If we talk about healthcare industry we can not ignore the fact that, January 30,2020 was the day when WHO (World Health Organization) declared officially the spread of COVID-19 pandemic as a matter of concern and recommended all the governments of the world to raise the level of health emergencies. The basic symptoms of covid were respiratory illness and if a person is tested positive for corona virus, every individual came into contact with that person was advised to go for self-quarantine for minimum 2 weeks so that the infection chain can be broken, and the disease does not spread further. The same scenario was experienced by India also. Lets have a glance at India’s performance in fighting with corona.
Now what we need to understand that how data analytics helped India to overcome this situation.
Many countries tried to develop a technique through which they can trace the patients like South Korean government started maintaining database of known patients along with their personal data like age, gender, occupation and so on. Israel government started tracking mobile phones of their citizen.
On the other hand, “India developed an app, called Arogya Setu which is designed in such a way that it informs the user whenever the user comes in contact with the infected person with the help of bluetooth and GPS location services. This data acquired from the app is not disclosed publicly and is only used by the government for tracing, tracking and management of Covid-19. This data is further stored and compared by Indian Council of Medical Research(ICMR) database“
Data Analysis Techniques:
The primary objective of using Arogya Setu app was to find major hotspots of covid 19 patients and take appropriate actions in order to stop further spread of infection. In order to locate those hotspots the app will provide the longitude and latitude coordinates of those covid-19 patients and at risk people, these clusters(hotspots) are created with the help of Artificial Intelligence and Machine Learning techniques.
Apart from Arogya Setu, data analytics helps Healthcare Industry in other applications too.
Applications of Data Analytics in Healthcare Industry:
- Predictive Analytics for Disease Outbreaks: Data science helps the organizations to analyze large datasets to identify patterns and predict disease outbreaks. Predictive analytics helps in early detection, allowing authorities to take preventive measures.
- Clinical Decision Support Systems: These support systems help the doctors to analyze patient’s data in order to make better and well-informed decisions about diagnosis and treatment. This helps in achieving more accurate and personalized patient care.
- Fraud Detection and Prevention: A country as large as India, healthcare fraud is a significant issue. Data science can help identify fraudulent activities, such as false insurance claims or overbilling. Advanced analytics can detect patterns that may indicate fraudulent behavior, saving resources and ensuring that healthcare funds are used efficiently.
- Personalized Medicine: India has a diverse population with varying genetic backgrounds. Data science enables the analysis of genetic and molecular data to develop personalized treatment plans. Healthcare providers can prescribe medications and treatments that are more likely to be effective for a specific patient, improving overall healthcare outcomes.
- Patient Engagement and Monitoring: As per the trend of using wearables and health monitoring devices, there’s a wealth of data that can be leveraged for patient care. Data science can help in monitoring patient’s health in real-time, providing insights into lifestyle factors, medication adherence, and early signs of health issues.
How to become a Data Analyst in Healthcare Industry
In order to serve the patients effectively and efficiently, the companies collect data of its patients on large scale but only collecting the data doesn’t solve the problem. In order to analyze data, a company needs resources of some certain skill sets. So now we will discuss exactly what skillsets are required to in order to become a skillful data analyst in any healthcare company.
- A Programming Language: Like any other IT profile, data analytics profile also requires knowledge of a programming language. Though languages like Java, Scala, R exist in the market but Python rules the market with 80% coverage all alone. The reason of its popularity is the availability of libraries and easiness to learn. Python has 1 lakhs and more libraries to perform tasks easily and efficiently but we do not need to learn all of them. Python has libraries depending upon the requirements and fields.
- NUMPY & PANDAS: Learning python alone is not sufficient enough to analyze data. In order to analyze you need to learn powerful libraries like numpy and pandas. Numpy is short for numerical python. Numpy is used for complex and big calculations. Numpy is so much powerful that even NASA uses this library for their scientific calculations. On the other hand, Pandas is a powerful library which helps you to manipulate a data as per your requirements and analyzing it.
- Matplotlib & SEABORN: After you have done your analysis on a dataset and achieved your output, now is the time to present it to your higher management. Any result in numbers is tough to understand for everyone, in order to solve this problem you use graphs and charts to present our analysis and these graphs and charts can be prepared through python libraries like matplotlib and seaborn.
- SQL: Structured Query Language(SQL) is another programming language which helps in analyzing the healthcare data. Before python and its libraries, companies used this language to perform analysis. But nowadays, SQL is not sufficient enough to handle huge amount of data and also it works on only structured data whereas Pandas is powerful enough to read any (structured and unstructured) data. Many organizations use SQL to handle their collected this is also one of the reasons that companies prefer their analysts to have command in SQL.
- Pyspark + Databricks: Whatever you have learned above is sufficient to analyze healthcare data but sometimes the data can be so huge that you can not even load in your laptop because of its configurations. We are talking about data in petabytes and zettabytes here. Healthcare is one of the industries which generate this kind of data. This kind of data to be analyzed is called Big Data So in order to handle and analyze this kind of huge data we need Pyspark which is a technology handled by Apache. Earlier this big data problem was handled by Hadoop, an older technology of Apache. Pyspark is a modern and advance technology then Hadoop.
Pyspark helps us to handle huge.amount of data in clustering format. Pyspark helps us to create a cluster of around 6000 computers to connect with a single master computer and the rest 6000 computers are called slave computers. Now in order to handle and analyze this much kind of data we need to create a cluster and give instructions to this clustered structure of data we need a platform that is Databricks. Databricks helps its users to create this type of clusters within few minutes and also provides a platform on which you can give your instructions of analysis.
According to Healthcare Weekly, the global big data market in the healthcare industry is expected to reach $34.3 billion by 2022, growing at a CAGR of 22.1%
6. PowerBI: A product of Microsoft that helps us to create interactive healthcare dashboards or in simple words we can say a dashboard which shows real time data activities at one place. Matplotlib and Seaborn are also helpful libraries which create graphs and charts to understand the results of the analysis but they are not real time. Suppose, you created a graph at any time in a day but after few hours that report will be old as new data must have been generated and the graphs needs to be changed.
This is the reason companies use PowerBI’s dashboards as it helps the upper hierarchy of any organization to monitor their business from any place and at any point of time with real data.
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