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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-09-01
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| Series: | Frontiers in Digital Health |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdgth.2022.958539/full |
| _version_ | 1811203933879664640 |
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| author | Sunyang Fu Maria Vassilaki Omar A. Ibrahim Ronald C. Petersen Ronald C. Petersen Sandeep Pagali Jennifer St Sauver Sungrim Moon Liwei Wang Jungwei W. Fan Jungwei W. Fan Hongfang Liu Sunghwan Sohn |
| author_facet | Sunyang Fu Maria Vassilaki Omar A. Ibrahim Ronald C. Petersen Ronald C. Petersen Sandeep Pagali Jennifer St Sauver Sungrim Moon Liwei Wang Jungwei W. Fan Jungwei W. Fan Hongfang Liu Sunghwan Sohn |
| author_sort | Sunyang Fu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T03:03:10Z |
| format | Article |
| id | doaj.art-c82236d9c2bb4d3c8d1d409b129da4de |
| institution | Directory Open Access Journal |
| issn | 2673-253X |
| language | English |
| last_indexed | 2024-04-12T03:03:10Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Digital Health |
| spelling | doaj.art-c82236d9c2bb4d3c8d1d409b129da4de2022-12-22T03:50:35ZengFrontiers Media S.A.Frontiers in Digital Health2673-253X2022-09-01410.3389/fdgth.2022.958539958539Quality assessment of functional status documentation in EHRs across different healthcare institutionsSunyang Fu0Maria Vassilaki1Omar A. Ibrahim2Ronald C. Petersen3Ronald C. Petersen4Sandeep Pagali5Jennifer St Sauver6Sungrim Moon7Liwei Wang8Jungwei W. Fan9Jungwei W. Fan10Hongfang Liu11Sunghwan Sohn12Department of AI and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of Neurology, Mayo Clinic, Rochester, MN, United StatesDepartment of Medicine, Mayo Clinic, Rochester, MN, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United StatesDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, United StatesDepartment of AI and Informatics, Mayo Clinic, Rochester, MN, United StatesThe 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.https://www.frontiersin.org/articles/10.3389/fdgth.2022.958539/fullinformation qualityelectronic health recordsnatural language processingfunctional status (activity levels)aging |
| spellingShingle | Sunyang Fu Maria Vassilaki Omar A. Ibrahim Ronald C. Petersen Ronald C. Petersen Sandeep Pagali Jennifer St Sauver Sungrim Moon Liwei Wang Jungwei W. Fan Jungwei W. Fan Hongfang Liu Sunghwan Sohn Quality assessment of functional status documentation in EHRs across different healthcare institutions Frontiers in Digital Health information quality electronic health records natural language processing functional status (activity levels) aging |
| 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 | information quality electronic health records natural language processing functional status (activity levels) aging |
| url | https://www.frontiersin.org/articles/10.3389/fdgth.2022.958539/full |
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