The Implementation of Artificial Intelligence-Based Models of Postoperative Care in Paediatric Healthcare Settings

Abstract

Postoperative pain management in pediatric patients remains an important problem because young children cannot verbally express pain. Unrelieved pain can have adverse neurodevelopmental outcomes, but conventional intermittent monitoring is often insufficient in capturing transient pain crises, especially in resource-constrained settings. This study develops and tests an AI-based multimodal construct of continuous, automated pain surveillance but specifically within the healthcare ecosystem of Haryana, India. Employing a mixed-methods approach to research, we combined clinical data on 100 pediatric patients at four districts (Hisar, Sirsa, Rohtak and Panipat) with an AI simulation trained on multimodal data (facial expressions, cry acoustics, and physiological vitals). The classification accuracy obtained by the proposed AI model was 90.20% and Area under the Curve (AUC) was 0.93, showing a good correlation (r = 0.88, p < 0.001) with expert clinical evaluations by FLACC and Wong-Baker scales. An alert latency of less than 1 minute was shown by the system, thus significantly faster than manual rounds. Furthermore, a perception survey of 20 healthcare officials showed a high degree of acceptance of the clinical utility of the technology (mean score 4.4/5) although training gaps are a major hindrance (score 3.65/5). The findings suggest that response latency and missed high pain episodes can be considerably reduced by AI assisted monitoring by around 45%. This framework can provide an ideal, scientifically-backed answer to improving the quality of care of pediatric patients in Haryana, as long as ethical governance and structured training of personnel take priority.

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