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Evaluating Completeness of Discrete Data on Physical Functioning for Children With Cerebral Palsy in a Pediatric
Nikolas J Koscielniak1, Carole A Tucker2, Andrew Grogan-Kaylor3
1Clinical and Translational Science Institute, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.
Insights
Completeness of physical function data in electronic health records for pediatric rehabilitation is low. This highlights a need to improve documentation standards for better quality measurement and research.
Area of Science:
- Pediatric Rehabilitation
- Health Informatics
- Data Science
Background:
- Electronic health records (EHR) are crucial for pediatric rehabilitation.
- Physical function discrete data elements (DDE) within EHRs are essential for quality measurement.
- Current completeness of these DDEs requires investigation.
Purpose of the Study:
- To assess the completeness of physical function DDEs documented in EHRs.
- To analyze data completeness within pediatric rehabilitation settings.
- To identify factors influencing data completeness.
Main Methods:
- A descriptive analysis was performed on EHR data from a pediatric rehabilitation research learning health system.
- Data from 20 care sites and 5766 outpatient records were analyzed.
- Completeness was calculated for unique data elements, visits, and records.
Main Results:
- Physical function DDE completeness was low (10.5%), with an average of 2 DDEs documented per record.
- The Gross Motor Function Classification System (GMFCS) level was documented in 21% of visits and 38% of records.
- Care site level factors accounted for 21.4% and 45% of the variance in DDE and GMFCS completeness, respectively.
Conclusions:
- Significant gaps exist in the documentation of physical function DDEs in pediatric rehabilitation EHRs.
- Missing data are not random and are influenced by site-specific practices and system standards.
- Improving DDE completeness is vital for enhancing learning health system research and quality measurement.
Objective:
The purpose of this study was to determine the extent that physical function discrete data elements (DDE) documented in electronic health records (EHR) are complete within pediatric rehabilitation settings.
Methods:
A descriptive analysis on completeness of EHR-based DDEs detailing physical functioning for children with cerebral palsy was conducted. Data from an existing pediatric rehabilitation research learning health system data network, consisting of EHR data from 20 care sites in a pediatric specialty health care system, were leveraged. Completeness was calculated for unique data elements, unique outpatient visits, and unique outpatient records.
Results:
Completeness of physical function DDEs was low across 5766 outpatient records (10.5%, approximately 2 DDEs documented). The DDE for Gross Motor Function Classification System level was available for 21% (n = 3746) outpatient visits and 38% of patient records. Ambulation level was the most frequently documented DDE. Intercept only mixed effects models demonstrated that 21.4% and 45% of the variance in completeness for DDEs and the Gross Motor Function Classification System, respectively, across unique patient records could be attributed to factors at the individual care site level.
Conclusion:
Values of physical function DDEs are missing in designated fields of the EHR infrastructure for pediatric rehabilitation providers. Although completeness appears limited for these DDEs, our observations indicate that data are not missing at random and may be influenced by system-level standards in clinical documentation practices between providers and factors specific to individual care sites. The extent of missing data has significant implications for pediatric rehabilitation quality measurement. More research is needed to understand why discrete data are missing in EHRs and to further elucidate the professional and system-level factors that influence completeness and missingness.
Impact:
Completeness of DDEs reported in this study is limited and presents a significant opportunity to improve documentation and standards to optimize EHR data for learning health system research and quality measurement in pediatric rehabilitation settings.
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