Abstract
This chapter describes how one may stock clinical data in digital forms, such as patient reports as an electronic health record, and how one may create meaningful information from these records utilizing analytics methods and tools. Apollo Hospital is the biggest hospital in West Bengal. It collects a huge quantity of heterogeneous data from various sources, including patient health records, lab test results, digital diagnostic supplies, healthcare insurance data, social media data, pharmaceutical data, gene expression records, transactions, and data from MY hospital's Mahatma Gandhi Memorial Medical College. Data analytics could be used to organise this data and make it retrievable. As a result, the term “big data” may be used. Big data is defined as exceptionally big datasets which may be analysed computationally to uncover trends, patterns, and relationships, as well as visualisation, querying, information privacy, and predictive analytics on a huge dataset.
Top2. Review Of The Literature
It has been discovered that digitalizing denotes a necessary in medical organisations since a great quantity of record is created connecting a patient's wellbeing data to its genomic investigation, and effective storage is sought utilising big data and analytics to maintain track of this information.
Doctors may produce profound insights, according to (Won et al 2021), via the digitalization of health records, which may expedite clinical processes, enhance treatment, build-patient associations, save expenditures, and better things.
In a research published in (Bi, H. et al. 2021) showed how big data analysis consists of having the ability for altering sophisticated technology for getting insight from healthcare and other datasets and making judgments.
(Mahajan, H.B. 2020) explained that attaining good results at reduced costs is critical for medical care, and that this may be accomplished by using MapReduce and Hadoop HDFS to unearth the knowledge hidden in large health datasets.
According to (Bi, H. et al. 2021) analysis by Mackinsey & Company, big data analytics may save the US $300 billion per year in costs, with $165 billion saved in medical operations and billions saved in Research & Development wastage. Indian government spends around 5.3% of the GDP on medical research, necessitating the utilization of technologies such as big data analysis to provide good medical treatment to the citizens.
Key Terms in this Chapter
Big Data: Big data is the term used to describe data sets that are too big or complicated for conventional data-processing application software to handle. While data with more complexity may result in a higher false discovery rate, data with more entries give better statistical power.
Electronic Health Record: The organised gathering of population- and patient-specific electronically stored health data in a digital format is called an electronic health record. These documents are interchangeable across various healthcare environments.
Primary Health Centres: In developing nations, the fundamental structural and operational component of public health services is the primary healthcare centre (PHC). As per the Alma Ata Declaration of 1978, PHCs were formed to provide people with primary health care that is accessible, inexpensive, and readily available. This was done in compliance with the World Health Organization's member states.
Hadoop: A set of free and open-source software tools called Hadoop makes it easier to use a large computer network to address challenges requiring enormous volumes of data and processing. It offers a software framework for the MapReduce programming paradigm, which is used for distributed massive data processing and storage.
Patient Management: It also involves managing patients in the sense of identifying their issues, monitoring their development, and offering the most effective course of therapy. Stated differently, we are discussing the clinical treatment of the person rather than relationship management.
Healthcare Analytics: Within the health care sector, health care analytics is a subset of data analytics that leverages historical and contemporary data to provide actionable insights, enhance decision-making, and maximise results.
Genomics: The analysis of an organism's whole or partial genetic or epigenetic sequence information, known as genomics, aims to comprehend the composition and operation of these sequences as well as the biological products that follow.