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A big data approach to evaluate receipt of optimal care in childhood cerebral palsy
Alexis Mitelpunkt1,2,3, Megan A Stodola4, Jilda Vargus-Adams5,6,7
1Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Insights
This study quantified optimal care for children with cerebral palsy (CP) using electronic health records. Higher adherence to recommended care, measured by the Receipt of Care Score (ROCS), correlated with better functional outcomes.
Area of Science:
- Pediatric Rehabilitation
- Health Services Research
- Big Data Analytics
Background:
- Children with cerebral palsy (CP) require multidisciplinary care aligned with evidence-based recommendations.
- Quantifying the delivery of optimal care in real-world settings remains a challenge.
- Electronic health records (EHR) offer a rich data source for evaluating care delivery.
Observation:
- A novel approach was developed to define and quantify optimal care for children with CP using EHR data.
- The Receipt of Care Score (ROCS) was created, incorporating physical medicine and rehabilitation (PMR), spasticity management, physical therapy (PT), and occupational therapy (OT).
- Proof-of-concept analysis was conducted on two children with unilateral CP, GMFCS Level II.
Findings:
- ROCS scores revealed variations in care delivery over time and across different care components.
- The child with a higher adjusted ROCS demonstrated superior functional outcomes.
- ROCS effectively quantified adherence to care recommendations and correlated with quality of life measures.
Implications:
- Optimal, integrated multidisciplinary care for childhood CP can be defined and quantified.
- EHR data and Big Data approaches are valuable tools for understanding and improving CP care.
- The developed ROCS methodology is robust, scalable, and ready for larger-scale validation.
Purpose:
Through automated electronic health record (EHR) data extraction and analysis, this project systematically quantified actual care delivery for children with cerebral palsy (CP) and evaluated alignment with current evidence-based recommendations.
Methods:
Utilizing EHR data for over 8000 children with CP, we developed an approach to define and quantify receipt of optimal care, and pursued proof-of-concept with two children with unilateral CP, Gross Motor Function Classification System (GMFCS) Level II. Optimal care was codified as a cluster of four components including physical medicine and rehabilitation (PMR) care, spasticity management, physical therapy (PT), and occupational therapy (OT). A Receipt of Care Score (ROCS) quantified the degree of adherence to recommendations and was compared with the Pediatric Outcomes Data Collection Instrument (PODCI) and Pediatric Quality of Life Inventory (PEDS QL).
Results:
The two children (12 year old female, 13 year old male) had nearly identical PMR and spasticity component scores while PT and OT scores were more divergent. Functional outcomes were higher for the child who had higher adjusted ROCS.
Conclusions:
ROCSs demonstrate variation in real-world care delivered over time and differentiate between components of care. ROCSs reflect overall function and quality of life. The ROCS methods developed are novel, robust, and scalable and will be tested in a larger sample.IMPLICATIONS FOR REHABILITATIONOptimal practice, with an emphasis on integrated multidisciplinary care, can be defined and quantified utilizing evidence-based recommendations.Receipt of optimal care for childhood cerebral palsy can be scored using existing electronic health record data.Big Data approaches can contribute to the understanding of current care and inform approaches for improved care.
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