7 Healthcare Data Analyst Skills for a Health Organization

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Healthcare data analysts are extremely rare in healthcare organizations!

And that’s because to analyze and use healthcare data effectively, you need to be skilled in statistics, programming, data mining, concepts in biomedical science, and most importantly, have a deep understanding of the patient journey.

When mastered, these skills can help you obtain valuable insights that can improve patient care, decrease turnaround time, fix inefficiencies in your healthcare organization, or just help you land that coveted healthcare data analyst job you’ve dreamed of!

In this article, let’s take a closer look at each healthcare data analyst skill and see how it can benefit your organization!

What Are The Best Healthcare Data Analyst Skills?

1. Analytical Skills

In a healthcare data analyst job, you’ll be analyzing healthcare data all the time – and that requires an analytical mindset.

An analytical mindset is an absolute must when it comes to being a healthcare data analyst. In fact, I would say that it’s the most important skill!

Essentially, an analytical mindset means always being intensely curious about the details, but at the same time, being able to step back and look at the big picture to pick out patterns in data!

This means being able to understand and interpret large amounts of data, discern different analyses for different data types, identify trends, and find patterns.

Analytical skills are important in healthcare because they can help you understand how patients interact with the healthcare system, what factors influence their health outcomes, and where there are opportunities to improve care.

For example, let’s say you’re analyzing data on patient wait times in your clinic. By identifying patterns in the data, you may be able to find ways to reduce wait times and improve the patient experience.

2. Statistical Skills

Statistical skills are another important healthcare data analyst skill. This involves the ability to use statistical methods to analyze data and make predictions.

Statistical skills are important in healthcare because they can help you understand the relationships between different variables, such as patient symptoms and disease progression. They can also be used to develop models that predict future health outcomes.

For example, let’s say you’re analyzing data on patients with heart disease. By understanding the relationships between different variables, you may be able to develop a model that predicts which patients are at risk for developing heart failure.

As biomedical science can have a great amount of big data (especially genomic data), knowing how statistical modeling works can really help make sense of all that data.

3. Programming Skills

Programming skills are another essential healthcare data analyst skill. This involves the ability to write code that can clean, manipulate, and analyze data.

Programming skills are important in healthcare because they can help you automate tasks, such as data cleaning and analysis. They can also be used to develop custom applications that can support decision-making in healthcare.

For example, let’s say you’re developing a custom application that predicts which patients are at risk for developing heart disease.

By writing code that cleans and analyzes data, you can develop an accurate prediction model.

Moreover, learning how to code effectively can drastically reduce human errors in manual point-and-click methods currently used in lots of legacy healthcare organizations such as hospitals and traditional clinics.

Programming also happens to be one of the more common technical skills that a data analyst job description would have. You’ll also need to learn how to use different data science IDEs for different use cases.

In my opinion, healthcare organizations may not have the best laptops for data science, so make sure your department has computers that are fast enough for processing bigger datasets.

As many managers in the healthcare industry do not know how to code, they will rely on you, the healthcare analyst, to provide the data analytics expertise (programming)!

Programming can extend into more complex fields such as machine learning and artificial intelligence. As such, it is increasingly a need to understand how biology relates to data science.

For this, taking an AI certifications course will help in upskilling your data skillset.

4. Database Management Skills

Database management skills are another healthcare data analyst skill. This involves the ability to design, build, and maintain databases.

Database management skills are important in healthcare because they can help you store and organize data effectively. They can also be used to develop custom applications that can support decision-making in healthcare.

For example, let’s say you’re developing a custom application that predicts which patients are at risk for developing heart disease.

By designing a database that stores and organizes data effectively, you can develop an accurate prediction model.

Moreover, learning how to manage databases effectively can drastically reduce the amount of time and effort required to maintain healthcare data.

As healthcare data analysts, you probably won’t have unlimited access to the backend enterprise data warehouse where all the electronic medical records are stored.

As such, learning how to store data well in databases that relate to one another in relational databases and unstructured databases is vital to handling larger amounts of information extracted from a health intelligence platform.

