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.

Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population.

BACKGROUND: Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing. OBJECTIVE: We aimed to describe current ventilator data in the electronic health record (EHR) and propose a framework for standardizing these data using a common data model (CDM) across multiple populations and sites. METHODS: We focused on a cohort of patients with LTMV dependence who were weaned from mechanical ventilation (MV). We extracted and described relevant EHR ventilation data. We identified the minimum necessary components, termed "Clinical Ideas," to describe MV from time of initiation to liberation. We then utilized existing resources and partnered with informatics collaborators to develop a framework for incorporating Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP). RESULTS: We identified 78 children with LTMV dependence who weaned from ventilator support. There were 25 unique device names and 28 unique ventilation mode names used in the cohort. We identified multiple Clinical Ideas necessary to describe ventilator support over time: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. We used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration. CONCLUSION: The proposed framework standardizes mechanical ventilation terminology and may facilitate efficient data exchange in a multisite network. Rapid data sharing is necessary to improve research and clinical care for children with LTMV dependence.

Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic.

Importance: The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents. Objective: To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic. Design, Setting, and Participants: This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children's hospitals in the US and France. Main Outcomes and Measures: Change in the monthly proportion of mental health condition-associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis. Results: There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5%] female) and 11 101 during the pandemic period (7603 [68.5%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55%; 95% CI, 0.26%-0.84%), depression (0.50%; 95% CI, 0.19%-0.79%), and suicidality or self-injury (0.38%; 95% CI, 0.08%-0.68%). There was an estimated 0.60% increase (95% CI, 0.31%-0.89%) overall in the monthly proportion of mental health-associated hospitalizations following onset of the pandemic compared with the prepandemic period. Conclusions and Relevance: In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children's hospitals to care for adolescents with mental health conditions during the pandemic and beyond.

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

AIM: To characterize the patterns of care of children with cerebral palsy (CP) in a tertiary healthcare system. METHOD: Electronic health record data from 2009 to 2019 were extracted for children with CP. Machine learning hierarchical clustering was used to identify clusters of care. The ratio of in-person to care coordination visits was calculated for each specialty. RESULTS: The sample included 6369 children with CP (55.7% males, 44.3% females, 76.2% white, 94.7% non-Hispanic; with a mean age of 8y 2mo [SD 5y 10mo; range 0-21y; median 7y 1mo]) at the time of diagnosis. A total of 3.7 million in-person visits and care coordination notes were identified across 34 specialties. The duration of care averaged 5 years 5 months with five specialty interactions and 21.8 in-person visits per year per child. Seven clusters of care were identified, including: musculoskeletal and function; neurological; high-frequency/urgent care services; procedures; comorbid diagnoses; development and behavioral; and primary care. Network analysis showed shared membership among several clusters. INTERPRETATION: Coordination of care is a central element for children with CP. Medical informatics, machine learning, and big data approaches provide unique insights into care delivery to inform approaches to improve outcomes for children with CP. What this paper adds Seven primary clusters of care were identified: musculoskeletal and function; neurological; high-frequency/urgent care services; procedures; comorbid diagnoses; development and behavioral; and primary care. The in-person to care coordination visit ratio was 1:5 overall for healthcare encounters. Most interactions with care teams occur outside of in-person visits. The ratio of in-person to care coordination activities differ by specialty.

Assessment of 135 794 Pediatric Patients Tested for Severe Acute Respiratory Syndrome Coronavirus 2 Across the United States.

