What is Healthcare Analytics? How can it help in Transforming Healthcare Services?

As we all know that the Healthcare Sector plays an important role in our lives. Healthcare is a vast area which comprises of hospitals, pharmaceuticals, health insurance, clinical trials, etc. Hereby using analytics, we can improve in many ways. Although we have a shortage of manpower as we can see the differences between rural and urban areas. We are also lacking the advancement of technology and analytics. To overcome it, healthcare analytics helps to a great extent.

You might be thinking “What is Healthcare Analytics?”, don’t worry I am coming there. Since we are moving from the traditional health system to the modern health system where usage of electronic health records (EHR), diagnostic results, Health Management Information System (HMIS), etc. are at large. These activities produce a large set of data which is known as Big Data which are complex and difficult to manage. To get insights about the healthcare data, we apply analytics on it for a better outlook. Healthcare Analytics is a set of applying statistical models and computational logic to unlock real value from healthcare data. In a simpler way, it is a part of Data Science.

Electronic health record of a patient

Classification of Healthcare Analytics

By now, you might have got a basic knowledge of healthcare analytics. Further moving on, healthcare analytics can be classified into  –

Here, it uses real-time data to make a decision and get insights about the treatment. It is also known as Clinical decision support (CDS). It improves the cost and quality of healthcare.

It deals with financial stuff and gives the outlook of financial data. It helps in monitoring cash flows, revenue and expenses in an organization. Finance plays an important role in increasing value in an organization and gives the current profit scenarios. It also helps to predict the future profit scenario.

Here, we can optimize operational performance by using data insights to manage costs, patient claims, etc. In simpler words, we can say that it deals to improve the existing process.

Now, I would like to discuss a few data science disciplines which are widely used in healthcare analytics to get better insights from a data.

The process of extracting useful information from large sets of raw data (complex data) is known as Data Mining. Here, we use descriptive and inferential statistics for analyzing the extracted data. It holds great potential in healthcare to improve services and reduce costs.

Predictive Analytics is a method of applying statistical techniques combined with applied mathematics and computational science to predict and improve decision making strategy in given scenarios. Here, we can predict the outcomes of certain disease and prefer handy for its circumstances.

Machine learning is the trending buzz around the globe for its potentiality. Arthur Samuel coined the term “Machine Learning” and defined as “Field of study that gives computers the ability to learn without being explicitly programmed.” It is helpful for disease identification, diagnosis, robotic surgery, drug discovery, etc.

Artificial Intelligence is a field of computer science where machine or computer programs perform tasks normally which requires human intelligence. It is a study of how the human brain thinks, learn, decide and work when it tries to solve the problem. It helps in healthcare system analysis, consultation, precision treatment and so forth.

Some of the tech giants like IBM, Microsoft, Google, etc. are working with healthcare providers in India to improve the field of healthcare analytics and doing exceptional on it.

NB - Many Healthcare service providers are using Minitab to improve their healthcare processes. Minitab is a Data Analytics Software, where we can Predict, Visualize, Analyze and Harness the power of our data. 

Now with our recent update, we can do Python Integration in Minitab. We can run Python scripts easily in Minitab and extend our analytical capabilities by collaborating with the Data Analyst, Data Engineer, Machine Learning Engineer and the Data Science Generalist.

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