Are you a nurse interested in a career in nursing informatics but need help figuring out where to start to get the skills and experience?

Read on for advice, information, and tips on how to get started on a nursing informatics career in data analytics from our first guest blogger on The Nursing Informatics blog.

Joe Squire is an ICU/OR nurse who has successfully transitioned to a nursing informatics role via data analytics. He is currently the Director of Analytics & Quality for the Heart & Vascular Service Line at UPMC. Joe is passionate about data analytics and quality and penned this post to outline information and resources that nurses can use if they are interested in getting started in data analytics. On LinkedIn, he regularly shares educational and informative posts and offers advice on how to get started with data work.

As a nurse, you possess a unique skill set in high demand in the healthcare industry. With the advent of electronic health records (EHRs) and the tech boom of the 2010s, healthcare is now generating an overwhelming amount of data – the equivalent of over 28 years’ worth of YouTube videos in data every year. Unfortunately, over 90% of that data goes unused. But what if I told you that as a nurse, you have the power to unlock the potential of that 90% of unused data

Data analytics is not just for tech geeks – it’s for problem-solvers, communicators, and critical thinkers like you. Florence Nightingale, the founder of modern nursing, understood the power of data long before the advent of EHRs. With just a tiny amount of data, she was able to drive change and revolutionize the field of nursing and healthcare. She used data to turn old ideas upside down and improve patient care, and you can do the same.
Just imagine the possibilities – that 90% of unused data could hold the key to accelerating new drug discoveries, enhancing evidence-based practice, solving staffing issues, creating better budgeting, and so much more. Unfortunately, we don’t have enough individuals with the right skills to tap into that data. That’s where you come in.

You are already trained to examine data in the form of vital sign trends, interpret lab results, and make critical decisions based on that and other data points. You understand the importance of data and its role in providing the best care to patients, whether you realize it or not. And with just a bit of training in tech tools like Microsoft Excel, SQL, business intelligence (BI) tools (i.e. Tableau & PowerBI), and programming languages like Python or R, you can become a lean, mean, data machine ready to make a real impact in the healthcare industry and nursing.

But where do you start? The first step is understanding that data work is not just about tech skills. You need some to get in and get going, but successful data work relies on a combination of technical and soft skills. As a nurse, you already possess the soft skills necessary for data work – analytical, problem-solving, effective communication, and business knowledge. The key is to add technical skills to your toolbox.

The best place to start is with good old Microsoft Excel. There is plenty of excellent free training (see below for free training resources in all these technical areas). Still, the key to making it stick is using those new Excel skills consistently after learning them. So, I would encourage you to look around your current job and see if a project or problem could be solved using a spreadsheet to track data. If there is, great, take that on, dig into spreadsheets, and begin learning more about them as you work on that project.

Another option to get into a data-heavy role as a nurse is to investigate transitioning to a quality role. Many quality roles are spreadsheet-heavy, so you will have plenty of opportunities to work with them and expand your knowledge. If you want to go on from there, make your boss aware; they can likely help you get plugged in with database work. Databases open the wide world of data. This is where you will have to learn tech skills like SQL and BI tools to harness the power of databases and turn data into information. This information can be charts, interactive dashboards, combining two disparate but related sets of data to create new insights, telling a story with your data, etc.

Turning data into information is a highly crucial aspect of data work. Just like nursing, the highest form of turning data into information is both an art and science, where you ultimately combine the concepts of effective data visualization with storytelling techniques to convey insights and sway individuals to act on those insights.
Once you are comfortable with Excel and have a good understanding of databases, SQL, and BI tools, it could be time to take your skills to the next level by learning a programming language like Python or R. These languages aren’t necessary to make an impact in healthcare data analytics but are powerful tools for data analysis and visualization and will allow you to work with larger and more complex data sets. R and Python are versatile languages widely used in data science, machine learning, and data visualization.

Learning a programming language may seem daunting at first, but with the right resources and a bit of dedication, you will be able to master it in no time. Many online tutorials and courses are available for both Python and R, and many are free. Once you understand the basics well, you can start working on projects to apply your new skills.

One of the most exciting things about data analytics is the ability to make a real impact in the healthcare industry and nursing. All you need to start making this impact is a basic knowledge of Microsoft Excel and a project to work on. But with the right tech skills and passion, you can further harness the power of data and drive change in the nursing field. So don’t wait – start your journey into the world of data analytics today and join the ranks of Florence Nightingale in using data to drive change and improve patient care. The world is waiting for you!

FREE EXCEL LEARNING RESOURCES

• 𝗝𝗼𝗵𝗻 𝗠𝗶𝗰𝗵𝗮𝗹𝗼𝘂𝗱𝗶𝘀 – free Excel course takes that can take you from beginner to advanced levels https://www.myexcelonline.com/138-156.html
• 𝗔𝗹𝗲𝘅 𝗙𝗿𝗲𝗯𝗲𝗿𝗴 – Youtube playlist on essential Excel skills https://www.youtube.com/playlist?list=PLUaB-1hjhk8Hyd5NiPQ9CND82vNodlFF5
• 𝗗𝗮𝘃𝗲 𝗟𝗮𝗻𝗴𝗲𝗿 – YouTube videos that cover more advanced Excel topics like data cleaning, exploratory data analysis, and Power Query. He also uses Excel as a foundation to teach SQL and R https://youtube.com/@davidlanger8217

FREE BI TOOL LEARNING RESOURCES

POWERBI

• 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗟𝗲𝗮𝗿𝗻 𝗳𝗼𝗿 𝗣𝗼𝘄𝗲𝗿𝗕𝗜: https://powerbi.microsoft.com/en-us/learning/
• 𝗣𝗼𝘄𝗲𝗿𝗕𝗜 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: https://community.powerbi.com/
• 𝗚𝘂𝘆 𝗜𝗻 𝗔 𝗖𝘂𝗯𝗲: https://guyinacube.com/
• 𝗖𝘂𝗿𝗯𝗮𝗹: https://curbal.com/

TABLEAU

• 𝗧𝗮𝗯𝗹𝗲𝗮𝘂’𝘀 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: https://www.tableau.com/learn/training/20224
• 𝗧𝗮𝗯𝗹𝗲𝗮𝘂 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗙𝗼𝗿𝘂𝗺: https://www.community.tableau.com/s/
• 𝗦𝗤𝗟𝗕𝗲𝗹𝗹𝗲: https://youtube.com/@sqlbelle
• 𝗧𝗶𝗺 𝗡𝗴𝘄𝗲𝗻𝗮: https://youtube.com/@TableauTim

FREE PROGRAMMING LANGUAGE LEARNING RESOURCES

PYTHON

• 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗹𝗮𝘀𝘀: https://developers.google.com/edu/python/
• 𝗔𝗹𝗲𝘅 𝗧𝗵𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝘁 ‘’𝗣𝘆𝘁𝗵𝗼𝗻 𝗙𝗼𝗿 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿𝘀” 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁: https://youtube.com/playlist?list=PLUaB-1hjhk8GHKfndKjyDMHPg_HlQ4vpK
• 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗯𝗼𝗱𝘆: https://www.py4e.com/

R LANGUAGE

• 𝗗𝗮𝘁𝗮𝗤𝘂𝗲𝘀𝘁 “𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗶𝗻 𝗥: https://www.dataquest.io/course/intro-to-r-rewrite/
• 𝗖𝗼𝗱𝗲 𝗔𝗰𝗮𝗱𝗲𝗺𝘆 “𝗟𝗲𝗮𝗿𝗻 𝗥”: https://www.codecademy.com/learn/learn-r
• 𝗥𝗦𝘁𝘂𝗱𝗶𝗼 𝗘𝗱𝘂𝗰𝗮𝘁𝗶𝗼𝗻: https://education.rstudio.com/learn/beginner/
• 𝗗𝗮𝘁𝗮𝗖𝗮𝗺𝗽 “𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗥”: https://www.datacamp.com/courses/free-introduction-to-r

Hailing from Enon Valley, Pennsylvania, a quiet farming community, Joe Squire grew up living in the outdoors and wishing he had better internet than his dial-up modem provided. He somehow stumbled into nursing as a major in college. He graduated and worked in cardiac surgery, specializing in the ICU and CVOR. He eventually transitioned into data work, also somewhat on accident, and has grown extremely passionate about the promise of analytics in improving lives. Currently, he works as the Director of Analytics and Quality within the Heart & Vascular Service line at UPMC in Pittsburgh, Pennsylvania, where he lives with his loving wife and three children.