Advancements in data analytics that assist research nurses

Nurses form the bulk of healthcare professionals in America, so they play a significant role in research and the collection of big data for the industry. According to one report by The Truth About Nursing, there are 10 nurses for every three doctors in the United States, and if the projections are right, then the numbers should continue to grow in the coming decade.

The role of nurses has also expanded over the years. Today, the average nurse has more responsibilities than ever before in the history of the profession. The average ICU patient spends around 86% of their time with a nurse, and it is estimated that those who visit the ER spend even more of their time with nurses.

This puts nurses in a unique position when it comes to research and big data. They can provide all sorts of details about patients that would otherwise be lost to healthcare professionals. A research nurse, whose duty it is to provide the sort of data that is needed for innovations in patient care, is in the unique position of providing the data that innovators need.

Although research nurses aren’t very common, they exist in many American healthcare facilities and play a crucial role in healthcare innovation. Without them, the industry would lack the sort of data and information that is needed to provide new and better treatments for patients, new technologies for diagnosis and improved patient care methods.

Becoming a research nurse

To become a research nurse, you need to have an advanced degree in nursing, such as an MSN or a DNP. To enroll in either of these, aspiring research nurses need to first earn a Bachelor of Science in Nursing (BSN).

It typically takes three to four years to earn a BSN, but those who want to do it faster can look for nursing schools that offer an accelerated BSN.

Potential candidates may ask: “Is an accelerated BSN worth it?” For those looking to transition into nursing research who have already earned a bachelor of science in another field, it provides an efficient path. Reputable institutions such as Wilkes University offer this accelerated program online, which covers topics such as physical assessment of patients of all ages, pharmacotherapeutics and decision making in nursing and nursing practice.

Nurses who complete this course can work as registered nurses, and are prepared for enrollment in specialized programs to further specialize in emergency care, critical care, anesthesia, midwifery and other areas depending on the kind of medicine they want to practice and the age of patients they prefer to deal with.

Accelerated nursing courses are primarily completed online, but students may be required to attend an on-campus residency, and they must also put in a set number of clinical hours. These are designed to prepare students to excel in a real clinical environment where they deal with actual patients and work with other healthcare professionals to deliver care in different parts of a hospital or clinic.

As a lot of the material covered in these courses is online, courses typically take a shorter time to complete. They are also more flexible, and students can access coursework when it is convenient for them.

As you plan your career as a clinical research nurse, it is important to choose the right courses, but you should also understand the role that innovation plays in data analytics, including how new advancements have affected what research nurses do, and what the future may hold for those in this field.

Before we look at how innovation is changing research nursing, it is worthwhile to take a moment to understand the role of big data and data analytics in nursing.

What is big data/data analytics in nursing?

The primary role of a research nurse is to collect data from patients. These nurses are on the frontlines of innovation because they work with research teams to bring us the latest in diagnosis, treatment and care of patients.

They also supervise clinical trials, which allow us to determine what drugs work, their side effects and even how well they work for different demographics.

Research nurses traditionally work in hospitals and clinics, but they can also be found within research labs and universities, where they analyze the data that is collected from patients and work together with researchers to develop evidence-based innovations that have a direct impact on the diagnosis, treatment and care of patients.

Research nurses may handle thousands and thousands of data points on any given day, hence the name big data. Big data can be defined as a massive amount of information that is collected on any given topic.

In healthcare, numerous data points are collected. This may include medical records, examination results, data that is collected by medical equipment such as EKGs and other types of scanners, data specific to certain illnesses or certain age groups, and feedback from specific demographics such as pregnant or new mothers.

The list is endless, and in big hospitals, the amount of data collected is also endless. As long as there are patients coming in every day, they are collecting data.

The role of the research nurse is to make sure that this data is properly collected and analyzed. Together with researchers, they can draw conclusions from this data to help patients and practitioners.

There are many examples of big data in use within the healthcare industry:

Patient tracking

Patient tracking was key during the COVID-19 pandemic. Not only did patient tracking help with keeping transmission numbers down, but it also helped nurses and doctors provide better care to those who were already ill. The data that was collected during this period will be further analyzed and the results will inform how we deal with future pandemics.

Diagnoses

By collecting data about different illnesses over time, researchers can develop better methods of detecting illness. This enables healthcare professionals to start treatment earlier with the goal of delivering better results for the patient.