Health information management is very important!

5. Conceptual Knowledge in Biomedical Science

Biomedical science is the study of the structure and function of the human body. It encompasses a wide range of disciplines, such as anatomy, physiology, pharmacology, and pathology.

As a healthcare data analyst, it’s important to have some knowledge of biomedical science. It’s not all about pure data analytics skills.

This can help you understand the data you’re working with and make better decisions.

The domain experience of some sort can really put you at a much higher advantage over others!

For example, while I was at work, I found that it was easier for me to segregate the clinical data according to their symptoms during a COVID-19 dashboard I was maintaining at my previous job at Singapore General Hospital.

On another occasion, I was probing the cause of over-utilized CT machines called C-arms. These machines used X-rays to assist doctors in gaining knowledge of the spatial position of a particular wounded body part.

As a result of their good utility, they were overused which led to lots of delays in surgeries. I investigated the time in which each C-arm was ordered and used, and looked at the overlapped timings within a graph to see where things went wrong.

This is where my biology training came in handy:

With my knowledge of medical terms and background in biology, it was easy for me to quickly classify messy text data into classifications of surgeries that used a C-arm and those that didn’t.

Eventually, I settled on analyzing and monitoring the total count of C-arms being used at any point in time, and it turned out that it was used more on some days of the week!

I just presented this to management and they were better able to make decisions on the improvement of their C-arm scheduling services

In summary, healthcare data analyst skills are essential for anyone working with healthcare data.

6. Understanding of the Patient Journey

As mentioned before, data analytics skill isn’t all that’s required in a healthcare data analyst job.

The patient journey is the path that a patient takes from when they first seek healthcare services to when they are discharged.

This includes all the touchpoints that a patient has with the healthcare system, such as making an appointment, seeing a doctor, undergoing tests and procedures, and so on.

As a healthcare data analyst, it’s important to have a good understanding of the patient journey. This can help you identify bottlenecks and inefficiencies in the healthcare system.

For example, let’s say you’re trying to improve the appointment booking process for a healthcare organization.

By understanding the patient journey, you can see how patients typically book appointments and identify any pain points they may have.

You’ll be able to quickly pick out and monitor common KPIs:

• Resource utilization
• Patient wait time
• No-show rate
• Overall patient workload

These were the ones that I personally monitored during my time at Singapore General Hospital!

This can help you design a better appointment booking process that is more efficient and user-friendly.

7. Ability to Communicate Complex Concepts Easily

As a healthcare data analyst, you will often be required to communicate your findings to non-technical stakeholders.

This means that you need to be able to explain complex concepts in a way that is easy for them to understand.

Data analysis findings that no one understands are as good as no findings! Healthcare professionals in the healthcare industry know lots about their clinical data, but they don’t know much about the complexity of data types and statistics.

Always be kind and state the objectives of your data analysis as clearly as possible.

One of the best ways to do this is by using visualizations. By creating clear and concise visualizations, you can effectively communicate your findings to anyone, regardless of their technical expertise.

For example, let’s say you want to show the relationship between healthcare spending and life expectancy.

You could create a simple line chart that shows how healthcare spending has increased over the years, while life expectancy has also increased. This would be an effective way to communicate the relationship between these two variables.

Another tip is to use plain language when you present data findings. Avoid using technical jargon or acronyms that non-technical people may not be familiar with.

For visualizing concepts clearly, I’d recommend using the Tableau software, ggplot graphs on R, and plotly and matplotlib libraries on Python.

Final Thoughts

By following these tips, you can effectively communicate complex concepts to anyone. This is an essential skill for healthcare data analysts!

These are just some examples of healthcare data analyst skills that are essential for anyone working with healthcare data 🙂

If you’re interested in becoming a healthcare data analyst, I recommend taking some online courses or attending a healthcare data analytics boot camp.

These will give you the practical skills and knowledge you need to be successful in this field.

I hope this has been helpful for you if you want to become a healthcare data analyst!

Please do reach out to me if you’d like to learn more about how healthcare data analysts work! 🙂