Introduction

According to a survey done by Emergen Research the global market size of healthcare analytics in 2021 was 21.10 Billion USD. This number is expected to reach 158.90 Billion USD in 2030.  

Data analytics is changing the way businesses were processed. The same technique is making its way into the healthcare sector and has massive impacts. Whether it’s clinical methods, patient experience, innovation or research, every aspect of the healthcare system is seeing a huge positive change due to the accessibility of data.  

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What does data analytics in healthcare mean?

Here’s a bookish kind of definition. Data analytics in healthcare means collecting, analyzing and presenting medical data in an understandable form to get valuable results and conclusions.  

There’s nothing new in healthcare analytics except the fact that the data here is related to the healthcare industry. The medical sector can pretty easily collect a huge amount of data which can potentially boost research and innovation. The problem is that this data needs to be sorted out in a way that’s mass understood and here data experts play the role. 

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The diversity of healthcare data

Healthcare data is not only in huge amounts but it’s pretty diverse as well. Unlike business where anyone can easily gain the skills to analyze the data, in healthcare you need experts. It’s because categorizing and analyzing such diverse data is tricky. 

The medical data includes,  

• Medical histories of patients  
• Bio images like X-rays and MRIs  
• Behavioural data  
• Bioinformatics and statistical data 

• Genomic records   
• Sensory data like brain waves and ECG.  

As a healthcare analyst, you’ll have to deal with all kinds of data like numbers, images, electric signals, and qualitative data as well. 

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Types of healthcare data analytics

We divide all the medical analytics into 4 main types. This categorization is based on the results that the data gives us. 

1. Descriptive data analytics:  

This type of analysis gives you describing information, it tells you what happened. Descriptive analytics help you answer questions like,  

• How many patients died of a heart attack last year?  
• What age group of people are the most to visit a psychiatrist?  
• With what percentage a particular disease is spreading?  

The data includes historical trends and patterns and the results are mostly quantitative. 

2. Diagnostic data analytics:  

Diagnostic data analytics is all about answering the question why did this happen? It tells you the reason behind something. For this, you have to conduct surveys and talk to targeted people. Let’s take some example questions that diagnostic analytics can help you answer.  

• What’s the reason for increasing heart attacks?  
• Why young people are the most to visit a psychiatrist?  
• Why this particular disease is spreading? 

If you notice you’re actually finding the reason behind a descriptive analysis. Here the results will be qualitative.  

3. Predictive data analytics:  

In February 2022 a worldwide survey was conducted. 72% of the healthcare leaders said that predictive analytics will have a positive impact on patient health in clinical settings and will also improve the patient experience. 

Predictive analysis tells you what’s going to happen next. It can help you predict upcoming pandemics, a surge of a certain disease or a shortage of machinery or medicines in the hospital. Following are some questions that predictive analysis can help you answer,  

• How many patients can die of a heart attack in the coming year?  
• How much the disease is going to spread in the upcoming months?  

Let’s take 2 more examples.  

1. The data shows a sudden increase in lung cancer patients coming in. You can predict the shortage of ventilators and beds in the hospital.  

2. The constant increase in pollution can help you predict an increase in lung and eye infections.   

4. Prescriptive data analytics:  

Prescriptive analytics answers the question of what should you do to prevent the unwanted scenario. It suggests new methods, techniques and solutions to advance the healthcare system and to be prepared for upcoming challenges. Let’s see the type of questions you can answer with predictive analytics.  

• What should we do to prevent a rise in heart attacks?  
• What measures should we take to avoid the spreading of disease? 

An example of prescriptive analytics can be artificial organs. Considering the low number of donors compared to the receivers we can prescribe using artificial organs. 

How do data analytics help in healthcare? 

Analyzing the healthcare data and making it accessible has some crazy benefits. Here are the 8 main applications and benefits of healthcare data analytics.  

1. Boosts research:  

Medical research and innovation are the basis of all medical advancements. Data is helping in understanding diseases better, finding their cure and advancing the existing procedures by aiding us in research. 

Clinical data can improve research in 2 ways.  

• It can help in finding new causes of diseases by linking the risk factors.  
• It can increase the efficiency of diagnosis and treatment by finding advanced methods.  

Example: Data told us that cancer which is a fatal disease is substantially increasing. This opened the doors of cancer research and scientists worldwide are trying to find its cure.  

2. Help in making better predictions:  

Data analytics can help us make better predictions about diseases, medication and other medical stuff. Once we’ve predicted only then we can start taking measures to avoid the bad scenario.  

In the healthcare sector, a prediction and its proper measures can save lives.  

Example: A practical example of this is the COVID-19 pandemic. With the help of data, analysts predicted an exponential increase in the disease. To counter that we were given SOPs to follow and got in lockdown as well.  

3. Incorporation of AI and machine learning: 

Using AI-based robots and machines is not possible without data. AI and machine learning need to analyze a lot of data before they can start giving you results.  

Example: An AI-based x-ray reader can read the X-rays and give the diagnosis without any human intervention. However, for this, it’ll need to see thousands of X-ray images to learn. 

Another example can be a device that can monitor a patient’s vitals and send an alert to the doctor as soon as they’re unstable 

4. Early diagnosis and disease prevention:  

Data analytics can help in the early diagnosis of diseases and in their prevention. We can find new risk factors with the help of data and also know how many people are at high risk of getting a particular disease.  

Once we know that we can incorporate lifestyle changes to prevent the disease.  

5. Data accessibility will increase:  

Research requires data. A lot of time many companies also require data for developing new medical technologies or drugs. However, the tricky part is this data isn’t well arranged. It’ll take effort and time to understand that, and this creates a barrier. 

Once we start to properly analyze healthcare data and present it in a form that’s understandable it will be more accessible. Researchers, students and companies will be able to understand that more easily and get to work on improving our healthcare system.  

6. Better hospital management: 

Keeping a record of hospital staff and machinery can help in management. You’ll know exactly where you need more staff and which wards and departments need more machinery or devices. This way the hospital can manage its money and resources better to improve the patient experience. 

7. Better supply chain management:  

During the outbreak of diseases, the medicine and machinery supply chain gets disturbed. The demand exceeds the production limit. This affects the healthcare system and ultimately a lot of lives.  

Predictive analytics can help us identify those outbreaks and the demand for medical products. Hospitals can inform the manufacturers in advance so they can start preparing in advance. 

8. Decrease in self-harm and suicide:  

Predictive analytics can help in preventing suicide and self-harm. By looking at the behavioural patterns of self-harm cases and comparing them to our patient we can estimate how much is he at risk of self-harm.  

The sea of opportunity

We’ve discussed here some main benefits and uses of data analytics in healthcare. However, that’s not all. There are many opportunities in this field. As you’ll look more into it, you’ll see that people are getting many different benefits from health data.  

How you use data and what problem you’re trying to solve can give a completely different benefit from what we’ve mentioned.  

The future is data, the healthcare industry will depend more on analytics in future. Covid-19 is a practical example of that. During the pandemic, it was data that helped us make decisions and track everything.  

In a nutshell

This was the importance of data analytics in healthcare. It’s opening the doors of new research and the development of new technologies. Patient experience will be increased, and we’ll be able to make better predictions and can take preventive measures.  

However, medical data is complicated. It’ll require an expert to sort that out and analyze it.  

Also, medical analytics are divided into 4 categories.  

1. Discovery analytics 

2. Diagnostic analytics 

3. Predictive analytics 

4. Prescriptive analytics 

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