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By Dr. Ganapathi Pulipaka, CEO, DeepSingularity
This article is by Featured Blogger Dr. Ganapathi Pulipaka from his Medium page. Republished with the author’s permission.
The technological framework for healthcare information systems has a new paradigm to handle fast and accelerated medical data coming from disparate sources--diagnostics tools, DNA mapping, precision medicine, bioinformatics, medical devices, Internet of Medical Things, biopharma, neurology, cardiovascular, drug discovery, and drug development. The majority of current challenges stem from lack of data liquidity and real-time data analytics in healthcare information systems. Healthcare providers adopting big data technologies such as Apache Hadoop can resolve major issues with data liquidity Recently, McKinsey has released a research report describing the new value pathways for the healthcare system that makes data flow in a more agile way (Sears, 2013).
Participants in cardiovascular treatments can leverage wearables and sensors to transmit vital signs wirelessly into the healthcare information systems. Machine learning algorithms deployed on an Apache Hadoop system can track the data and identify anomalies in specific patterns and trends to avoid any risk of readmission into the hospitals.
This can be achieved by participation in the treatment in the early stages through real-time monitoring systems. The data from the sensors is transmitted from the participants to Apache Hadoop ecosystem every minute to track the vital signs and alert the physicians for any flagged alerts.
Providing the best treatment options from various healthcare providers for storing historical EMR analysis on Apache Hadoop from clinical trials. Apache Hadoop HDFS can be key for promoting the best practices for processing such data.
Leveraging geospatial data and location intelligence through the sensors data for medical devices and equipment can provide insights on the operational intelligence of the healthcare devices. Apache Hadoop can provide the cost-effectiveness of medical devices through robust analysis on the usage of various medical devices and effectiveness for providing real-time statistics on the operations.
There is a massive treasure trove of anonymized information in Apache Hadoop ecosystem based on the clinical trials conducted on patients allowing cohort discovery. The researchers at various medical institutions perform sampling of the data through statistical framework applying machine learning and deep learning methods to identify new therapies by building innovation engines and submitting their results to the Internal Review Board for process review and approval.
Sears, J. (2013). Modern Healthcare Architectures Built with Hadoop. Retrieved July 26, 2016, from http://hortonworks.com/blog/modern-healthcare-architectures-built-with-hadoop/
Originally published on Medium.