Development of a standards‐based phenotype model for gross motor function to support learning health systems in pediatric rehabilitation

IntroductionResearch and continuous quality improvement in pediatric rehabilitation settings require standardized data and a systematic approach to use these data.MethodsWe systematically examined pediatric data concepts from a pediatric learning network to determine capacity for capturing gross motor function (GMF) for children with Cerebral Palsy (CP) as a demonstration for enabling infrastructure for research and quality improvement activities of an LHS. We used an iterative approach to construct phenotype models of GMF from standardized data element concepts based on case definitions from the Gross Motor Function Classification System (GMFCS). Data concepts were selected using a theory and expert‐informed process and resulted in the construction of four phenotype models of GMF: an overall model and three classes corresponding to deviations in GMF for CP populations.ResultsSixty five data element concepts were identified for the overall GMF phenotype model. The 65 data elements correspond to 20 variables and logic statements that instantiate membership into one of three clinically meaningful classes of GMF. Data element concepts and variables are organized into five domains relevant to modeling GMF: Neurologic Function, Mobility Performance, Activity Performance, Motor Performance, and Device Use.ConclusionOur experience provides an approach for organizations to leverage existing data for care improvement and research in other conditions. This is the first consensus‐based and theory‐driven specification of data elements and logic to support identification and labeling of GMF in patients for measuring improvements in care or the impact of new treatments. More research is needed to validate this phenotype model and the extent that these data differentiate between classes of GMF to support various LHS activities.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171600/1/lrh210266_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171600/2/lrh210266.pd