TY - JOUR
T1 - Quality assessment of functional status documentation in EHRs across different healthcare institutions
AU - Fu, Sunyang
AU - Vassilaki, Maria
AU - Ibrahim, Omar A.
AU - Petersen, Ronald C.
AU - Pagali, Sandeep
AU - St Sauver, Jennifer
AU - Moon, Sungrim
AU - Wang, Liwei
AU - Fan, Jungwei W.
AU - Liu, Hongfang
AU - Sohn, Sunghwan
N1 - Publisher Copyright:
2022 Fu, Vassilaki, Ibrahim, Petersen, Pagali, St Sauver, Moon, Wang, Fan, Liu and Sohn.
PY - 2022/9/27
Y1 - 2022/9/27
N2 - 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.
AB - 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.
KW - aging
KW - electronic health records
KW - functional status (activity levels)
KW - information quality
KW - natural language processing
UR - http://www.scopus.com/inward/record.url?scp=85139561765&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139561765&partnerID=8YFLogxK
U2 - 10.3389/fdgth.2022.958539
DO - 10.3389/fdgth.2022.958539
M3 - Article
AN - SCOPUS:85139561765
SN - 2673-253X
VL - 4
JO - Frontiers in Digital Health
JF - Frontiers in Digital Health
M1 - 958539
ER -