Electronic health record and patterns of care for children with cerebral palsy

Outpatient encounters, continuity of care, and unplanned hospital care for children and young people with cerebral palsy

AIM

To describe the relationships between outpatient encounters, continuity of care, and unplanned hospital care in children/young people with cerebral palsy (CP).

METHOD

In this population-based data-linkage cohort study we included children/young people with CP identified in the New South Wales/Australian Capital Territory CP Register (birth years 1994-2018). We measured the frequency of outpatient encounters and unplanned hospital care, defined as presentations to emergency departments and/or urgent hospital admissions (2015-2020). Continuity of outpatient care was measured using the Usual Provider of Care Index (UPCI).

RESULTS

Of 3267 children/young people with CP, most (n = 2738, 83.8%, 57.6% male) had one or more outpatient encounters (123 463 total encounters, median six outpatient encounters per year during childhood). High UPCI was more common in children/young people with mild CP (Gross Motor Function Classification System levels I-III, with no epilepsy or no intellectual disability), residing in metropolitan and areas of least socioeconomic disadvantage. Low UPCI was associated with four or more emergency department presentations (adjusted odds ratio [aOR] 2.34; 95% confidence interval [CI] 1.71-3.19) and one or more urgent hospital admissions (aOR 2.02; 95% CI 1.57-2.61).

INTERPRETATION

Children/young people with CP require frequent outpatient services. Improving continuity of care, particularly for those residing in regional/remote areas, may decrease need for unplanned hospital care.

WHAT THIS PAPER ADDS

Many children with cerebral palsy use multiple and frequent outpatient services. Better continuity of care is associated with living in metropolitan and less socioeconomically disadvantaged areas. Outpatient service utilization reduces at the time of transition to adult services. High outpatient utilization is associated with unplanned hospital care. Decreased continuity of care is associated with unplanned hospital care.

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

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.

Outpatient hospital utilization after single event multi-level surgery in children with cerebral palsy

PURPOSE

This study aimed to examine outpatient hospital utilization (number of specialties seen and number of visits to each specialty) in the year after single event multi-level surgery (SEMLS) in children with cerebral palsy (CP), and to determine if utilization differs across the medical center in the year after compared to the year before SEMLS.

METHODS

This retrospective cross-sectional study used electronic medical record data of outpatient hospital utilization in children with CP who underwent SEMLS.

RESULTS

Thirty children with CP (Gross Motor Function Classification System Levels I-V, mean age of 9.9 years) were included. In the year after surgery, a significant difference (p = 0.001) was found for the number of specialties seen, with non-ambulatory children seeing more specialties than ambulatory children. No statistically significant difference was found between the number of outpatient visits to each specialty in the year after SEMLS. Compared to the year before SEMLS, fewer therapy visits occurred in the year after SEMLS (p <  0.001) but significantly more visits to orthopaedics (p = 0.001) and radiology (p = 0.001).

CONCLUSION

Children with CP had fewer therapy visits but more orthopaedic and radiology visits the year after SEMLS. Nearly half of the children were non-ambulatory. Examination of care needs in children with CP undergoing SEMLS is justified with consideration of ambulatory status, surgical burden, and post-operative immobilization.

Data harnessing to nurture the human mind for a tailored approach to the child

Big data in pediatrics is an ocean of structured and unstructured data. Big data analysis helps to dive into the ocean of data to filter out information that can guide pediatricians in their decision making, precision diagnosis, and targeted therapy. In addition, big data and its analysis have helped in the surveillance, prevention, and performance of the health system. There has been a considerable amount of work in pediatrics that we have tried to highlight in this review and some of it has been already incorporated into the health system. Work in specialties of pediatrics is still forthcoming with the creation of a common data model and amalgamation of the huge "omics" database. The physicians entrusted with the care of children must be aware of the outcome so that they can play a role to ensure that big data algorithms have a clinically relevant effect in improving the health of their patients. They will apply the outcome of big data and its analysis in patient care through clinical algorithms or with the help of embedded clinical support alerts from the electronic medical records. IMPACT: Big data in pediatrics include structured, unstructured data, waveform data, biological, and social data. Big data analytics has unraveled significant information from these databases. This is changing how pediatricians will look at the body of available evidence and translate it into their clinical practice. Data harnessed so far is implemented in certain fields while in others it is in the process of development to become a clinical adjunct to the physician. Common databases are being prepared for future work. Diagnostic and prediction models when incorporated into the health system will guide the pediatrician to a targeted approach to diagnosis and therapy.

Estimation of Gross Motor Functions in Children with Cerebral Palsy Using Zebris FDM-T Treadmill

A standardized observational instrument designed to measure change in gross motor function over time in children with cerebral palsy is the Gross Motor Function Measure (GMFM). The process of evaluating a value for the GMFM index can be time consuming. It typically takes 45 to 60 min for the patient to complete all tasks, sometimes in two or more sessions. The diagnostic procedure requires trained and specialized therapists. The paper presents the estimation of the GMFM measure for patients with cerebral palsy based on the results of the Zebris FDM-T treadmill. For this purpose, the regression analysis was used. Estimations based on the Generalized Linear Regression were assessed using different error metrics. The results obtained showed that the GMFM score can be estimated with acceptable accuracy. Because the Zebris FDM-T is a widely used device in gait rehabilitation, our method has the potential to be widely adopted for objective diagnostics of children with cerebral palsy.