A Complete Guide to Healthcare Data Analytics

Healthcare is going through big changes. These changes are happening because of the amount of data and tools available now.

Healthcare data analytics can help make patients feel better, lower the costs of healthcare, and make the whole system work in a better way.

Want to know how healthcare data analytics can help your company? Well, this article on data analytics in healthcare tells you what it is, the different types, and what it does.‍

What is Healthcare Data Analytics?

Healthcare data analytics involves the use of data and analytical techniques to derive insights and improve decision-making in the healthcare sector.

Healthcare data analytics covers a wide range of activities, from basic reporting and descriptive statistics to advanced predictive modeling and machine learning.

Types of Healthcare Data Analytics

Healthcare data analysis can be split into several different types. Individuals do this by analyzing various types of healthcare data, including patient records, claims data, and clinical trial data.

In doing this, healthcare companies can identify patterns, trends, and anomalies that can occur.

Each type has its own specific goal and its own ways to look at the data:

• Descriptive Analytics: This is the most basic type of analysis. This type of healthcare analytics deals with showing what has happened in the past. Descriptive analytics uses things like reports, averages, and charts. In healthcare, it can be used to keep track of things like patient wait times, how often patients have to come back to the hospital. This also makes it easier to understand how many people have certain diseases.
  
• Diagnostic Analytics: In healthcare, this type of analytics tries to find out why certain things have happened. On the whole, diagnostic analytics deals with analyzing data to find the root causes of problems and the things that cause certain results. In healthcare, it can be used to study disease outbreaks, look at why treatment results change, and find out what makes patients happy or unhappy.
  
• Predictive Analytics: Predictive analytics finds patterns in past data to guess future things, like patient risk scores, how diseases might spread, and how well different treatments might work. In healthcare, however, it can be used to find patients who are at high risk, estimate how often patients will have to come back to the hospital, and make personal treatment plans.
  
• Prescriptive Analytics: Prescriptive Analytics in healthcare deals with saying what the best thing to do with specific results is. In order to do this, it uses predictions and plans to find the most effective actions. In healthcare, it can be used to help use resources in a better way, make personal treatments, and help doctors make plans.

How Data Analytics is Used in Healthcare

Data analysis has many different uses in healthcare, and more uses are being found as technology gets better. Some of the main uses are:

• Better Patient Care: Data analytics in healthcare can be used to make personal treatment plans, diagnose patient risk, and keep track of patient results. This leads to better patient care and better health results. For example, it can help find patients who are at high risk for serious infections, which allows doctors to act fast.
  
• Better Work: Data analytics in healthcare can help healthcare groups make their work better, lower costs, and improve how things work. This includes making better staff plans, managing supplies, lowering wait times, and improving patient flow. For example, it can help hospitals guess how many patients they will have and plan their staff in a better way. Predictive analytics can help process up to 60 claims per hour.
  
• Help for Doctors: Data analytics can give doctors real-time information and advice that is based on facts. This helps them make better plans and lowers the number of errors. Computer systems can analyze patient data and give alerts and advice for making plans and choosing medicine.
  
• Public Health: Data analytics in healthcare can be used to monitor disease outbreaks, track health trends in the public, and see if public health plans are working. This helps public health groups find and react to health threats in a better way, and improve the health of the public.
  
• Research: Data analysis can speed up medical research by finding possible drug goals, planning clinical trials, and looking at research data. This can lead to the development of new treatments and therapies in a faster and more efficient way.
  
• Fraud Detection: Analysis can find patterns of fraudulent activity in healthcare claims, which can lower costs and protect against abuse.
  
• Personal Medicine: By looking at a person's genetic makeup, lifestyle, and medical history, data analysis in the medical field can help make treatments that fit their specific needs, which leads to more effective and targeted therapies.

Benefits of Data Analytics in Healthcare

• Better Patient Results: When doctors can make more correct diagnoses, give treatments that fit each person, and step in before problems get out of hand, patients tend to have better outcomes. Data analysis lets them do these things, which means patients feel better and live better lives. This is because data analysis helps doctors see patterns and problems that might not be clear without it.
  
• Lower Healthcare Costs: Healthcare groups can save money by making their work run more smoothly, stopping patients from needing to stay in the hospital when they don't need to, and making sure care is given in the most effective way. Data analysis helps them see where money is being spent and where changes can be made, which can lead to big savings.
  
• Better Work: Data analysis can make processes simpler, lower the amount of paperwork and other tasks, and make the whole healthcare system work better. This means that doctors and nurses can spend more time focusing on patients, and less time on other tasks. The whole flow of the hospital or clinic works in a more smooth way.
  
• Better Plans: When doctors, leaders, and people who make rules have access to timely and correct information, they can make better choices. Data analysis gives them that information, so they can make plans that are based on facts and numbers, not just guesses.
  
• More Patient Involvement: Data analysis can help doctors and nurses talk to patients in a way that fits each person. By doing this, patients feel more involved in their own care, which can lead to them being happier with their care and following their treatment plans better.
  
• Better Disease Control: Analysis helps find diseases early, keep track of how they are changing, and manage long-term diseases. In doing this, doctors can keep diseases under control, which can stop problems from getting worse.
  
