Data analysis in healthcare can be highly complex and confusing to the uninitiated. Many tools and techniques vary by field and region, meaning that one set of data analysis fundamentals might only work for some.
Healthcare data has been growing exponentially, causing privacy and safety issues but providing critical information to improve healthcare. This data plays a vital role in healthcare, as it allows us to understand our patients better and improve the quality of the care we provide.
Data analysis in healthcare can lead to new ways of looking at treatments, preventing medical mistakes, and saving lives. Hence, you must understand the basics of this topic so you can harness the power of data analysis and utilize your data effectively.
An Overview of Healthcare Analytics
Healthcare analytics uses information and data to identify patterns and trends related to health. Healthcare providers, such as doctors, hospitals, and insurance companies, are all using analytics to find new ways to improve healthcare quality and cost efficiency.
For organizations to make informed decisions about how they deliver care or offer coverage, they need access to a large amount of clinical data. For instance, a physician needs to know which patients are more likely to develop certain diseases, such as diabetes or cancer.
Similarly, an insurer needs to know which customers have chronic conditions that might be expensive to treat. Data analysts analyze this data by reviewing medical records and demographic information like age, gender, and location.
When done incorrectly or poorly, data analysis in the healthcare industry can lead to tremendous amounts of wasted time and resources. However, data analysis can save healthcare providers vast amounts of money and dramatically improve your operation’s efficiency.
Before starting your next project, you must know healthcare data analysis fundamentals.
Familiarize Yourself with the Data
Before analyzing your data, it’s essential to be familiar with it. Data may occur in many formats and types, such as word processing files, PDFs, spreadsheets, or scanned paper documents.
Sometimes the data will come from multiple sources in the same format, while other times it might come in different forms that need converting into a uniform type. Regardless of the type of file the data is stored in, before you can use it for analysis, ensure you know how each column is formatted.
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Understand How the Data Was Collected
The data, on its own, is meaningless. It’s what you do with the data that matters. Understanding how analysts collected the data can help you make more informed decisions about how to use it.
For example, when comparing test results between two groups, are they all exposed to the same environment? Were they exposed to different environments? What factors might have affected their health status?
Understanding these scenarios will help you better interpret and identify patterns in your data. And with better interpretation come more accurate conclusions.
Choose the Right Tool for the Job
Before your analysis, ensure you know which tool will work best for the job. Some tools, like Excel or SAS, are better for visualizing data and looking for patterns. Others, like Python or R, are better for deep statistical analysis.
The type of tool you choose will depend highly on what you want to do with the data. If you’re looking for fundamental insights into a given data set, it might not matter as much which one you use.
However, if you want to conduct more complex analyses, such as inferential statistics, then having the right tool can save time and ensure accuracy.
In the healthcare industry, for example, there is often an abundance of different data types that need to be analyzed simultaneously. For this purpose, it is advisable to find tools for handling categorical and continuous variables.
Understand What the Data Means
Data analysis is a complex process that can take time to understand. When looking at data, the first thing to do is understand the data and what it means.
If you need to know the values, how you collected them, or what they signify, then understanding any patterns within them will take a lot of work. Taking a step back before diving in can help avoid misinterpreting things that may not be relevant to your research.
For example, if there’s an increase in hospital admissions for asthma during pollen season, this may not necessarily mean an increase in asthma rates overall. It could be related to increased exposure to allergens while walking near flowering plants.
The critical point here is to take the time upfront to clarify what data you have, why it was collected, and its implications. With these basics in mind, identifying meaningful trends becomes much more manageable.
Clinical Data Sets are Complex
Data analysis can be done with various methods, including simple descriptive statistics and quantitative statistical analyses such as regression modeling. Each method has its uses and limitations, so it is essential to know what best suits your specific need.
Data Analysis can cover a wide range of tasks, from analyzing clinical data sets to calculating counts or averages within those sets. Of course, these calculations become more complex when using more than one variable simultaneously.
For instance, multivariable analyses require some combination of linear regression and correlation coefficients to understand how two variables are related over time.
The healthcare industry is becoming more focused on data-driven decision-making to use limited resources better. That said, it’s still up to you to ensure you have the skills and knowledge necessary to analyze the data correctly.
Data Analysis Requires Collaboration
Data analysis is a team sport, which means that it requires collaboration. Teams need to work together to share insights and expertise across departments.
This is especially true for data analysts, who need to take input from the data scientist and translate it into something actionable for the organization. It’s essential to build good habits when starting because they’ll become harder to change.
