Enhancing Clinical Data Retrieval with Smart Watchers: A NiFi-based ETL Pipeline for Elasticsearch Queries

Abstract

Abstract Background The aim is to develop and deploy an automated clinical alert system to enhance patient care and streamline healthcare operations. Structured and unstructured data from multiple sources are used to generate near real-time alerts for specific clinical scenarios, with an additional goal to improve clinical decision-making through accuracy and reliability. Results The automated clinical alert system was developed employing Apache NiFi and Python scripts to simplify data processing pipelines and flexibility in data manipulation and customization of clinical alerts. A comparative analysis between the new automated system and the legacy watchers was performed to evaluate performance metrics such as accuracy, reliability, and scalability. Deployment of the automated clinical alert system showcased a marked improvement in patient care and healthcare operations when compared with legacy watchers. The results indicate its superiority in aspects of accuracy, reliability, and scalability, ensuring efficient data integration and timely alert generation for various clinical scenarios, when compared to manual data extraction and analysis. Conclusions The research underscores the utility of employing an automated clinical alert system and its portability facilitated its use across multiple clinical settings, magnifying its impact across varied healthcare scenarios. The study accentuates the vital role that Smart Watchers play in the development of automated clinical alerting systems, highlighting a transformative potential to optimize patient care and healthcare delivery across various clinical settings. The successful implementation and positive impact of the system lay a foundation for future technological innovations in this rapidly evolving field.