EFFECTIVENESS OF DATA STREAMING TECHNOLOGIES ON REAL TIME
PATIENT MONITORING
INTRODUCTION
In the healthcare industry, real-time patient monitoring plays a crucial role in ensuring
timely interventions and improving patient outcomes. With the advent of wearable
medical devices, remote sensing technologies, and the Internet of Things (IoT), an
enormous amount of data is being continuously generated, leading to the emergence
of streaming big data. This real-time data stream poses significant challenges in
terms of ingestion, processing, and analysis, as traditional batch processing methods
are inadequate and time-consuming. Consequently, there is a pressing need for
efficient and scalable data streaming technologies that can effectively handle and
process these continuous data streams in real-time, enabling healthcare providers to
make informed decisions and take appropriate actions promptly.
The paper titled ‘ Data streaming applications and their Usage in Healthcare’ by
(Mangamuri, 2023) provide a compherehensive overview of the of data streaming
applications and their applications in the healthcare domain. The author introduces
the concept of streaming data and discusses the key components of Confluent
Kafka, a leading platform for building scalable and efficient data streaming
applications. The paper highlights the best design practices and considerations for
developing Confluent Kafka-based streaming applications and explores real-world
use cases in healthcare, such as patient monitoring, prior authorizations, and clinical
trials.
However the paper entitled ‘Real time healthcare monitoring system using Online
Machine learning and Spark streaming’ by (Hassan et al., 2020) presents a novel
online prediction system for real-time health status monitoring. The proposed system
leverages Apache Kafka for data ingestion, Apache Spark Streaming for data
processing, and streaming machine learning algorithms (e.g., streaming linear
regression with SGD) for online prediction. The authors evaluate the system's
performance using historical medical datasets and simulated wearable sensor data,
demonstrating its effectiveness in accurately predicting health status in real-time.
The two papers discussed above highlight the significance of data streaming
technologies in the healthcare domain and provide insights into the design,
implementation, and evaluation of real-time patient monitoring systems. By
leveraging efficient data streaming platforms like Confluent Kafka and integrating
them with streaming machine learning techniques, healthcare providers can unlock
the potential of real-time data analysis, enabling timely interventions, improved
patient outcomes, and enhanced healthcare delivery. Both papers discussed
contribute significant knowledge and insights into the design, implementation, and
evaluation of real-time patient monitoring systems using data streaming
technologies, making them highly relevant to the research topic at hand.