In a healthcare setup, this might mean building a cross-functional analytics team with people from various fields, such as finance, data science, or marketing.
This team should have clear, shared goals around what data to analyze, how to use the results, and what key performance indicators to track.
There Is No Such Thing as Too Much Data
Data is the foundation for any successful business, and healthcare is no different. With the recent waves of new technologies, data analysis has become more accessible and prevalent.
When leveraging data in the healthcare industry, there is no such thing as too much because the volume of data available today is staggering. Finding what you’re looking for can be challenging without an analytical approach.
A solid analytical framework will allow you to identify critical trends and insights from your data to use them to make better decisions about your future strategy. The first principle of practical data analysis is filtering.
It would be best to filter out unnecessary information like duplicate records or extraneous fields to get started. Filtering helps organize your dataset into smaller subsets, making analyzing easier and more accurate.
After filtering out useless information, you can organize the remaining records by date or some other categorical variable like region or gender so that they are easier to compare.
Ethical Considerations are Paramount
In the healthcare industry, data is used to make informed decisions. For example, you could use data to track patient health and evaluate outcomes.
However, it can also be used for marketing purposes or to discriminate against people with specific characteristics or disorders. Ethical considerations are paramount when working with these types of sensitive information.
You must carefully consider how data is collected, analyzed, stored, and shared to protect privacy rights while benefiting society. You must be aware of any relevant privacy laws.
Laws may vary by state, so it’s important to know what type of data your organization collects and where your organization falls under the law. If you need help determining what is appropriate, consult an expert.
Make Data Available in Real-time
The data analysis process should start with finding the data. This is done by either getting it from a third party or collecting it yourself. Once the data has been found and made available, the next step is to clean up and structure the data so that you can analyze it quickly.
While cleaning up the data, make sure not to lose any valuable information and ensure that all aspects are represented. The final step in this process is analyzing the data and finding answers.
For example, if you want to know how long patients stay at your hospital before they leave, you would use the date and time fields in your hospital’s medical records system to figure out when each patient was discharged.
That way, you can compare how much time people spend in the hospital for different diagnoses or compare how much time people spend in the hospital for various procedures.
The Data Analyst Role is Multidisciplinary
You must work with numbers, text, and images as a data analyst. In addition, data analysts are usually multidisciplinary, meaning they have skills and knowledge across a range of subjects rather than being an expert in one specific area.
Regarding healthcare, there are many opportunities for data analysis, including clinical trials, treatment effectiveness research, public health monitoring, and more.
You might be a statistician who understands how to use statistics to answer questions or a computer scientist who understands how to store information on computer systems.
Data analysis is not just about understanding the type of question but also about the tools that need to be used and how these will help get answers.
4 Levels of Analytics You Need for Better Decision Making
Business decisions are the lifeblood of every organization, but they’re also tricky because you can’t predict the future or fully understand what your customers and partners will do next.
For help choosing the right analytics for your business, check out this helpful list that breaks down four levels of analytics you need for better decision-making.
- Descriptive Analytics
Descriptive analytics tells you what happened in the past, when it happened, and who was involved. It also gives you insight into how a customer interacted with your company. The healthcare industry can use this information to determine patients’ health trends.
- Diagnostic Analytics
Diagnostic analytics refers to analyses primarily used to detect problems or potential problems. You can use this type of analytics in healthcare to determine which drugs and surgeries will work best with a patient’s unique physical characteristics and preferences.
- Predictive Analytics
Predictive analytics is the science of making predictions. Businesses have been collecting data for decades, and this type of analysis allows companies to make more informed decisions in the future by applying statistics and probability theory. It’s a process that generates predictions about what might happen in the future based on what has happened in the past.
- Prescriptive Analytics
Prescriptive analytics uses data mining and predictive algorithms to determine the optimal decision. These are used for all business decisions, including product design, manufacturing, inventory management, pricing, marketing campaigns, and more. In the healthcare industry, you can use prescriptive analytics to determine which type of medical intervention will be most effective given a set of symptoms.
Final Thoughts
Data analysis is a never-ending process that requires constant effort and dedication to gain maximum benefits. The fundamentals will prove helpful if you are keen on using data analytics to enhance your business processes and achieve success in the ever-changing healthcare environment.
It all starts with defining the goals for your business and what success means for you as an organization. The healthcare industry would greatly benefit from implementing these techniques because they would increase their operations’ efficiency and accuracy while improving patient outcomes.
Healthcare organizations can start making strides in this field by understanding these fundamental principles and applying them to their strategy.
