Authors: Jimit Kishor Mehta, Tanvi Umesh Mehta, Divya Jitendra Chaudhari
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Amid this pandemic situation all around the world, data analysis has proved helpful by contributing to the development of Smart Healthcare System. With the development of information technology, the concept of smart healthcare has gradually come to the fore. Smart healthcare uses a new generation of information technologies, such as the internet of things (loT), data analysis, cloud computing, and artificial intelligence, to transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. The use of such healthcare system have helped to diagnose many victims during the absence of a doctor. Also, the use of these healthcare systems do not only help patients know how to manage their health but assists healthcare providers to reduce emergency cases, track patients, staff, and inventory; for the overall control of epidemics.
 Data Analysis has changed the way we manage, analyze, and leverage data across industries. One of the most notable areas where data analysis is making big changes is healthcare. In fact, healthcare analytics has the potential to reduce costs of treatment, predict outbreaks of epidemics, avoid preventable diseases, and improve the quality of life in general. The average human lifespan is increasing across the world population, which poses new challenges to today’s treatment delivery methods. Health professionals, just like business entrepreneurs, are capable of collecting massive amounts of data and look for the best strategies to use these numbers. Data Analysis is becoming the new key raw material for the healthcare industry helping AI and machine learning algorithms and data scientists utilize such crucial information and improve the various services into the sub-fields of the medical industry.
II. RESEARCH SCOPE
 The tremendous growth of Internet-of-Things devices enable the data collection of health-related parameters (e.g., body temperature, blood pressure, heart beat, respiratory rate, oxygen saturation, blood glucose level, wrist pulse signal, magnetoencephalogram (MEG), galvanic skin response (GSR), electrooculography (EOG), mechanomyogram (MMG), electromyogram (EMG), electrocardiogram (ECG) and electroencephalogram (EEG)). Numerous data analytic techniques are applied to analyze the data in order to realize smart healthcare applications. The world has been seeking effective measures to relieve the issues of population ageing as well as inadequate amounts of medical staff. And in this pandemic situation present worldwide, a smart healthcare system comes to the saviour.
How does Healthcare and Data Analytics Go Hand in Hand?
 Analytics is driving the healthcare industry towards an upgrade and upliftment. The customer satisfaction is the priority with the minimal chaos in the management on this side. Sensor-driven data has led to various steps being taken like coaching for elderly people including real-time feedback. Patient care analytics solutions are yet another boom. The market is offering several options to choose from to select the appropriate healthcare data analytics solution provider. It is a boon to have a variety of choices but at the same time it does makes our task as the customers of these services are a little complex.
A. Data Analysis in Healthcare
 Data Analysis in healthcare is a term used to describe massive volumes of information created by the adoption of digital technologies that collect patients' records and help in managing hospital performance, otherwise too large and complex for traditional technologies. The application of data analysis in healthcare has a lot of positive and also life-saving outcomes. In essence, big-style data refers to the vast quantities of information created by the digitization of everything, that gets consolidated and analyzed by specific technologies. Applied to healthcare, it will use specific health data of a population (or of a particular individual) and potentially help to prevent epidemics, cure disease, cut down costs, etc.
Now that we live longer, treatment models have changed and many of these changes are namely driven by data. Doctors want to understand as much as they can about a patient and as early in their life as possible, to pick up warning signs of serious illness as they arise – treating any disease at an early stage is far more simple and less expensive. By utilizing key performance indicators in healthcare and healthcare data analytics, prevention is better than cure, and managing to draw a comprehensive picture of a patient will let insurance provide a tailored package. This is the industry’s attempt to tackle the siloes problems a patient’s data has: everywhere are collected bits and bites of it and archived in hospitals, clinics, surgeries, etc., with the impossibility to communicate properly. Indeed, for years gathering huge amounts of data for medical use has been costly and time-consuming. With today’s always-improving technologies, it becomes easier not only to collect such data but also to create comprehensive healthcare reports and convert them into relevant critical insights, that can then be used to provide better care. This is the purpose of healthcare data analytics: using data-driven findings to predict and solve a problem before it is too late, but also assess methods and treatments faster, keep better track of inventory, involve patients more in their own health, and empower them with the tools to do so.
a. Helps to know the Patients Better: The most common use of health data analysis is to gain insights about patients. Who they are, what habits they cultivate, and what procedures they have done in the past are information that can assist in diagnosis and treatment and, therefore, relevant to the analysis of health data. Using the information your business has collected about patients over the years, it is possible to make an early diagnosis of ailments and explore more effective and customized treatment channels for each patient. This is one of the main benefits of data analysis in health.
b. Advanced Patient Care and Treatment: Health is not a sensitive issue just because incorrect decisions can be costly or less effective. It is also a discipline in which maximum efficiency – in the speed with which customer service is provided from the moment it arrives at the hospital until the discharge regimen – is capable of saving lives.
For example, with a good data analysis, a hospital can understand what the causes of delays are in its visits and why the screening process is less efficient than one would like. All of this will contribute to reducing the waiting time for patients and increasing the chances of a good, personalized, and agile service.
2. Types of Health Care Analytics:  Not every question can be answered by using the same analysis of the data. Through the use of different types of data analytics, we can answer many of the questions being asked in health care settings.
a. Descriptive Analytics: Descriptive analytics uses historical data to draw comparisons or discover patterns. This type of analysis is best for answering questions about what has already occurred. We can gain insight into the past with descriptive analytics.
b. Predictive Analytics: Predictive analytics uses current and historical data to make predictions about the future. The models created with this type of analytics are best for answering questions about what could happen next. We can gain insight into the future with predictive analytics.
c. Prescriptive Analytics: Prescriptive analytics will also make predictions about future outcomes. Machine learning is a big factor with this type of analytics. The information provided can help determine the best course of action. We can gain insight on what course of action should be taken to reach the most ideal outcome with prescriptive analytics.
B. Data Analysis and COVID-19
C. Application of Data Analysis in Healthcare
IV. FUTURE SCOPE
 The role of data analytics in the healthcare sector is going to become more vital with more demand from fast-growing technologies like AI and machine learning. And adopting data analytics will also become vital for healthcare organizations to operate with better efficiency and productiveness.
Moreover, the availability of healthcare training data for AI will also help wearable device makers to provide more accurate information to end-users. And using such wearable devices, patient monitoring also gives useful information to healthcare service providers to improve their services and help people enjoy advanced healthcare facilities.
Big data has a potential of revolutionizing healthcare from top to bottom. Healthcare organizations should bet big on big data to provide better patient outcomes, save on costs, and build efficiency across all departments. More crucially, big data will help clinicians and hospitals provide more targeted healthcare and see better results. For pharma companies, big data is a driving force that’ll help the design and build more innovative drugs and products. On the overall, healthcare stakeholders can rely on big data and predictive analytics to tackles major issues like readmission rates, high-risk patient care, staffing issues, dosage errors, and much more.
 Tian, S., Yang, W., Grange, J., Wang, P., Huang, W. and Ye, Z., 2019. Smart healthcare: making medical care more intelligent. Global Health Journal, 3(3), pp.62-65.
 Bello, R., 2022. Smart Healthcare System: A Primer. [online] Academia.edu. Available at:
Copyright © 2022 Jimit Kishor Mehta, Tanvi Umesh Mehta, Divya Jitendra Chaudhari. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.