Wang L; Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Fan JW; Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Liu H; Department of AI and Informatics, Mayo Clinic, Rochester, MN, United States.
  • Sohn S; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Front Digit Health ; 4: 958539, 2022.
Article en En | MEDLINE | ID: mdl-36238199
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.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Qualitative_research Idioma: En Revista: Front Digit Health Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Qualitative_research Idioma: En Revista: Front Digit Health Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos