Enhancing clinical data retrieval with Smart Watchers: a NiFi-based ETL pipeline for Elasticsearch queries

• Published in: BMC medical informatics and decision making 2024-09, Vol.24 (1), p.255-12
• Main Authors: Al-Agil, Mohammad, Obee, Stephen J, Dinu, Vlad, Teo, James, Brawand, David, Patten, Piers E M, Alhaq, Anwar
• Format: Article
• Language: English
• Subjects: Allergies; Apache Nifi; Archives & records; Automatic data collection systems; Automation; Clinical alert systems; CogStack; Comparative analysis; Data processing; Data retrieval; Decision making; Demographics; Elastic analysis; Elasticsearch; Electronic Health Records - standards; ETL pipelines; Humans; Information retrieval; Information Storage and Retrieval - methods; Medical informatics; Methods; Patient safety; Patients; Penicillin; Performance evaluation; Performance measurement; Public software; Python; Queries; Real time operation; Reliability; Software; Structured Query Language-SQL; Technological change; Time measurement; Unstructured data
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-024-02633-w

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. The automated clinical alert system, named Smart Watchers, was developed using Apache NiFi and Python scripts to create flexible data processing pipelines and customisable clinical alerts. A comparative analysis between Smart Watchers and the legacy Elastic Watchers was conducted to evaluate performance metrics such as accuracy, reliability, and scalability. The evaluation involved measuring the time taken for manual data extraction through the electronic patient record (EPR) front-end and comparing it with the automated data extraction process using Smart Watchers. Deployment of Smart Watchers showcased a consistent time savings between 90% to 98.67% compared to manual data extraction through the EPR front-end. The results demonstrate the efficiency of Smart Watchers in automating data extraction and alert generation, significantly reducing the time required for these tasks when compared to manual methods in a scalable manner. The research underscores the utility of employing an automated clinical alert system, and its portability facilitated its use across multiple clinical settings. The successful implementation and positive impact of the system lay a foundation for future technological innovations in this rapidly evolving field.