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Published on: June 20, 2020
Identifying Communication-Impaired Pediatric Patients Using Detailed Hospital Administrative Data
Douglas L Hill1, Karen W Carroll1, Dingwei Dai2
1Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; and.
Insights
Hospitalized children with communication impairment can now be identified using a new classification model. This tool helps improve pain and symptom management for these vulnerable pediatric patients.
Area of Science:
- Pediatric healthcare informatics
- Clinical data analysis
- Patient communication assessment
Background:
- Pediatric inpatients with communication impairment often receive inadequate pain and symptom management.
- Existing pediatric databases lack direct measures of patient communication ability, hindering research on care variations.
- Identifying communication-impaired children is crucial for equitable care and research.
Purpose of the Study:
- To develop and evaluate a classification model for identifying pediatric inpatients likely to have communication impairment within a large administrative database.
- To address the gap in data for studying care disparities in communication-impaired children.
- To enable better pain and symptom management for vulnerable pediatric populations.
Main Methods:
- Utilized a dataset of 236 hospitalized pediatric patients (age ≥12 months) with assessed pain communication ability.
- Randomly divided the sample into development (n=118) and validation (n=118) sets.
- Employed logistic regression with 11 pre-defined variables (diagnoses, technology dependencies, procedures, medications) to predict communication impairment.
Main Results:
- The classification model demonstrated excellent accuracy in the validation set (AUC 0.92, sensitivity 82.6%, specificity 86.3%).
- The model showed high sensitivity and specificity, indicating reliable identification of communication-impaired patients.
- Predicted probabilities of communication impairment were well-calibrated with observed statuses across the entire sample.
Conclusions:
- A robust classification model can accurately identify hospitalized pediatric patients with communication impairment using administrative data.
- This model facilitates research into care variations and can inform targeted interventions for improved patient outcomes.
- Enables better understanding and management of pain and symptoms in communication-impaired children.
Background And Objectives:
Pediatric inpatients with communication impairment may experience inadequate pain and symptom management. Research regarding potential variation in care among patients with and without communication impairment is hampered because existing pediatric databases do not include information about patient communication ability per se, even though these data sets do contain information about diagnoses and medical interventions that are probably correlated with the probability of communication impairment. Our objective was to develop and evaluate a classification model to identify patients in a large administrative database likely to be communication impaired.
Methods:
Our sample included 236 hospitalized patients aged ≥12 months whose ability to communicate about pain had been assessed. We randomly split this sample into development (n = 118) and validation (n = 118) sets. A priori, we developed a set of specific diagnoses, technology dependencies, procedures, and medications recorded in the Pediatric Health Information System likely to be strongly associated with communication impairment. We used logistic regression modeling to calculate the probability of communication impairment for each patient in the development set, assessed the model performance, and evaluated the performance of the 11-variable model in the validation set.
Results:
In the validation sample, the classification model showed excellent classification accuracy (area under the receiver operating characteristic curve 0.92; sensitivity 82.6%; 95% confidence interval, 74%-100%; specificity 86.3%; 95% confidence interval, 80%-97%). For the complete sample, the predicted probability of communication impairment demonstrated excellent calibration with the observed communication impairment status.
Conclusions:
Hospitalized pediatric patients with communication impairment can be accurately identified in a large hospital administrative database.
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