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Quality assessment of functional status documentation in EHRs across different healthcare institutions
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 conv...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552292/ https://www.ncbi.nlm.nih.gov/pubmed/36238199 http://dx.doi.org/10.3389/fdgth.2022.958539 |
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author | 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 |
author_facet | 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 |
author_sort | Fu, Sunyang |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9552292 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95522922022-10-12 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 Front Digit Health Digital Health 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. Frontiers Media S.A. 2022-09-27 /pmc/articles/PMC9552292/ /pubmed/36238199 http://dx.doi.org/10.3389/fdgth.2022.958539 Text en © 2022 Fu, Vassilaki, Ibrahim, Petersen, Pagali, St Sauver, Moon, Wang, Fan, Liu and Sohn. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health 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 Quality assessment of functional status documentation in EHRs across different healthcare institutions |
title | Quality assessment of functional status documentation in EHRs across different healthcare institutions |
title_full | Quality assessment of functional status documentation in EHRs across different healthcare institutions |
title_fullStr | Quality assessment of functional status documentation in EHRs across different healthcare institutions |
title_full_unstemmed | Quality assessment of functional status documentation in EHRs across different healthcare institutions |
title_short | Quality assessment of functional status documentation in EHRs across different healthcare institutions |
title_sort | quality assessment of functional status documentation in ehrs across different healthcare institutions |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9552292/ https://www.ncbi.nlm.nih.gov/pubmed/36238199 http://dx.doi.org/10.3389/fdgth.2022.958539 |
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