Navigating electronic health records (EHRs) remains a time-consuming task for clinicians, especially in resource-constrained healthcare settings. This study aimed to assess whether a lightweight chatbot, built using LangChain and GPT-3.5, could reduce search time and improve retrieval accuracy for CSV-based pediatric EHRs in a district hospital. A prototype EHR system was developed using Flask and Angular and integrated with a LangChain CSV agent capable of generating Python code dynamically to query anonymized data. The dataset consisted of 1,200 pediatric encounters, each comprising 160 variables. Forty clinicians performed 240 standardized information-seeking tasks both with and without the chatbot. The hospital’s legacy EHR system, which relied on basic keyword search, served as the baseline for comparison. Search time and retrieval accuracy—measured using precision and recall—were evaluated using two-tailed paired t-tests with a significance threshold of α = 0.05. The chatbot reduced the median search time from 64 s to 38 s, representing a 40.6% improvement (p < 0.001). It also increased F1 retrieval accuracy from 0.71 to 0.89, a 25.4% gain (p < 0.01). These results demonstrate that low-cost conversational AI can significantly accelerate and improve access to structured pediatric EHR data while maintaining data privacy by avoiding the exposure of raw patient records to external models.
Gowramma et al. (Thu,) studied this question.