Evaluating Completeness of Discrete Data on Physical Functioning for Children with Cerebral Palsy in a Pediatric Rehabilitation Learning Health System

Nikolas J. Koscielniak, Carole A. Tucker, Andrew Grogan-Kaylor, Charles P. Friedman, Rachel Richesson, Josh S. Tucker, Gretchen A. Piatt

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article numberpzab234
JournalPhysical therapy
Issue number1
StatePublished - Jan 1 2022
Externally publishedYes


  • Electronic Health Records
  • Gross Motor Function Classification System
  • Learning Health Systems
  • Pediatric Rehabilitation

ASJC Scopus subject areas

  • General Medicine


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