A big data approach to evaluate receipt of optimal care in childhood cerebral palsy

Abstract

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 REHABILITATION

• Optimal 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.

Keywords:

• Childhood cerebral palsy
• care delivery
• optimal care
• big data approach
• electronic health record

Acknowledgements

We thank Aimee Miley and Sydney Thompson for their help with regulatory support and preparing this manuscript submission, in addition to Surbhi Bhatnagar for describing the algorithms.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Cincinnati Children’s Research Foundation, Academic Research Committee grant and the National Institutes of Health (R01 HD103654).