• Better Public Health: By finding trends and differences in health results, data analysis can help public health groups make plans to improve the health of whole groups of people. This helps to make sure that everyone has the chance to be healthy.

Challenges of Data Analytics in Healthcare

Despite its immense potential, healthcare data analytics faces several challenges:

• Data Spread Out: Often, healthcare data is kept in many different systems that do not talk to each other. Separated data can make it hard to consolidate information and for a full picture. Because of this, data analysis plans may not work as well as they should.
  
• Data Quality: Sometimes, healthcare data is not consistent, it is missing information, or it is wrong. This can make the results of data analysis unreliable. So, it is very important to make sure the data is reliable, to get correct and useful information.
  
• Privacy and Security: Healthcare data is very private and needs strong privacy and security measures to protect patient information. By doing this, you make sure to follow rand GDPR.
  
• Lack of Set Rules: There are not enough set rules for how healthcare data should be formatted and what terms should be used. And, this makes it hard to compare and look at data from different healthcare groups.
  
• People Not Wanting Change: Some healthcare workers may not want to use new technology and ways of working with data. Educating people about these data analytics and helping them understand why it can be useful becomes a priority.
  
• Not Enough Tools: Some healthcare groups may not have the tools they need, like computers, programs, and trained people, to use data analysis.
  
• Ethical Problems: Using data analysis in healthcare brings up ethical questions about patient privacy, if computer programs are biased, and whether data could be used in unethical ways. These problems need to be considered carefully.

Future Trends in Healthcare Data Analytics

The field of healthcare data analytics is constantly evolving, with several key trends shaping its future. In fact, big data analytics market in healthcare is expected to reach a valuation of USD 327.57 billion by 2034:

• AI and Machine Learning: AI and machine learning are being used more and more to look at complex healthcare data. They can help do tasks without people, and give more correct guesses and information. With this, computers can learn from huge data sets, and find patterns that people might miss. They can help doctors make quicker, better decisions.
  
• Big Data: The amount, types, and speed of healthcare data is growing. In the future, we’ll develop new ways to handle and accurately look at big data. We’ll have more data than ever before and programs that can handle it all and turn it into useful information.
  
• Cloud Computing: Cloud computing gives us a way to store and work on large amounts of healthcare data. This way is easy to change and does not cost too much. This type of computing lets more healthcare groups use data analysis. This means that hospitals don't need to buy and keep their own expensive systems. However, instead can use the cloud, which saves money and space.
  
• Internet of Things (IoT): Devices that people wear and other IoT tech are generating a lot of real-time health data. This data can be used to monitor patients remotely and care that fits each person. For example, watches that track heart rate can send alerts to doctors, so they can see if something is wrong.
  
• Data Sharing Between Systems: People are working to make it easier to share healthcare data between different systems and groups. This will let us do more complete and joined-up analysis. When hospitals can share data easily, doctors get a much more complete picture of a patient's health.
  
• Real-World Data Use (RWE): There will be more focus on using data from the real world, like patient records and claims data, to find out how well and how safe treatments are. This means that we don't just use data from trials. We also use data from real life to see how treatments work for real people.
  
• Blockchain: Blockchain tech may help make healthcare data more secure and easier to share. In doing so, you create a safe and clear way to share and handle data. This technology could help stop data from being changed or stolen.
  
• Gene-Based and Personal Medicine: Advances in gene study are letting us use data analysis to make treatments that fit each person's genes. This leads to more targeted and effective treatments. This means that doctors can make treatments that are based on a person's unique DNA.

How Entrans is Powering Smarter Healthcare with Data Analytics

As tech keeps moving forward, staying up to date on trends and the best ways of doing things. This can be analyzing patient claims data to reduce misdiagnosis rates and optimize treatment plans, which will be key to getting the most out of data analytics.

At Entrans, we help curate specialized data analytics solutions especially within the complex healthcare domain.

With expertise in Python, SQL, Power BI, Tableau, and over 40+ data ecosystems, we empower businesses and healthcare companies with real-time insight, critical for patient care and administrative efficiency. ‍

Want to harness the full potential of your healthcare data? Book a free 30-minute consultation call!

Healthcare Data Analytics FAQs

What is the role of data analytics in healthcare?

Data analytics in healthcare changes basic, raw data into information that people can use to provide better patient care, and to improve the whole healthcare system. By doing this, you help find patterns, trends, and things that are not normal in healthcare data, leading to more correct diagnoses. Also, treatments that are curated to each person, and to using resources in a more effective way

What are the types of Healthcare Data Analytics?

There are a few main types of healthcare data analysis. First, there is Descriptive Analysis, which deals with showing what has happened in the past. Then there is Diagnostic Analysis, which helps find the causes of events that have already happened. Next, there is Predictive Analysis, which looks at accurately predicting what will happen in the future, based on past trends. And finally, there is Prescriptive Analysis, which deals with saying what the best thing to do is in any given situation, based on all of the available data.

Which type of data is most commonly used in healthcare?

Healthcare uses a wide variety of data types, but some of the most commonly used include Electronic Health Records (EHRs), Claims Data, Patient-Generated Data, Genomic Data, and Imaging Data.