Importance: There is limited information on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing and infection among pediatric patients across the United States. Objective: To describe testing for SARS-CoV-2 and the epidemiology of infected patients. Design, Setting, and Participants: A retrospective cohort study was conducted using electronic health record data from 135 794 patients younger than 25 years who were tested for SARS-CoV-2 from January 1 through September 8, 2020. Data were from PEDSnet, a network of 7 US pediatric health systems, comprising 6.5 million patients primarily from 11 states. Data analysis was performed from September 8 to 24, 2020. Exposure: Testing for SARS-CoV-2. Main Outcomes and Measures: SARS-CoV-2 infection and coronavirus disease 2019 (COVID-19) illness. Results: A total of 135 794 pediatric patients (53% male; mean [SD] age, 8.8 [6.7] years; 3% Asian patients, 15% Black patients, 11% Hispanic patients, and 59% White patients; 290 per 10 000 population [range, 155-395 per 10 000 population across health systems]) were tested for SARS-CoV-2, and 5374 (4%) were infected with the virus (12 per 10 000 population [range, 7-16 per 10 000 population]). Compared with White patients, those of Black, Hispanic, and Asian race/ethnicity had lower rates of testing (Black: odds ratio [OR], 0.70 [95% CI, 0.68-0.72]; Hispanic: OR, 0.65 [95% CI, 0.63-0.67]; Asian: OR, 0.60 [95% CI, 0.57-0.63]); however, they were significantly more likely to have positive test results (Black: OR, 2.66 [95% CI, 2.43-2.90]; Hispanic: OR, 3.75 [95% CI, 3.39-4.15]; Asian: OR, 2.04 [95% CI, 1.69-2.48]). Older age (5-11 years: OR, 1.25 [95% CI, 1.13-1.38]; 12-17 years: OR, 1.92 [95% CI, 1.73-2.12]; 18-24 years: OR, 3.51 [95% CI, 3.11-3.97]), public payer (OR, 1.43 [95% CI, 1.31-1.57]), outpatient testing (OR, 2.13 [1.86-2.44]), and emergency department testing (OR, 3.16 [95% CI, 2.72-3.67]) were also associated with increased risk of infection. In univariate analyses, nonmalignant chronic disease was associated with lower likelihood of testing, and preexisting respiratory conditions were associated with lower risk of positive test results (standardized ratio [SR], 0.78 [95% CI, 0.73-0.84]). However, several other diagnosis groups were associated with a higher risk of positive test results: malignant disorders (SR, 1.54 [95% CI, 1.19-1.93]), cardiac disorders (SR, 1.18 [95% CI, 1.05-1.32]), endocrinologic disorders (SR, 1.52 [95% CI, 1.31-1.75]), gastrointestinal disorders (SR, 2.00 [95% CI, 1.04-1.38]), genetic disorders (SR, 1.19 [95% CI, 1.00-1.40]), hematologic disorders (SR, 1.26 [95% CI, 1.06-1.47]), musculoskeletal disorders (SR, 1.18 [95% CI, 1.07-1.30]), mental health disorders (SR, 1.20 [95% CI, 1.10-1.30]), and metabolic disorders (SR, 1.42 [95% CI, 1.24-1.61]). Among the 5374 patients with positive test results, 359 (7%) were hospitalized for respiratory, hypotensive, or COVID-19-specific illness. Of these, 99 (28%) required intensive care unit services, and 33 (9%) required mechanical ventilation. The case fatality rate was 0.2% (8 of 5374). The number of patients with a diagnosis of Kawasaki disease in early 2020 was 40% lower (259 vs 433 and 430) than in 2018 or 2019. Conclusions and Relevance: In this large cohort study of US pediatric patients, SARS-CoV-2 infection rates were low, and clinical manifestations were typically mild. Black, Hispanic, and Asian race/ethnicity; adolescence and young adulthood; and nonrespiratory chronic medical conditions were associated with identified infection. Kawasaki disease diagnosis is not an effective proxy for multisystem inflammatory syndrome of childhood.

Implementation of a Regional Perinatal Data Repository from Clinical and Billing Records.

Objectives To describe the implementation of the first phase of a regional perinatal data repository and to provide a roadmap for others to navigate technical, privacy, and data governance concerns in implementing similar resources. Methods Our implementation integrated regional physician billing records with maternal and infant electronic health records from an academic delivery hospital. These records, representing births during 2013-2015, constituted a data core supporting linkage to additional ancillary data sets. Measures obtained from pediatric follow-up, urgent care, emergency, and inpatient encounters were linked at the individual level as were measures obtained by home visitors during pre- and postnatal encounters. Residential addresses were geocoded supporting linkage to area-level measures. Results Integrated data contained regional billing records for 69,290 newborns representing approximately 81% of all regional live births and nearly 95% of live births in the region's most populous county. Billing records linked to 7293 infant delivery hospital records and 7107 corresponding maternal hospital records. Manual review demonstrated 100% validity of matches among audited records. Additionally, 2430 home visiting records were linked to the data core as were pediatric primary care, urgent care, emergency department, and inpatient visits representing 42,541 children. More than 99% of the newborn billing records were geocoded and assigned a census tract identifier. Conclusions for Practice Our approach to methodological and regulatory challenges affords opportunities for expansion of systems to integrate electronic health records originating from additional medical centers as well as individual- and area-level linkage to additional data sets relevant to perinatal health.