Opioid abuse

This is a big problem within the American population. A recent CDC report shows that in 2022, nearly 110,000 people died of opioid overdoses, which far exceeded the 83,000 deaths that had been predicted. Many states have instituted steps to combat opioid dependency. One of the methods they use is to collect and analyze big data from hospitals and other healthcare facilities that treat opioid addicts. They can detect where addiction is highest and intervene to reduce the number of premature deaths. This data also allows healthcare providers to put plans in place to deal with the projected number of overdoses every year.

Development of new treatments

We have witnessed a lot of healthcare innovation in the last few years, and many of the new treatments we have today are developed in collaboration with clinical research nurses who collect and analyze data about the different conditions that patients suffer from. The data they provide researchers provides a basis for new treatments that combat pain and improve the quality of life, and they also prevent many early deaths.

Genome and gene mapping

This is a rapidly growing field that is vital in healthcare because it helps us predict chronic illnesses and whenever possible, take preventative steps.

More efficient hospital equipment

When nurses collect data from imaging machines within healthcare facilities, they approach researchers and medical manufacturers about creating more efficient and accurate machines.

These are not the only fields where big data is being used to help patients. It is also used to prevent fraud, track vulnerable populations and whenever possible, institute intervention measures. If an illness cannot be prevented from spreading, the data that has been collected can be used so we can be as prepared as possible to deal with it.

How is innovation in data analytics helping clinical research nurses?

Not very long ago, big hospitals held treasure troves of data. Unfortunately, this information was in paper files and there we lacked the proper tools to analyze it. 

This made the job of the clinical research nurse rather complex. In some cases, if they needed information on a certain topic, they started from scratch so that they could capture data afresh without having to dig into thousands of paper files.

Innovation has changed not just this, but several other aspects of research nursing.

Electronic patient records

For a long time, records were written by hand. When collecting both patient data and hospital information, practitioners had to fill in forms with the information and file them away for future reference.

This was tedious and often inaccurate, and retrieving patient files was often problematic.

Today’s research nurse can rely on centralized, automated records to do the job. Both patient and hospital data are much easier to collect and store, and retrieval is a breeze.

If a nurse wants to check how many patients presented with a certain condition during a given period, all they need to do is type in the parameters to bring up all the information on a screen. They can sort and analyze the data in a variety of ways, and they can also produce reports with the click of a mouse.

Centralized databases

The information that is collected within most healthcare facilities goes into a centralized location which research nurses can access to analyze.

A centralized way of keeping records not only increases efficiency, it also makes it easier for researchers to perform in-depth analyses on a wide range of topics.

Data security

This is one of the most significant ways in which innovation has changed the role of the research nurse; they can now store data and hospital records securely, and they don’t have to worry about it falling into the wrong hands.

While there are major hacks of hospital records from time to time, it is generally agreed that patient records are safer today than they have ever been.

Better analytical tools

Nurses in the 60s, 70s and even the 80s often had to analyze data by hand. If they wanted to make projections about a certain illness, for example, not only did they need to dig through thousands of patient records to extract the data they needed, but they often had to make complicated calculations by hand. Today, all they need to do is plug in a subset of data from the database and run it through preprogrammed formulas to perform the desired analysis.

Innovation has made collaboration easier

Research nurses don’t have to spend time traveling to labs to meet with researchers. Both groups have access to the healthcare database that they plan to analyze and can access it wherever they are to see what progress has been made.

If a researcher needs the research nurse to collect certain data, they can put the request in an email, including all the parameters, and the nurse can collect the information as patients come in. They can enter the data into the database for the researcher to access.

This has made the job much easier; you can certainly not compare the role of a research nurse today with one who worked 30 or 40 years ago. Today’s nurse has an easier time, they work more efficiently and they can collect and share data much more quickly.

Data is more accurate

Inaccurate data leads to inaccurate results and this can affect patient outcomes because researchers draw conclusions that aren’t properly informed by what is happening on the ground.

Innovations in big data collection and analysis support much more accurate collection of information, and the results are more reliable than they were in the past.

Reduced costs

Rather than shoot in the dark and hope to cure patients, the research that clinical nurses collect helps to provide more accurate treatments, reducing costs for both the patient and the hospital.

Conclusion

Innovations in big data have made the role of the clinical research nurse much easier; they can collect data more quickly, they have better procedures to analyze it and the conclusions that are drawn from this data are reliable.

Those entering this profession can expect things to only get better. As technologies improve, there will be further innovations in the field that will lead to better diagnosis, treatment and care for patients.