Machine learning approach to analyzing complex care coordination patterns for medically complex children
Saki Aoto1,2, Yoshikazu Ito3, Shintaro Morooka4
1Medical Genome Center, National Center for Child Health and Development, Tokyo, Japan.
Insights
Machine learning models can aid care coordination for children with medical complexity (CMC) by identifying key service needs. This AI approach helps optimize support for this growing patient population.
Area of Science:
- Pediatric Healthcare
- Artificial Intelligence in Medicine
- Health Informatics
Background:
- The population of children with medical complexity (CMC) in Japan exceeds 20,000, with significant international implications.
- Care coordination for CMC is challenging due to the need to address growth, school attendance, and limited coordinator experience.
- Investigated the potential of machine learning (ML) to support care coordination for CMC.
Purpose of the Study:
- To assess the feasibility of using ML models to predict care needs for CMC.
- To identify critical factors influencing service utilization and care patterns in CMC.
- To evaluate the effectiveness of AI in enhancing care coordination for CMC.
Main Methods:
- Developed three gradient boosting prediction models using 1,042 care sheets from a pediatric respite facility.
- Models predicted ventilator use, home doctor utilization, and age to assess care requirements.
- Utilized SHAP values for model interpretability and performed ten-fold cross-validation to mitigate sampling bias.
Main Results:
- Achieved high F1 scores (0.83, 0.826, 0.829) for the prediction models.
- Identified significant predictors such as equipment maintenance for ventilator use and complex service utilization for home doctor needs.
- Demonstrated varying factors influencing care needs across different school-age transitions.
Conclusions:
- AI shows significant potential as a tool for identifying crucial factors in CMC services and improving care coordination.
- Emphasized the importance of digital healthcare information preservation for CMC.
- ML models offer a valuable approach to support care coordination for children with medical complexity.
Background:
The number of children with medical complexity (CMC) in Japan has exceeded 20,000, doubling over the past 15 years, forming a significant population group internationally. While care coordination for CMC requires consideration of growth and school attendance, the relatively small population size compared to elderly care makes it challenging for care coordinators to gain sufficient experience. We investigated whether machine learning models could assist in care coordination for CMC.
Methods:
We developed three tree-based gradient boosting prediction models using 1,042 care sheets from a pediatric respite facility. The first model estimated ventilator use to verify if care patterns could predict symptoms; the second predicted home doctor utilization to examine if symptoms could indicate required care; and the third estimated age to analyze school-related care needs. SHAP values were used to interpret feature importance in the prediction models. Each model was independently created and validated ten times to account for potential sampling bias among respite facility users.
Results:
The highest F1 scores for the three models were 0.83, 0.826, and 0.829, respectively. The ventilator model identified equipment maintenance services as highly significant; the home doctor model revealed patterns in complex service utilization; and the age model demonstrated varying important factors across school-age transitions.
Conclusion:
This study highlights AI's potential in identifying crucial factors related to CMC services and its effectiveness as a valuable tool for service coordination. Despite the limited dataset of 1,042 cases, our findings emphasize the importance of digital preservation of CMC healthcare information.
Trial Registration:
Retrospectively registered.
Clinical Trial Number:
Not applicable.
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